Learning the Designer's Preferences to Drive Evolution

This paper presents the Designer Preference Model, a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user's design style to better assess the tool's procedurally generated content with respect to that user's preferences. Through this approach, we aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the Evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.Comment: 16 pages, Accepted and to appear in proceedings of the 23rd European Conference on the Applications of Evolutionary and bio-inspired Computation, EvoApplications 202

Group studio cycling; an effective intervention to improve cardio-metabolic health in overweight physically inactive individuals

Introduction: Supervised, laboratory based studies of high intensity interval training (HIIT) is effective at improving health markers in groups at risk of cardiovascular and metabolic disease. Studio cycling, incorporating aerobic and high intensity exercise, may offer a platform for the implementation of HIIT within the wider community. Methods: Eight, overweight, physically inactive (<1.5 hr·wk-1) but otherwise healthy volunteers completed eight weeks of supervised studio cycling lasting 20-50 minutes 3 times per week. Participants underwent assessment for maximal oxygen uptake (VO2max) body composition, blood lipids, glucose tolerance and insulin resistance before and after the intervention. Results: Adherence to training was >95%. Mean and peak intensity were equivalent to 83% and 97% of HRmax·VO2max increased from 27.1 ± 4.7 mL·kg·min-1 to 30.3 ± 4.3 mL·kg·min-1 (p < 0.0001). Body fat percentage was reduced by 13.6% from 31.8 ± 2.4% to 27.5 ± 4.5% (p < 0.05). Total cholesterol (4.8 ± 1.1 mmol·L-1 to 4.2 ± 1.2 mmol·L-1) and low-density lipoprotein cholesterol (2.6 ± 0.9 mmol·L-1 to 2.0 ± 1.2 mmol·L-1) were reduced (both p < 0.05). There were no significant differences to glucose tolerance or insulin resistance. Discussion: Group exercise is effective at improving the cardio-metabolic health in previously physically inactive overweight individuals. Coupled with the high adherence rate, studio cycling offers an effective intervention improving cardiovascular health in physically inactive cohorts. Conclusions: Studio cycling can be implemented as a highly effective high intensity interval training intervention for improving health in overweight, inactive individuals and may promote improved exercise adherence

Automatic Calibration of Artificial Neural Networks for Zebrafish Collective Behaviours using a Quality Diversity Algorithm

During the last two decades, various models have been proposed for fish collective motion. These models are mainly developed to decipher the biological mechanisms of social interaction between animals. They consider very simple homogeneous unbounded environments and it is not clear that they can simulate accurately the collective trajectories. Moreover when the models are more accurate, the question of their scalability to either larger groups or more elaborate environments remains open. This study deals with learning how to simulate realistic collective motion of collective of zebrafish, using real-world tracking data. The objective is to devise an agent-based model that can be implemented on an artificial robotic fish that can blend into a collective of real fish. We present a novel approach that uses Quality Diversity algorithms, a class of algorithms that emphasise exploration over pure optimisation. In particular, we use CVT-MAP-Elites, a variant of the state-of-the-art MAP-Elites algorithm for high dimensional search space. Results show that Quality Diversity algorithms not only outperform classic evolutionary reinforcement learning methods at the macroscopic level (i.e. group behaviour), but are also able to generate more realistic biomimetic behaviours at the microscopic level (i.e. individual behaviour).Comment: 8 pages, 4 figures, 1 tabl

Mapping structural diversity in networks sharing a given degree distribution and global clustering: Adaptive resolution grid search evolution with Diophantine equation-based mutations

Methods that generate networks sharing a given degree distribution and global clustering can induce changes in structural properties other than that controlled for. Diversity in structural properties, in turn, can affect the outcomes of dynamical processes operating on those networks. Since exhaustive sampling is not possible, we propose a novel evolutionary framework for mapping this structural diversity. The three main features of this framework are: (a) subgraph-based encoding of networks, (b) exact mutations based on solving systems of Diophantine equations, and (c) heuristic diversity-driven mechanism to drive resolution changes in the MapElite algorithm.We show that our framework can elicit networks with diversity in their higher-order structure and that this diversity affects the behaviour of the complex contagion model. Through a comparison with state of the art clustered network generation methods, we demonstrate that our approach can uncover a comparably diverse range of networks without needing computationally unfeasible mixing times. Further, we suggest that the subgraph-based encoding provides greater confidence in the diversity of higher-order network structure for low numbers of samples and is the basis for explaining our results with complex contagion model. We believe that this framework could be applied to other complex landscapes that cannot be practically mapped via exhaustive sampling

