The similarity between pairs of people is often measured on relatively static traits and at a given point in time. Moving beyond this approach, a burgeoning line of research is investigating temporal dyadic similarity on traits and behaviors, such as health activities. Our study contributes to this line of inquiry by using fine-grained longitudinal data obtained from sensors, mobile devices, and surveys to examine whether we can observe distinct types of dyadic similarity trajectories based on physical activity, and if so, what dyad-level properties predict membership in each trajectory class. Treating daily differences in the steps for dyads as time series, we use k-shape clustering to identify classes of similarity trajectories and logistic regression to examine the link between trajectory class and key dyad-level factors. We identify 21 dyadic trajectory clusters and find that trajectory membership predicts dyadic connectivity in the communication network, as well as racial and religious—but not gender-based—similarity. We conclude by noting how research on dyadic similarity trajectories can be further integrated with ongoing work in social network analysis.

ABSTRACTPortable and inexpensive analytical tools are required to monitor pharmaceutical quality in technology limited settings including low- and middle-income countries (LMICs). Whole cell yeast biosensors have the potential to help meet this need. However, most of the read-outs for yeast biosensors require expensive equipment or reagents. To overcome this challenge, we have designed a yeast biosensor that produces a unique scent as a readout. This inducible scent biosensor, or “scentsor,” does not require the user to administer additional reagents for reporter development and utilizes only the user’s nose to be “read.” In this manuscript, we describe a scentsor that is responsive to the hormone estradiol (E2). The best estimate threshold (BET) for E2 detection with a panel of human volunteers (n = 49) is 39 nM E2 (15 nM when “non-smellers” are excluded). This concentration of E2 is sensitive enough to detect levels of E2 that would be found in dosage forms. This manuscript provides evidence that scent has potential for use in portable yeast biosensors as a read out, particularly for use in technology-limited environments.

Portable and inexpensive analytical tools are required to monitor pharmaceutical quality in technology limited settings including low-and middle-income countries (LMICs). Whole cell yeast biosensors have the potential to help meet this need. However, most of the read-outs for yeast biosensors require expensive equipment or reagents. To overcome this challenge, we have designed a yeast biosensor that produces a unique scent as a readout. This inducible scent biosensor, or "scentsor," does not require the user to administer additional reagents for reporter development and utilizes only the user's nose to be "read." In this manuscript, we describe a scentsor that is responsive to the hormone estradiol (E2). The best estimate threshold (BET) for E2 detection with a panel of human volunteers (n = 49) is 39 nM E2 (15 nM when "non-smellers" are excluded). This concentration of E2 is sensitive enough to detect levels of E2 that would be found in dosage forms. This manuscript provides evidence that scent has potential for use in portable yeast biosensors as a read out, particularly for use in technology-limited environments.

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