In OVA-sensitized and challenged mice, γδ T cells expressing Vγ1 enhance airway hyperresponsiveness (AHR) but the underlying mechanism is unclear. These cells also reduce IL-10 levels in the airways, suggesting that they might function by inhibiting CD4+CD25+ regulatory T cells (Treg) or other CD4+ T cells capable of producing IL-10 and suppressing AHR. Indeed, sensitization and challenge with OVA combined with inactivation of Vγ1+ cells increased CD4+CD25+ cells in the lung, and markedly those capable of producing IL-10. The cellular change was associated with increased IL-10 and TGF-β levels in the airways, and a decrease of IL-13. Treg include naturally occurring Foxp3+ Treg, inducible Foxp3− Treg, and antigen-specific Treg many of which express folate receptor 4 (FR4). Although Foxp3 gene expression in the lung was also increased pulmonary CD4+ T cells, expressing Foxp3-protein or FR4 remained stable. Therefore, the inhibition by Vγ1+ γδ T cells might not be targeting Foxp3+ Treg but rather CD4+ T cells destined to produce IL-10.
Devices used in Internet of Things (IoT) networks continue to perform sensing, gathering, modifying, and forwarding data. Since IoT networks have a lot of participants, mitigating and reducing collisions among the participants becomes an essential requirement for the Medium Access Control (MAC) protocols to increase system performance. A collision occurs in wireless channel when two or more nodes try to access the channel at the same time. In this paper, a reinforcement learning-based MAC protocol was proposed to provide high throughput and alleviate the collision problem. A collaboratively predicted Q-value was proposed for nodes to update their value functions by using communications trial information of other nodes. Our proposed protocol was confirmed by intensive system level simulations that it can reduce convergence time in 34.1% compared to the conventional Q-learning-based MAC protocol.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.