Fluorescent RNA-based biosensors are useful tools for real-time detection of molecules in living cells. These biosensors typically consist of a chromophore-binding aptamer and a target-binding aptamer, whereby the chromophore-binding aptamer is destabilized until a target is captured, which causes a conformational change to permit chromophore binding and an increase in fluorescence. The target-binding region is typically fabricated using known riboswitch motifs, which are already known to have target specificity and undergo structural changes upon binding. However, known riboswitches only exist for a limited number of molecules, significantly constraining biosensor design. To overcome this challenge, we designed a framework for producing mammalian cell-compatible biosensors using aptamers selected from a large random library by Capture-SELEX. As a proof-of-concept, we generated and characterized a fluorescent RNA biosensor against L-dopa, the precursor of several neurotransmitters. Overall, we suggest that this approach will have utility for generating RNA biosensors that can reliably detect custom targets in mammalian cells.

Citizen science games must balance task difficulty with player skill to ensure optimal engagement and performance. This issue has been previously addressed via player-level matchmaking, a dynamic difficulty adjustment method in which player and level ratings are used to present levels best suited for players' individual abilities. However, this work has been done in small, isolated test games and left out potential techniques that could further improve player performance. Therefore, we examined the effects of player-level matchmaking in Foldit, a live citizen science game. An experiment with 221 players demonstrated that dynamic matchmaking approaches led to significantly more levels completed, as well as a more challenging highest level completed, compared to random level ordering, but not greater than a static approach. We conclude that player-level matchmaking is worth consideration in the context of live citizen science games, potentially paired with other dynamic difficulty adjustment methods.