Exploring Level Blending across Platformers via Paths and Affordances

Techniques for procedural content generation via machine learning (PCGML) have been shown to be useful for generating novel game content. While used primarily for producing new content in the style of the game domain used for training, recent works have increasingly started to explore methods for discovering and generating content in novel domains via techniques such as level blending and domain transfer. In this paper, we build on these works and introduce a new PCGML approach for producing novel game content spanning multiple domains. We use a new affordance and path vocabulary to encode data from six different platformer games and train variational autoencoders on this data, enabling us to capture the latent level space spanning all the domains and generate new content with varying proportions of the different domains.Comment: 6 pages, 5 figures, 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020

Development and exploitation of a controlled vocabulary in support of climate modelling

There are three key components for developing a metadata system: a container structure laying out the key semantic issues of interest and their relationships; an extensible controlled vocabulary providing possible content; and tools to create and manipulate that content. While metadata systems must allow users to enter their own information, the use of a controlled vocabulary both imposes consistency of definition and ensures comparability of the objects described. Here we describe the controlled vocabulary (CV) and metadata creation tool built by the METAFOR project for use in the context of describing the climate models, simulations and experiments of the fifth Coupled Model Intercomparison Project (CMIP5). The CV and resulting tool chain introduced here is designed for extensibility and reuse and should find applicability in many more projects

Ro 04-6790-induced cognitive enhancement: No effect in trace conditioning and novel object recognition procedures in adult male Wistar rats

The evidence for cognitively enhancing effects of 5-hydroxytryptamine6 (5-HT6) receptor antagonists such as Ro 04-6790 is inconsistent and seems to depend on the behavioural test variant in use. Trace conditioning holds promise as a behavioral assay for hippocampus-dependent working memory function. Accordingly, Experiment 1 assessed the effect of Ro 04-6790 (5 and 10 mg/kg i.p.) on associating a noise conditioned stimulus paired with foot shock (unconditioned stimulus) at a 3 or 30 s trace interval in adult male Wistar rats. Contextual conditioning was measured as suppression to the contextual cues provided by the experimental chambers and as suppression to a temporally extended light background stimulus which provided an experimental context. Experiment 2 assessed the effect of Ro 04-6790 (5 and 10 mg/kg i.p.) on recognition memory as tested by the exploration of novel relative to familiar objects in an open arena. In Experiment 1, Ro 04-6790 (5 and 10 mg/kg) was without effect on trace and contextual conditioning. In Experiment 2, there was no indication of the expected improvement under Ro 04-6790 at the same doses previously found to enhance recognition memory as measured in tests of novel object exploration. Thus, there was no evidence that treatment with the 5-HT6 receptor antagonist Ro 04-6790 acted as a cognitive enhancer in either trace conditioning or object recognition procedures. We cannot exclude the possibility that the experimental procedures used in the present study would have been sensitive to the cognitive enhancing effects of Ro 04-6790 in a different dose range, behavioral test variant, or in a different strain of rat. Nonetheless the drug treatment was not ineffective in that object exploration was reduced under 10 mg/kg Ro 04-6790

Repair of Acute Respiratory Distress Syndrome in COVID-19 by Stromal Cells (REALIST-COVID Trial):A Multicentre, Randomised, Controlled Trial

RationaleMesenchymal stromal cells (MSCs) may modulate inflammation, promoting repair in COVID-19-related Acute Respiratory Distress Syndrome (ARDS).ObjectivesWe investigated safety and efficacy of ORBCEL-C (CD362-enriched, umbilical cord-derived MSCs) in COVID-related ARDS.MethodsThis multicentre, randomised, double-blind, allocation concealed, placebo-controlled trial (NCT03042143) randomised patients with moderate-to-severe COVID-related ARDS to receive ORBCEL-C (400million cells) or placebo (Plasma-Lyte148).MeasurementsThe primary safety and efficacy outcomes were incidence of serious adverse events and oxygenation index at day 7 respectively. Secondary outcomes included respiratory compliance, driving pressure, PaO2/FiO2 ratio and SOFA score. Clinical outcomes relating to duration of ventilation, length of intensive care unit and hospital stays, and mortality were collected. Long-term follow up included diagnosis of interstitial lung disease at 1 year, and significant medical events and mortality at 2 years. Transcriptomic analysis was performed on whole blood at day 0, 4 and 7.Main results60 participants were recruited (final analysis n=30 ORBCEL-C, n=29 placebo: 1 in placebo group withdrew consent). 6 serious adverse events occurred in the ORBCEL-C and 3 in the placebo group, RR 2.9(0.6-13.2)p=0.25. Day 7 mean[SD] oxygenation index did not differ (ORBCEL-C 98.357.2], placebo 96.667.3). There were no differences in secondary surrogate outcomes, nor mortality at day 28, day 90, 1 or 2 years. There was no difference in prevalence of interstitial lung disease at 1year nor significant medical events up to 2 years. ORBCEL-C modulated the peripheral blood transcriptome.ConclusionORBCEL-C MSCs were safe in moderate-to-severe COVID-related ARDS, but did not improve surrogates of pulmonary organ dysfunction. Clinical trial registration available at www.Clinicaltrialsgov, ID: NCT03042143. This article is open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/)

c-Myc deregulation induces mRNA capping enzyme dependency

c-Myc is a potent driver of many human cancers. Since strategies for directly targeting c-Myc protein have had limited success, upstream regulators and downstream effectors of c-Myc are being investigated as alternatives for therapeutic intervention. c-Myc regulates transcription and formation of the mRNA cap, which is important for transcript maturation and translation. However, the direct mechanism by which c-Myc upregulates mRNA capping is unclear. mRNA cap formation initiates with the linkage of inverted guanosine via a triphosphate bridge to the first transcribed nucleotide, catalysed by mRNA capping enzyme (CE/RNGTT). Here we report that c-Myc increases the recruitment of catalytically active CE to RNA polymerase II and to its target genes. c-Myc-induced target gene expression, cell proliferation and cell transformation is highly dependent on CE, but only when c-Myc is deregulated. Cells retaining normal control of c-Myc expression are insensitive to repression of CE. c-Myc expression is also dependent on CE. Therefore, inhibiting CE provides an attractive route for selective therapeutic targeting of cancer cells which have acquired deregulated c-Myc

