The genome sequence of the highly acetic acid-tolerant zygosaccharomyces bailii-derived interspecies hybrid strain ISA1307, isolated from a sparkling wine plant

In this work, it is described the sequencing and annotation of the genome of the yeast strain ISA1307, isolated from a sparkling wine continuous production plant. This strain, formerly considered of the Zygosaccharomyces bailii species, has been used to study Z. bailii physiology, in particular, its extreme tolerance to acetic acid stress at low pH. The analysis of the genome sequence described in this work indicates that strain ISA1307 is an interspecies hybrid between Z. bailii and a closely related species. The genome sequence of ISA1307 is distributed through 154 scaffolds and has a size of around 21.2 Mb, corresponding to 96% of the genome size estimated by flow cytometry. Annotation of ISA1307 genome includes 4385 duplicated genes (~90% of the total number of predicted genes) and 1155 predicted single-copy genes. The functional categories including a higher number of genes are 'Metabolism and generation of energy', 'Protein folding, modification and targeting' and 'Biogenesis of cellular components'. The knowledge of the genome sequence of the ISA1307 strain is expected to contribute to accelerate systems-level understanding of stress resistance mechanisms in Z. bailii and to inspire and guide novel biotechnological applications of this yeast species/strain in fermentation processes, given its high resilience to acidic stress. The availability of the ISA1307 genome sequence also paves the way to a better understanding of the genetic mechanisms underlying the generation and selection of more robust hybrid yeast strains in the stressful environment of wine fermentations.This research was supported by FCT and FEDER through POFC-COMPETE [contracts PEst-OE/EQB/ LA0023/2011_ research line: Systems and Synthetic Biology PTDC/AGR-ALI/102608/2008, PEst-C/BIA/ UI4050/2011, and post-doctoral grant to M.P. (SFRH/BPD/73306/2010) and PhD grants to J.F.G. (SFRH/ BD/80065/2011) and F.C.R. (SFRH/BD/82226/2011)]. U.G. acknowledges the Austrian Science Fund (FWF, special research project F3705)

OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics

Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their relatively steep learning curve and the different methodologies employed by DL compared to traditional approaches, along with the high complexity of DL models, which often leads to the need of employing specialized hardware accelerators, further increase the effort and cost needed to employ DL models in robotics. Also, most of the existing DL methods follow a static inference paradigm, as inherited by the traditional computer vision pipelines, ignoring active perception, which can be employed to actively interact with the environment in order to increase perception accuracy. In this paper, we present the Open Deep Learning Toolkit for Robotics (OpenDR). OpenDR aims at developing an open, non-proprietary, efficient, and modular toolkit that can be easily used by robotics companies and research institutions to efficiently develop and deploy AI and cognition technologies to robotics applications, providing a solid step towards addressing the aforementioned challenges. We also detail the design choices, along with an abstract interface that was created to overcome these challenges. This interface can describe various robotic tasks, spanning beyond traditional DL cognition and inference, as known by existing frameworks, incorporating openness, homogeneity and robotics-oriented perception e.g., through active perception, as its core design principles.acceptedVersionPeer reviewe