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Lactic acid bacteria (LAB) have been studied for several decades to understand and determine their mechanism and interaction within the matrix into which they are introduced. This study aimed to determine the spatial distribution of Lacticaseibacillus rhamnosus GG (LGG) in a dairy matrix and to decipher its behaviour towards milk components, especially fat globules. Two strains of this widely studied bacterium with expected probiotic effects were used: LGG WT with pili on the cell surface and its pili-depleted mutant—LGG ΔspaCBA—in order to determine the involvement of these filamentous proteins. In this work, it was shown that LGG ΔspaCBA was able to limit creaming with a greater impact than the wild-type counterpart. Moreover, confocal imaging evidenced a preferential microbial distribution as aggregates for LGG WT, while the pili-depleted strain tended to be homogenously distributed and found as individual chains. The observed differences in creaming are attributed to the indirect implication of SpaCBA pili. Indeed, the bacteria-to-bacteria interaction surpassed the bacteria-to-matrix interaction, reducing the bacterial surface exposed to raw milk. Conversely, LGG ΔspaCBA may form a physical barrier responsible for preventing milk fat globules from rising to the surface.

The latest advances in car automation present new challenges in vehicle–driver interactions. Indeed, acceptance and adoption of high levels of automation (when full control of the driving task is given to the automated system) are conditioned by human factors such as user trust. In this work, we study the impact of anthropomorphic design on user trust in the context of a highly automated car. A virtual assistant was designed using two levels of anthropomorphic design: “voice-only” and “voice with visual appearance”. The visual appearance was a three-dimensional model, integrated as a hologram in the cockpit of a driving simulator. In a driving simulator study, we compared the three interfaces: two versions of the virtual assistant interface and the baseline interface with no anthropomorphic attributes. We measured trust versus perceived anthropomorphism. We also studied the evolution of trust throughout a range of driving scenarios. We finally analyzed participants’ reaction time to takeover request events. We found a significant correlation between perceived anthropomorphism and trust. However, the three interfaces tested did not significantly differentiate in terms of perceived anthropomorphism while trust converged over time across all our measurements. Finally, we found that the anthropomorphic assistant positively impacts reaction time for one takeover request scenario. We discuss methodological issues and implication for design and further research.