Immune-mediated diseases affect more than 20% of the population, and many autoimmune diseases affect the skin. Drug repurposing (or repositioning) is a cost-effective approach for finding drugs that can be used to treat diseases for which they are currently not prescribed. We implemented an efficient bioinformatics approach using word embedding to summarize drug information from more than 20 million articles and applied machine learning to model the drug-disease relationship. We trained our drug repurposing approach separately on nine cutaneous diseases (including psoriasis, atopic dermatitis, and alopecia areata) and eight other immunemediated diseases and obtained a mean area under the receiver operating characteristic of 0.93 in crossvalidation. Focusing in particular on psoriasis, a chronic inflammatory condition of skin that affects more than 100 million people worldwide, we were able to confirm drugs that are known to be effective for psoriasis and to identify potential candidates used to treat other diseases. Furthermore, the targets of drug candidates predicted by our approach were significantly enriched among genes differentially expressed in psoriatic lesional skin from a large-scale RNA sequencing cohort. Although our algorithm cannot be used to determine clinical efficacy, our work provides an approach for suggesting drugs for repurposing to immune-mediated cutaneous diseases.

Recent advancements in object-tracking technologies can turn mundane constructive assemblies into Tangible User Interfaces (TUI) media. Users rely on instructions or their own creativity to build both permanent and temporary structures out of such objects. However, most existing object-tracking technologies focus on tracking structures as monoliths, making it impossible to infer and track the user's assembly process and the resulting structures. Technologies that can track the assembly process often rely on specially fabricated assemblies, limiting the types of objects and structures they can track. Here, we present StructureSense, a tracking system based on passive UHF-RFID sensing that infers constructive assembly structures from object motion. We illustrated StructureSense in two use cases (as guided instructions and authoring tool) on two different constructive sets (wooden lamp and Jumbo Blocks), and evaluated system performance and usability. Our results showed the feasibility of using StructureSense to track mundane constructive assembly structures.