This paper shows the influence of multitasking on the usage of a voice assistant. Voice assistants allow users to input queries over a speech-only channel, and as a result they do not require the same attention as a traditional search engine. Existing research describes the effects of the use of a voice assistant on driving and other demanding activities, however, there is no research that describes how that demanding activity influences the usage of the voice assistant. To research these effects, three sub questions have been constructed and answered. These tackled three characteristics to describe the usage: the query formulation, the knowledge gain and the user experience. To answer these questions a user study was conducted where the participants used a voice assistant in three situations. Two of these situations included a distraction in the form of a game. We found that the presence of dual tasking results in shorter queries. We also found that a higher intensity of the session can decrease the knowledge gain.

Knowing the relation between cell types is crucial for translating experimental results from mice to humans. Establishing cell type matches, however, is hindered by the biological differences between the species. A substantial amount of evolutionary information between genes that could be used to align the species, is discarded by most of the current methods since they only use one-to-one orthologous genes. Some methods try to retain the information by explicitly including the relation between genes, however, not without caveats. In this work, we present a model to Transfer and Align Cell Types in Cross-Species (TACTiCS). First, TACTiCS uses an natural language processing model to match genes using their protein sequences. Next, TACTiCS employs a neural network to classify cell types within a species. Afterwards, TACTiCS uses transfer learning to propagate cell type labels between species. We applied TACTiCS on scRNA-seq data of the primary motor cortex and the ventral tegmental area. Our model can accurately match and align cell types on these datasets. Moreover, at a high resolution, our model outperforms two state-of-the-art methods, SAMap and CAME. Finally, we show that our gene matching method results in better matches than BLAST, both in our model and SAMap.