Target gene selectivity of hypoxia-inducible factor-α in renal cancer cells is conveyed by post-DNA-binding mechanisms

Inactivation of the von Hippel–Lindau tumour suppressor in renal cell carcinoma (RCC) leads to failure of proteolytic regulation of the α subunits of hypoxia-inducible factor (HIF), constitutive upregulation of the HIF complex, and overexpression of HIF target genes. However, recent studies have indicated that in this setting, upregulation of the closely related HIF-α isoforms, HIF-1α and HIF-2α, have contrasting effects on tumour growth, and activate distinct sets of target genes. To pursue these findings, we sought to elucidate the mechanisms underlying target gene selectivity for HIF-1α and HIF-2α. Using chromatin immunoprecipitation to probe binding to hypoxia response elements in vivo, and expression of chimaeric molecules bearing reciprocal domain exchanges between HIF-1α and HIF-2α molecules, we show that selective activation of HIF-α target gene expression is not dependent on selective DNA-binding at the target locus, but depends on non-equivalent C-terminal portions of these molecules. Our data indicate that post-DNA binding mechanisms that are dissimilar for HIF-1α and HIF-2α determine target gene selectivity in RCC cells

Children with Reading Disability Show Brain Differences in Effective Connectivity for Visual, but Not Auditory Word Comprehension

Background: Previous literature suggests that those with reading disability (RD) have more pronounced deficits during semantic processing in reading as compared to listening comprehension. This discrepancy has been supported by recent neuroimaging studies showing abnormal activity in RD during semantic processing in the visual but not in the auditory modality. Whether effective connectivity between brain regions in RD could also show this pattern of discrepancy has not been investigated. Methodology/Principal Findings: Children (8- to 14-year-olds) were given a semantic task in the visual and auditory modality that required an association judgment as to whether two sequentially presented words were associated. Effective connectivity was investigated using Dynamic Causal Modeling (DCM) on functional magnetic resonance imaging (fMRI) data. Bayesian Model Selection (BMS) was used separately for each modality to find a winning family of DCM models separately for typically developing (TD) and RD children. BMS yielded the same winning family with modulatory effects on bottom-up connections from the input regions to middle temporal gyrus (MTG) and inferior frontal gyrus(IFG) with inconclusive evidence regarding top-down modulations. Bayesian Model Averaging (BMA) was thus conducted across models in this winning family and compared across groups. The bottom-up effect from the fusiform gyrus (FG) to MTG rather than the top-down effect from IFG to MTG was stronger in TD compared to RD for the visual modality. The stronge

Epigenetic targeting of Hedgehog pathway transcriptional output through BET bromodomain inhibition

Hedgehog signaling drives oncogenesis in several cancers and strategies targeting this pathway have been developed, most notably through inhibition of Smoothened. However, resistance to Smoothened inhibitors occurs via genetic changes of Smoothened or other downstream Hedgehog components. Here, we overcome these resistance mechanisms by modulating GLI transcription via inhibition of BET bromodomain proteins. We show the BET bromodomain protein, BRD4, regulates GLI transcription downstream of SMO and SUFU and chromatin immunoprecipitation studies reveal BRD4 directly occupies GLI1 and GLI2 promoters, with a substantial decrease in engagement of these sites upon treatment with JQ1, a small molecule inhibitor targeting BRD4. Globally, genes associated with medulloblastoma-specific GLI1 binding sites are downregulated in response to JQ1 treatment, supporting direct regulation of GLI activity by BRD4. Notably, patient- and GEMM-derived Hedgehog-driven tumors (basal cell carcinoma, medulloblastoma and atypical teratoid/rhabdoid tumor) respond to JQ1 even when harboring genetic lesions rendering them resistant to Smoothened antagonists