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Abstract SummaryNew advances in single-cell multi-omics experiments have allowed biologists to examine how various biological factors regulate processes in concert on the cellular level. However, measuring multiple cellular features for a single cell can be quite resource-intensive or impossible with the current technology. By using optimal transport (OT) to align cells and features across disparate datasets produced by separate assays, Single Cell alignment using Optimal Transport + (SCOT+), our unsupervised single-cell alignment software suite, allows biologists to align their data without the need for any correspondence. SCOT+ implements a generic optimal transport solution that can be reduced to multiple different previously studied OT optimization procedures including SCOT, SCOTv2, SCOOTR, and AGW for single cell, each of which provides state-of-the-art single-cell alignment performance. Outside of giving a unified framework to interact with prior formulations, the generality of SCOT+ optimization naturally gives rise to a new OT loss, Unbalanced Augmented Gromov-Wasserstein (UAGW), and a corresponding optimizer. With our user-friendly website and tutorials, this new package will help improve biological analyses by allowing for more accurate downstream analyses on multi-omics single-cell measurements. Implementation and AvailabilityOur algorithm is implemented in Pytorch and available on PyPI and GitHub (https://github.com/scotplus/scotplus). Additionally, we have many tutorials available in a separate GitHub repository (https://github.com/scotplus/book_source) and on our website (https://scotplus.github.io/). 

Urban environments may alter the landscape of disease transmission with implications for control. Yet, it is unclear whether urban-rural differences exist in the dynamics of childhood respiratory diseases, given specific mixing patterns in younger age groups. Here, we leverage county-level data on respiratory syncytial virus (RSV) from the United States to reveal an urban-rural gradient in both the intensity and age structure of the RSV epidemic, where urban locations experience more prolonged epidemics with higher burden in infants (under 1 year of age). We develop a mechanistic epidemiological model to show that these differences can be explained by daycare utilization rates in children under 5. Using our model to consider control measures, we find that expanding seasonal immunization access in urban and rural areas may limit the risk of off season RSV epidemics. 

Web-browsing histories, online newspapers, streaming music, and stock prices all show that we live in an age of data. Extracting meaning from data is necessary in many fields to comprehend the information flow. This need has fueled rapid growth in data science education aiming to serve the next generation of policy makers, data science researchers, and global citizens. Initially, teaching practices have been drawn from data science's parent disciplines (e.g., computer science and mathematics). This project addresses the early stages of developing a concept inventory of student difficulty within the newly emerging field of data science. In particular this project will address three primary research objectives: (1) identify student misconceptions in data science courses; (2) document students’ prior knowledge and identify courses that teach early data science concepts; and (3) confirm expert identification of data science concepts, and their importance for introductory-level data science curricula. During the first year of this grant, we have collected approximately 200 responses for a survey to confirm concepts from an existing body of knowledge presented by the Edison Project. Survey respondents are comprised of faculty and industry practitioners within data science and closely related fields. Preliminary analysis of these results will be presented with respect to our third research objective. In addition, we developed and launched a pilot assessment for identifying student difficulties within data science courses. The protocol includes regular responses to reflective questions by faculty, teaching assistants, and students from selected data science courses offered at the three participating institutions. Preliminary analyses will be presented along with implications for future data collection in year two of the project. In addition to the anticipated results, we expect that the data collection and analysis methodologies will be of interest to many scholars who have or will engage in discipline-based educational research.