This study examined the effect that silica content in diatom cells has on the behavior of protists. The diatoms Thalassiosira weissflogii and T. pseudonana were cultured in high or low light conditions to achieve low and high silica contents, respectively. These cells were then fed to a heterotrophic dinoflagellate Noctiluca scintillans and a ciliate Euplotes sp. in single and mixed diet experiments. Our results showed that in general, N. scintillans and Euplotes sp. both preferentially ingested the diatoms with a low silica content rather than those with a high silica content. However, Euplotes sp. seemed to be less influenced by the silica content than was N. scintillans. In the latter case, the clearance and ingestion rate of the low silica diatoms were significantly higher, both in the short (6-h) and long (1-d) duration grazing experiments. Our results also showed that N. scintillans required more time to digest the high silica-containing cells. As the high silica diatoms are harder to digest, this might explain why N. scintillans exhibits a strong preference for the low silica prey. Thus, the presence of high silica diatoms might limit the ability of the dinoflagellate to feed. Our findings suggest that the silica content of diatoms affects their palatability and digestibility and, consequently, the grazing activity and selectivity of protozoan grazers.

Building management systems tout numerous benefits, such as energy efficiency and occupant comfort but rely on vast amounts of data from various sensors. Advancements in machine learning algorithms make it possible to extract personal information about occupants and their activities beyond the intended design of a non-intrusive sensor. However, occupants are not informed of data collection and possess different privacy preferences and thresholds for privacy loss. While privacy perceptions and preferences are most understood in smart homes, limited studies have evaluated these factors in smart office buildings, where there are more users and different privacy risks. To better understand occupants’ perceptions and privacy preferences, we conducted twenty-four semi-structured interviews between April 2022 and May 2022 on occupants of a smart office building. We found that data modality features and personal features contribute to people’s privacy preferences. The features of the collected modality define data modality features – spatial, security, and temporal context. In contrast, personal features consist of one’s awareness of data modality features and data inferences, definitions of privacy and security, and the available rewards and utility. Our proposed model of people’s privacy preferences in smart office buildings helps design more effective measures to improve people’s privacy.

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