High-dimensional mediation analysis is often associated with a multiple testing problem for detecting significant mediators. Assessing the uncertainty of this detecting process via false discovery rate (FDR) has garnered great interest. To control the FDR in multiple testing, two essential steps are involved: ranking and selection. Existing approaches either construct p-values without calibration or disregard the joint information across tests, leading to conservation in FDR control or non-optimal ranking rules for multiple hypotheses. In this paper, we develop an adaptive mediation detection procedure (referred to as "AMDP") to identify relevant mediators while asymptotically controlling the FDR in high-dimensional mediation analysis. AMDP produces the optimal rule for ranking hypotheses and proposes a data-driven strategy to determine the threshold for mediator selection. This novel method captures information from the proportions of composite null hypotheses and the distribution of p-values, which turns the high dimensionality into an advantage instead of a limitation. The numerical studies on synthetic and real data sets illustrate the performances of AMDP compared with existing approaches.

Comicboarding is a participatory design method that uses specially created comic books to generate engaging, productive brainstorming sessions with children. By leveraging known plot formats, interaction styles, and characters in comics, researchers can elicit ideas even from children who are not accustomed to brainstorming, such as those from schools were rote learning is the norm. We conducted an experiment using two variants of the comicboarding methodology with 17 children in China, where traditional participatory design may fail in the face of local cultural practices. The results suggest that comicboarding holds promise for co-design with children.