We describe how current radiological best practices are predicated on a sophisticated technological ecosystem usually comprised of multiple large-scale displays, and integrated record keeping and communication systems driven by high-speed networks. At the same time, current development of low-cost ultrasound (U/S) devices for low-resource settings trends towards small-scale, independent devices with palm-sized screens. We reviewed existing literature, analyzed findings from two years of fieldwork in Uganda, and conducted an interview study with clinicians about radiology work practices to determine which patterns and technologies contribute to the efficacy of ultrasound. We use these findings to inform how ultrasound technology in low-resource settings can most usefully be developed and deployed. In addition, findings are relevant for creating medical technologies for low-resource environments generally, as we make clear the importance of considering not just technology development aspects like power consumption and interface, but also larger technology and work ecosystems.

Ultrasound imaging is an effective tool for identifying maternal mortality risk factors. However, the high cost of ultrasound devices and the scarcity of ultrasound training are two major barriers to adoption in the developing world; complicated and expensive medical technologies are unlikely to meet the needs of users with limited opportunities for formal training and continuing education. To address these particular barriers, we have designed an inexpensive ultrasound system composed of off-the-shelf hardware and custom software. Our system is designed for use by rural midwives---often central medical figures in resource-constrained communities. This paper presents our work integrating help and tutorial software into the ultrasound system we developed. Our goal is to supplement a midwife's conceptual and operational knowledge of diagnostic ultrasound through appropriate and adaptive scaffolding. The design of our help system is informed by interviews with sonographers and radiologists in the United States and Uganda, and data collected during two fieldwork visits to Uganda. Through our fieldwork and preliminary evaluations, we have found that in addition to in-context reference material accessible during an exam, midwives need in-depth learning materials that can be accessed outside of a medical exam scenario.

SUMMARY: Measuring the consequences of mutation in proteins is critical to understanding their function. These measurements are essential in such applications as protein engineering, drug development, protein design and genome sequence analysis. Recently, high-throughput sequencing has been coupled to assays of protein activity, enabling the analysis of large numbers of mutations in parallel. We present Enrich, a tool for analyzing such deep mutational scanning data. Enrich identifies all unique variants (mutants) of a protein in high-throughput sequencing datasets and can correct for sequencing errors using overlapping paired-end reads. Enrich uses the frequency of each variant before and after selection to calculate an enrichment ratio, which is used to estimate fitness. Enrich provides an interactive interface to guide users. It generates user-accessible output for downstream analyses as well as several visualizations of the effects of mutation on function, thereby allowing the user to rapidly quantify and comprehend sequence-function relationships. AVAILABILITY AND IMPLEMENTATION: Enrich is implemented in Python and is available under a FreeBSD license at http://depts.washington.edu/sfields/software/enrich/. Enrich includes detailed documentation as well as a small example dataset. CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.