BackgroundHealth information is increasingly being digitally stored and exchanged. The public is regularly collecting and storing health-related data on their own electronic devices and in the cloud. Diabetes prevention is an increasingly important preventive health measure, and diet and exercise are key components of this. Patients are turning to online programs to help them lose weight. Despite primary care physicians being important in patients’ weight loss success, there is no exchange of information between the primary care provider (PCP) and these online weight loss programs. There is an emerging opportunity to integrate this data directly into the electronic health record (EHR), but little is known about what information to share or how to share it most effectively. This study aims to characterize the preferences of providers concerning the integration of externally generated lifestyle modification data into a primary care EHR workflow.MethodsWe performed a qualitative study using two rounds of semi-structured interviews with primary care providers. We used an iterative design process involving primary care providers, health information technology software developers and health services researchers to develop the interface.ResultsUsing grounded-theory thematic analysis 4 themes emerged from the interviews: 1) barriers to establishing healthy lifestyles, 2) features of a lifestyle modification program, 3) reporting of outcomes to the primary care provider, and 4) integration with primary care. These themes guided the rapid-cycle agile design process of an interface of data from an online diabetes prevention program into the primary care EHR workflow.ConclusionsThe integration of external health-related data into the EHR must be embedded into the provider workflow in order to be useful to the provider and beneficial for the patient. Accomplishing this requires evaluation of that clinical workflow during software design. The development of this novel interface used rapid cycle iterative design, early involvement by providers, and usability testing methodology. This provides a framework for how to integrate external data into provider workflow in efficient and effective ways. There is now the potential to realize the importance of having this data available in the clinical setting for patient engagement and health outcomes.

ColE1 plasmid replication is unidirectional and requires two DNA polymerases: DNA polymerase I (Pol I) and DNA polymerase III (Pol III). Pol I initiates leading-strand synthesis by extending an RNA primer, allowing the Pol III holoenzyme to assemble and to finish replication of both strands. The goal of the present work is to study the interplay between Pol I and Pol III during ColE1 plasmid replication, in order to gain new insights into Pol I function in vivo. Our approach consists of using mutations generated by a low fidelity mutant of Pol I (LF-Pol I) during replication of a ColE1 plasmid as a footprint for Pol I replication. This approach allowed mapping areas of Pol I replication on the plasmid with high resolution. In addition, we were able to approximate the strandedness of Pol I mutations throughout the plasmid, allowing us to estimate the spectrum of the LF-Pol I in vivo. Our study produced the following three mechanistic insights: 1) we identified the likely location of the polymerase switch at ~200 bp downstream of replication initiation; 2) we found evidence suggesting that Pol I can replicate both strands, supporting earlier studies indicating a functional redundancy between Pol I and Pol III 3) we found evidence pointing to a specific role of Pol I during termination of lagging-strand replication. In addition, we illustrate how our strand-specific footprinting approach can be used to dissect factors modulating Pol I fidelity in vivo.

Traditionally, practitioners initialize the k-means algorithm with centers chosen uniformly at random. Randomized initialization with uneven weights (k-means++) has recently been used to improve the performance over this strategy in cost and run-time. We consider the k-means problem with semi-supervised information, where some of the data are pre-labeled, and we seek to label the rest according to the minimum cost solution. By extending the k-means++ algorithm and analysis to account for the labels, we derive an improved theoretical bound on expected cost and observe improved performance in simulated and real data examples. This analysis provides theoretical justification for a roughly linear semi-supervised clustering algorithm.

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