Optimizing sparse testing for genomic prediction of plant breeding crops

While sparse testing methods have been proposed by researchers to improve the efficiency of genomic selection (GS) in breeding programs, there are several factors that can hinder this. In this research, we evaluated four methods (M1–M4) for sparse testing allocation of lines to environments under multi-environmental trails for genomic prediction of unobserved lines. The sparse testing methods described in this study are applied in a two-stage analysis to build the genomic training and testing sets in a strategy that allows each location or environment to evaluate only a subset of all genotypes rather than all of them. To ensure a valid implementation, the sparse testing methods presented here require BLUEs (or BLUPs) of the lines to be computed at the first stage using an appropriate experimental design and statistical analyses in each location (or environment). The evaluation of the four cultivar allocation methods to environments of the second stage was done with four data sets (two large and two small) under a multi-trait and uni-trait framework. We found that the multi-trait model produced better genomic prediction (GP) accuracy than the uni-trait model and that methods M3 and M4 were slightly better than methods M1 and M2 for the allocation of lines to environments. Some of the most important findings, however, were that even under a scenario where we used a training-testing relation of 15–85%, the prediction accuracy of the four methods barely decreased. This indicates that genomic sparse testing methods for data sets under these scenarios can save considerable operational and financial resources with only a small loss in precision, which can be shown in our cost-benefit analysis

Evolving Synaptic Plasticity with an Evolutionary Cellular Development Model

Since synaptic plasticity is regarded as a potential mechanism for memory formation and learning, there is growing interest in the study of its underlying mechanisms. Recently several evolutionary models of cellular development have been presented, but none have been shown to be able to evolve a range of biological synaptic plasticity regimes. In this paper we present a biologically plausible evolutionary cellular development model and test its ability to evolve different biological synaptic plasticity regimes. The core of the model is a genomic and proteomic regulation network which controls cells and their neurites in a 2D environment. The model has previously been shown to successfully evolve behaving organisms, enable gene related phenomena, and produce biological neural mechanisms such as temporal representations. Several experiments are described in which the model evolves different synaptic plasticity regimes using a direct fitness function. Other experiments examine the ability of the model to evolve simple plasticity regimes in a task -based fitness function environment. These results suggest that such evolutionary cellular development models have the potential to be used as a research tool for investigating the evolutionary aspects of synaptic plasticity and at the same time can serve as the basis for novel artificial computational systems

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Funding GMP, PN, and CW are supported by NHLBI R01HL127564. GMP and PN are supported by R01HL142711. AG acknowledge support from the Wellcome Trust (201543/B/16/Z), European Union Seventh Framework Programme FP7/2007–2013 under grant agreement no. HEALTH-F2-2013–601456 (CVGenes@Target) & the TriPartite Immunometabolism Consortium [TrIC]-Novo Nordisk Foundation’s Grant number NNF15CC0018486. JMM is supported by American Diabetes Association Innovative and Clinical Translational Award 1–19-ICTS-068. SR was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (Grant No 312062), the Finnish Foundation for Cardiovascular Research, the Sigrid Juselius Foundation, and University of Helsinki HiLIFE Fellow and Grand Challenge grants. EW was supported by the Finnish innovation fund Sitra (EW) and Finska Läkaresällskapet. CNS was supported by American Heart Association Postdoctoral Fellowships 15POST24470131 and 17POST33650016. Charles N Rotimi is supported by Z01HG200362. Zhe Wang, Michael H Preuss, and Ruth JF Loos are supported by R01HL142302. NJT is a Wellcome Trust Investigator (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 217065/Z/19/Z), is supported by the University of Bristol NIHR Biomedical Research Centre (BRC-1215–2001) and the MRC Integrative Epidemiology Unit (MC_UU_00011), and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/A19169). Ruth E Mitchell is a member of the MRC Integrative Epidemiology Unit at the University of Bristol funded by the MRC (MC_UU_00011/1). Simon Haworth is supported by the UK National Institute for Health Research Academic Clinical Fellowship. Paul S. de Vries was supported by American Heart Association grant number 18CDA34110116. Julia Ramierz acknowledges support by the People Programme of the European Union’s Seventh Framework Programme grant n° 608765 and Marie Sklodowska-Curie grant n° 786833. Maria Sabater-Lleal is supported by a Miguel Servet contract from the ISCIII Spanish Health Institute (CP17/00142) and co-financed by the European Social Fund. Jian Yang is funded by the Westlake Education Foundation. Olga Giannakopoulou has received funding from the British Heart Foundation (BHF) (FS/14/66/3129). CHARGE Consortium cohorts were supported by R01HL105756. Study-specific acknowledgements are available in the Additional file 32: Supplementary Note. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services.Peer reviewedPublisher PD