Bei-Wen Ying

Abstract Medium formulation is critical for successful mammalian cell culture, yet the influence of component blending during medium preparation remains underexplored. Here, we applied machine learning-guided active learning to investigate whether different patterns of medium premixes, designed as cocktail configurations, affect cell culture performance. As a first challenge, CHO-K1 cells were used, and all cocktail configurations were formulated for the eRDF medium nominal recipe.

Abstract Bacterial growth is fundamental to understanding microbial survival strategies and optimizing microbial culture systems, yet the variation in these dynamics across bacteria and environments remains largely unexplored. Here, we present a bacterial growth dataset comprising 11,686 growth curves, including six representative model bacteria (Escherichia coli, Lactiplantibacillus plantarum, Yersinia bercovieri, Brevundimonas subvibrioides, Aeromonas salmonicida subsp.

Abstract Cleaning of public spaces for human activities is commonly employed and reduces the microbial load in these spaces. However, the mechanisms underlying changes in microbial community structure following cleaning remain poorly understood. As a pilot study, we investigated the bacterial community in public toilets before and after cleaning using 16S rRNA sequencing and computational simulations, as well as bacterial colony growth assays and metabolic functional prediction.