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Funder: FP7 People: Marie-Curie Actions; doi: http://dx.doi.org/10.13039/100011264; Grant(s): 238701, 238701, An affinity fingerprint is the vector consisting of compound’s affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based affinity fingerprint, components of which are predicted by Random Forest regression models trained on bioactivity data from the ChEMBL database. Both real-valued (rv-QAFFP) and binary (b-QAFFP) versions of the QAFFP fingerprint were implemented and their performance in similarity searching, biological activity classification and scaffold hopping was assessed and compared to that of the 1024 bits long Morgan2 fingerprint (the RDKit implementation of the ECFP4 fingerprint). In both similarity searching and biological activity classification, the QAFFP fingerprint yields retrieval rates, measured by AUC (~ 0.65 and ~ 0.70 for similarity searching depending on data sets, and ~ 0.85 for classification) and EF5 (~ 4.67 and ~ 5.82 for similarity searching depending on data sets, and ~ 2.10 for classification), comparable to that of the Morgan2 fingerprint (similarity searching AUC of ~ 0.57 and ~ 0.66, and EF5 of ~ 4.09 and ~ 6.41, depending on data sets, classification AUC of ~ 0.87, and EF5 of ~ 2.16). However, the QAFFP fingerprint outperforms the Morgan2 fingerprint in scaffold hopping as it is able to retrieve 1146 out of existing 1749 scaffolds, while the Morgan2 fingerprint reveals only 864 scaffolds.

Video-based analysis is one of the most important tools of animal behavior and animal welfare scientists. While automatic analysis systems exist for many species, this problem has not yet been adequately addressed for one of the most studied species in animal science—dogs. In this paper we describe a system developed for analyzing sleeping patterns of kenneled dogs, which may serve as indicator of their welfare. The system combines convolutional neural networks with classical data processing methods, and works with very low quality video from cameras installed in dogs shelters.

PHRESCO is an EU-H2020 funded project that was running for four years and will be ending in September 2019. PHRESCO focused on the development of efficient cognitive computing into a specific silicon-based technology by co-designing a new reservoir computing chip, including innovative electronic and photonic components that will enable major breakthrough in the field. So far, a first-generation reservoir with 18 nodes and integrated readout was designed, fabricated, characterized and a training method has been developed. Additionally, large efforts of the consortium were dedicated to the design of the second-generation chip consisting of larger networks (60 nodes), with an on-chip readout and novel training approaches. This short abstract provides key information on the status of the work achieved and discuss further the potential exploitation routes and the key barriers that still need to be removed to bring the technology to a higher maturity level. A part of the exit strategy of PHRESCO is to identify potential future cooperation with interested stakeholders who are willing to co-develop the PHRESCO technology together with the PHRESCO partners for bringing it to an exploitable or marketable system. This abstract lays down the foundations for potential exploitation activities with interested stakeholders.