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Mucosal-associated invariant T (MAIT) cells are innate-like T cells that are highly abundant in human blood and tissues. Most MAIT cells have an invariant T cell receptor (TCR) α chain that uses TRAV1-2 joined to TRAJ33/20/12 and recognize metabolites from bacterial riboflavin synthesis bound to the antigen-presenting molecule, MR1. Recently, our attempts to identify alternative MR1-presented antigens led to the discovery of rare MR1-restricted T cells with non-TRAV1-2 TCRs. Because altered antigen specificity is likely to lead to altered affinity for the most potent known antigen, 5-(2-oxopropylideneamino)-6-D-ribitylaminouracil (5-OP-RU), we performed bulk TCRα and β chain sequencing, and single cell-based paired TCR sequencing, on T cells that bound the MR1-5-OP-RU tetramer, but with differing intensities. Bulk sequencing showed that use of V genes other than TRAV1-2 was enriched among MR1-5-OP-RU tetramerlow cells. Whereas we initially interpreted these as diverse MR1-restricted TCRs, single cell TCR sequencing revealed that cells expressing atypical TCRα chains also co-expressed an invariant MAIT TCRα chain. Transfection of each non-TRAV1-2 TCRα chain with the TCRβ chain from the same cell demonstrated that the non-TRAV1-2 TCR did not bind the MR1-5-OP-RU tetramer. Thus, dual TCRα chain expression in human T cells and competition for the endogenous β chain explains the existence of some MR1-5-OP-RU tetramerlow T cells. The discovery of simultaneous expression of canonical and non-canonical TCRs on the same T cell means that claims of roles for non-TRAV1-2 TCR in MR1 response must be validated by TCR transfer-based confirmation of antigen specificity.

Tuberculosis (TB) an infectious disease and remains a major cause of death globally. The World Health Organization (WHO) estimates that there were 10.4 million new TB cases worldwide in 2015. The majority of the infected populations come from resource-poor and marginalized communities with poor healthcare infrastructure. It is critical to reduce TB diagnosis delay in mitigating disease transmission and minimizing the reproductive rate of the tuberculosis epidemic. To combine machine learning and mobile computing techniques may help to accelerate the TB diagnosis among these communities. The goal of our research is to reduce TB patient wait times for being diagnosed by developing new machine learning techniques and mobile health technologies. In this paper, major technique barriers and proposed system architecture are first introduced. Then two major progresses are reported: (1) To develop an X-ray image database and annotation software dedicated for automated TB screening. The annotation software can help to highlight the TB manifestations, which are very useful for machine learning algorithms; (2) To develop effective and efficient computational models to classify the image into different category of TB manifestations. The model we proposed is a deep convolutional neural networks (CNN)-based models. We have conducted substantial experiments and the results have demonstrated that our approach is promising. We envision our future work includes two research activities. First, we plan to improve the performance of the algorithms with deeper neural networks. Second, we plan to implement our algorithms on mobile device and deploy our system in the city of Carabayllo, a high-burden TB area in Lima, the capital of Peru.