Critical Role of an MHC Class I-Like/Innate-Like T Cell Immune Surveillance System in Host Defense against Ranavirus (Frog Virus 3) Infection

Source at https://doi.org/10.3390/v11040330. Besides the central role of classical Major Histocompatibility Complex (MHC) class Ia-restricted conventional Cluster of Differentiation 8 (CD8) T cells in antiviral host immune response, the amphibian Xenopus laevis critically rely on MHC class I-like (mhc1b10.1.L or XNC10)-restricted innate-like (i)T cells (iVα6 T cells) to control infection by the ranavirus Frog virus 3 (FV3). To complement and extend our previous reverse genetic studies showing that iVα6 T cells are required for tadpole survival, as well as for timely and effective adult viral clearance, we examined the conditions and kinetics of iVα6 T cell response against FV3. Using a FV3 knock-out (KO) growth-defective mutant, we found that upregulation of the XNC10 restricting class I-like gene and the rapid recruitment of iVα6 T cells depend on detectable viral replication and productive FV3 infection. In addition, by in vivo depletion with XNC10 tetramers, we demonstrated the direct antiviral effector function of iVα6 T cells. Notably, the transitory iVα6 T cell defect delayed innate interferon and cytokine gene response, resulting in long-lasting negative inability to control FV3 infection. These findings suggest that in Xenopus and likely other amphibians, an immune surveillance system based on the early activation of iT cells by non-polymorphic MHC class-I like molecules is important for efficient antiviral immune response

EURECA: Enhanced Understanding of Real Environments via Crowd Assistance

Indoor robots hold the promise of automatically handling mundane daily tasks, helping to improve access for people with disabilities, and providing on-demand access to remote physical environments. Unfortunately, the ability to understand never-before-seen objects in scenes where new items may be added (e.g., purchased) or altered (e.g., damaged) on a regular basis remains an open challenge for robotics.  In this paper, we introduce EURECA, a mixed-initiative system that leverages online crowds of human contributors to help robots robustly identify 3D point cloud segments corresponding to user-referenced objects in near real-time. EURECA allows robots to understand multi-object 3D scenes on-the-fly (in ~40 seconds) by providing groups of non-expert crowd workers with intelligent tools that can segment objects more quickly (~70% faster) and more accurately (6% higher F1 score) than individuals. More broadly, EURECA introduces the first real-time crowdsourcing tool that addresses the challenge of learning about new objects in real-world settings, creating a new source of data for training robots online, as well as a platform for studying mixed-initiative crowdsourcing workflows for understanding 3D scenes