The bacterial domain produces numerous types of sphingolipids with various physiological functions. In the human microbiome, commensal and pathogenic bacteria use these lipids to modulate the host inflammatory system. Despite their growing importance, their biosynthetic pathway remains undefined since several key eukaryotic ceramide synthesis enzymes have no bacterial homologue. Here we used genomic and biochemical approaches to identify six proteins comprising the complete pathway for bacterial ceramide synthesis. Bioinformatic analyses revealed the widespread potential for bacterial ceramide synthesis leading to our discovery of the first known Gram-positive species to produce ceramides. Biochemical evidence demonstrated that the bacterial pathway operates in a different order than in eukaryotes. Furthermore, phylogenetic analyses support the hypothesis that the bacterial and eukaryotic ceramide pathways evolved independently.

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The prospect of implementing recommender systems within the context of cultural research has not been explored nearly as much compared to implementation in e-commerce websites and applications. Recommender systems allow for users to be shown new objects either based upon object similarity or based upon what the algorithm thinks the user will like – which can be derived from user feedback and comparing the user to other similar users. This paper discusses how a recommender system could benefit an augmented reality application that enables 3D viewing of artifacts – as part of the Tangible Cultural Analytics (TCA) project at Ryerson University’s Synaesthetic Lab. This paper outlines four recommender systems: 1) content-based filtering, 2) collaborative filtering, 3) cluster models 4) search based models, and 5) hybrid models; discussing the pros and cons to each. Ultimately, a content-based model without the user profile aspect was chosen for this stage in the prototype. This model showed us just how much potential these recommender systems have when helping cultural researchers uncover new relationships and pieces of history through the study and comparison of artifacts.

Bacteria synthesize numerous types of sphingolipids with various physiological functions. Despite their roles in mediating host inflammation, cellular differentiation, and protection from environmental stress, their biosynthetic pathway remains undefined since several essential eukaryotic ceramide synthesis enzymes have no bacterial homologue. Using genetic and biochemical approaches, we identified the complete pathway for bacterial ceramide synthesis. Bioinformatic and phylogenetic analyses revealed the presence of these genes in a broad range of bacterial taxa and led to our discovery of the first Gram‐positive species to produce ceramides. Biochemical experiments with purified proteins support a model in which the bacterial pathway operates in a different order than in eukaryotes. Furthermore, phylogenetic analyses are consistent with the independent evolution of the bacterial and eukaryotic ceramide pathways. Current work is being done to elucidate the specific subcellular localization of the synthetic enzymes and identify additional proteins required for the transport of sphingolipids to the outer membrane of Gram‐negative bacteria.

The prospect of implementing recommender systems within the context of cultural research has not been explored nearly as much compared to implementation in e-commerce websites and applications. Recommender systems allow for users to be shown new objects either based upon object similarity or based upon what the algorithm thinks the user will like – which can be derived from user feedback and comparing the user to other similar users. This paper discusses how a recommender system could benefit an augmented reality application that enables 3D viewing of artifacts – as part of the Tangible Cultural Analytics (TCA) project at Ryerson University’s Synaesthetic Lab. This paper outlines four recommender systems: 1) content-based filtering, 2) collaborative filtering, 3) cluster models 4) search based models, and 5) hybrid models; discussing the pros and cons to each. Ultimately, a content-based model without the user profile aspect was chosen for this stage in the prototype. This model showed us just how much potential these recommender systems have when helping cultural researchers uncover new relationships and pieces of history through the study and comparison of artifacts.