Bringing Authoring Tools for Intelligent Tutoring Systems and Serious Games Closer Together: Integrating GIFT with the Unity Game Engine

In an effort to bring intelligent tutoring system (ITS) authoring tools closer to content authoring tools, the authors are working to integrate GIFT with the Unity game engine and editor. The paper begins by describing challenges faced by modern intelligent tutors and the motivation behind the integration effort, with special consideration given to how this work will better meet the needs of future serious games. The next three sections expand on these major hurdles more thoroughly, followed by proposed design enhancements that would allow GIFT to overcome these issues. Finally, an overview is given of the authors’ current progress towards implementing the proposed design. The key contribution of this work is an abstraction of the interface between intelligent tutoring systems and serious games, thus enabling ITS authors to implement more complex training behaviors

Simplified representation of a hypothetical coexpression network.

<p>Node A represents a hub gene while node B represents a peripheral gene. Lines connecting nodes represent network edges, and reflect correlations in expression.</p

Connectivity in gene coexpression networks negatively correlates with rates of molecular evolution in flowering plants

<div><p>Gene coexpression networks are a useful tool for summarizing transcriptomic data and providing insight into patterns of gene regulation in a variety of species. Though there has been considerable interest in studying the evolution of network topology across species, less attention has been paid to the relationship between network position and patterns of molecular evolution. Here, we generated coexpression networks from publicly available expression data for seven flowering plant taxa (<i>Arabidopsis thaliana</i>, <i>Glycine max</i>, <i>Oryza sativa</i>, <i>Populus</i> spp., <i>Solanum lycopersicum</i>, <i>Vitis</i> spp., and <i>Zea mays</i>) to investigate the relationship between network position and rates of molecular evolution. We found a significant negative correlation between network connectivity and rates of molecular evolution, with more highly connected (i.e., “hub”) genes having significantly lower nonsynonymous substitution rates and <i>dN</i>/<i>dS</i> ratios compared to less highly connected (i.e., “peripheral”) genes across the taxa surveyed. These findings suggest that more centrally located hub genes are, on average, subject to higher levels of evolutionary constraint than are genes located on the periphery of gene coexpression networks. The consistency of this result across disparate taxa suggests that it holds for flowering plants in general, as opposed to being a species-specific phenomenon.</p></div

Linear regression of gene connectivity of seven taxa analyzed.

<p>Taxa: <i>A</i>. <i>thaliana</i>, <i>G</i>. <i>max</i>, <i>Populus spp</i>., <i>S</i>. <i>lycopersicum</i>, <i>Vitis spp</i>., <i>O</i>. <i>sativa</i>, and <i>Z</i>. <i>mays</i>, against (a): non-synonymous substitutions (<i>dN</i>), (b): synonymous substitutions (<i>dS</i>), (c): estimates of adaptive evolution (ω = <i>dN</i>/<i>dS</i>) and (d): number of connections in ortholog comparison. Circles represent genes, while the regression coefficient, represented as Kendall's tau (τ) coefficient, is the dashed line. Significance is indicated by bold text. Note that all significant results except the two marked with an asterisk (*) remained significant after correcting for multiple comparisons (see text for details).</p

BEST input file JC model

BEST input file JC mode

*BEAST input file Pipa calibration GTR codon model

*BEAST input file Pipa calibration GTR codon mode

*BEAST input file Pipa and Root calibration GTR codon model

*BEAST input file Pipa and Root calibration GTR codon mode

*BEAST input file Pipa calibration JC codon model

*BEAST input file Pipa calibration JC codon mode

*BEAST input file Root calibration HKY codon model

*BEAST input file Root calibration HKY codon mode

*BEAST input file Root calibration JC codon model

*BEAST input file Root calibration JC codon mode