The Orion constellation as an installation - An innovative three dimensional teaching and learning environment

Visualising the three dimensional distribution of stars within a constellation is highly challenging for both students and educators, but when carried out in an interactive collaborative way it can create an ideal environment to explore common misconceptions about size and scale within astronomy. We present how the common table top activities based upon the Orion constellation miss out on this opportunity. Transformed into a walk-through Orion installation that includes the position of our Solar system, it allows the students to fully immerse themselves within the model and experience parallax. It enables participants to explore within the installation many other aspects of astronomy relating to sky culture, stellar evolution, and stellar timescales establishing an innovative learning and teaching environment.Comment: 2 pages, submitted to The Physics Teacher - Colum

Lactate-guided resuscitation saves lives: we are not sure

SCOPUS: ed.jinfo:eu-repo/semantics/publishe

A Population Genetics-Phylogenetics Approach to Inferring Natural Selection in Coding Sequences

Through an analysis of polymorphism within and divergence between species, we can hope to learn about the distribution of selective effects of mutations in the genome, changes in the fitness landscape that occur over time, and the location of sites involved in key adaptations that distinguish modern-day species. We introduce a novel method for the analysis of variation in selection pressures within and between species, spatially along the genome and temporally between lineages. We model codon evolution explicitly using a joint population genetics-phylogenetics approach that we developed for the construction of multiallelic models with mutation, selection, and drift. Our approach has the advantage of performing direct inference on coding sequences, inferring ancestral states probabilistically, utilizing allele frequency information, and generalizing to multiple species. We use a Bayesian sliding window model for intragenic variation in selection coefficients that efficiently combines information across sites and captures spatial clustering within the genome. To demonstrate the utility of the method, we infer selective pressures acting in Drosophila melanogaster and D. simulans from polymorphism and divergence data for 100 X-linked coding regions.</p

Memory and mental time travel in humans and social robots.

From neuroscience, brain imaging and the psychology of memory, we are beginning to assemble an integrated theory of the brain subsystems and pathways that allow the compression, storage and reconstruction of memories for past events and their use in contextualizing the present and reasoning about the future-mental time travel (MTT). Using computational models, embedded in humanoid robots, we are seeking to test the sufficiency of this theoretical account and to evaluate the usefulness of brain-inspired memory systems for social robots. In this contribution, we describe the use of machine learning techniques-Gaussian process latent variable models-to build a multimodal memory system for the iCub humanoid robot and summarize results of the deployment of this system for human-robot interaction. We also outline the further steps required to create a more complete robotic implementation of human-like autobiographical memory and MTT. We propose that generative memory models, such as those that form the core of our robot memory system, can provide a solution to the symbol grounding problem in embodied artificial intelligence. This article is part of the theme issue 'From social brains to social robots: applying neurocognitive insights to human-robot interaction'.Funding. The preparation of this chapter was supported by funding from the EU Seventh Framework Programme as part of the projects Experimental Functional Android Assistant (EFAA, FP7-ICT-270490) and What You Say Is What You Did (WYSIWYD, FP7-ICT-612139) and by the EU H2020 Programme as part of the Human Brain Project (HBP-SGA1, 720270; HBP-SGA2, 785907). Acknowledgements. The authors are grateful to Paul Verschure, Peter Dominey, Giorgio Metta, Yiannis Demiris and the other members of the WYSIWYD and EFAA consortia; to members of the HBP EPISENSE group; and to our colleagues at the University of Sheffield who have helped us to develop memory systems for the iCub, particularly Luke Boorman, Harry Jackson and Matthew Evans. The Sheffield iCub was purchased with the support of the UK Engineering and Physical Sciences Research Council (EPSRC)

Stable isotope metabolomics of pulmonary artery smooth muscle and endothelial cells in pulmonary hypertension and with TGF-beta treatment.

Altered metabolism in pulmonary artery smooth muscle cells (PASMCs) and endothelial cells (PAECs) contributes to the pathology of pulmonary hypertension (PH), but changes in substrate uptake and how substrates are utilized have not been fully characterized. We hypothesized stable isotope metabolomics would identify increased glucose, glutamine and fatty acid uptake and utilization in human PASMCs and PAECs from PH versus control specimens, and that TGF-β treatment would phenocopy these metabolic changes. We used 13C-labeled glucose, glutamine or a long-chain fatty acid mixture added to cell culture media, and mass spectrometry-based metabolomics to detect and quantify 13C-labeled metabolites. We found PH PASMCs had increased glucose uptake and utilization by glycolysis and the pentose shunt, but no changes in glutamine or fatty acid uptake or utilization. Diseased PAECs had increased proximate glycolysis pathway intermediates, less pentose shunt flux, increased anaplerosis from glutamine, and decreased fatty acid β-oxidation. TGF-β treatment increased glycolysis in PASMCs, but did not recapitulate the PAEC disease phenotype. In TGF-β-treated PASMCs, glucose, glutamine and fatty acids all contributed carbons to the TCA cycle. In conclusion, PASMCs and PAECs collected from PH subjects have significant changes in metabolite uptake and utilization, partially recapitulated by TGF-β treatment

Learning on a Budget Using Distributional RL

Agents acting in real-world scenarios often have constraints such as finite budgets or daily job performance targets. While repeated (episodic) tasks can be solved with existing RL algorithms, methods need to be extended if the repetition depends on performance. Recent work has introduced a distributional perspective on reinforcement learning, providing a model of episodic returns. Inspired by these results we contribute the new budget- and risk-aware distributional reinforcement learning (BRAD-RL) algorithm that bootstraps from the C51 distributional output and then uses value iteration to estimate the value of starting an episode with a certain amount of budget. With this strategy we can make budget-wise action selection within each episode and maximize the return across episodes. Experiments in a grid-world domain highlight the benefits of our algorithm, maximizing discounted future returns when low cumulative performance may terminate repetition