Addressing Barriers to the Development and Adoption of Rapid Diagnostic Tests in Global Health

Immunochromatographic rapid diagnostic tests (RDTs) have demonstrated significant potential for use as point-of-care diagnostic tests in resource-limited settings. Most notably, RDTs for malaria have reached an unparalleled level of technological maturity and market penetration, and are now considered an important complement to standard microscopic methods of malaria diagnosis. However, the technical development of RDTs for other infectious diseases, and their uptake within the global health community as a core diagnostic modality, has been hindered by a number of extant challenges. These range from technical and biological issues, such as the need for better affinity agents and biomarkers of disease, to social, infrastructural, regulatory and economic barriers, which have all served to slow their adoption and diminish their impact. In order for the immunochromatographic RDT format to be successfully adapted to other disease targets, to see widespread distribution, and to improve clinical outcomes for patients on a global scale, these challenges must be identified and addressed, and the global health community must be engaged in championing the broader use of RDTs

Assessing Demand for Transparency in Intelligent Systems Using Machine Learning

Intelligent systems offering decision support can lessen cognitive load and improve the efficiency of decision making in a variety of contexts. These systems assist users by evaluating multiple courses of action and recommending the right action at the right time. Modern intelligent systems using machine learning introduce new capabilities in decision support, but they can come at a cost. Machine learning models provide little explanation of their outputs or reasoning process, making it difficult to determine when it is appropriate to trust, or if not, what went wrong. In order to improve trust and ensure appropriate reliance on these systems, users must be afforded increased transparency, enabling an understanding of the systems reasoning, and an explanation of its predictions or classifications. Here we discuss the salient factors in designing transparent intelligent systems using machine learning, and present the results of a user-centered design study. We propose design guidelines derived from our study, and discuss next steps for designing for intelligent system transparency

Assessing the Value of Transparency in Recommender Systems: An End-User Perspective

Recommender systems, especially those built on machine learning, are increasing in popularity, as well as complexity and scope. Systems that cannot explain their reasoning to end-users risk losing trust with users and failing to achieve acceptance. Users demand interfaces that afford them insights into internal workings, allowing them to build appropriate mental models and calibrated trust. Building interfaces that provide this level of transparency, however, is a significant design challenge, with many design features that compete, and little empirical research to guide implementation. We investigated how end-users of recommender systems value different categories of information to help in determining what to do with computer-generated recommendations in contexts involving high risk to themselves or others. Findings will inform future design of decision support in high-criticality contexts

The Outer Limits of Galaxy Clusters: Observations to the Virial Radius with Suzaku, XMM, and Chandra

The outskirts of galaxy clusters, near the virial radius, remain relatively unexplored territory and yet are vital to our understanding of cluster growth, structure, and mass. In this presentation, we show the first results from a program to constrain the state of the outer intracluster medium (ICM) in a large sample of galaxy clusters, exploiting the strengths of three complementary X-ray observatories: Suzaku (low, stable background), XMM-Newton (high sensitivity), and Chandra (good spatial resolution). By carefully combining observations from the cluster core to beyond r_200, we are able to identify and reduce systematic uncertainties that would impede our spatial and spectral analysis using a single telescope. Our sample comprises nine clusters at z ~ 0.1-0.2 fully covered in azimuth to beyond r_200, and our analysis indicates that the ICM is not in hydrostatic equilibrium in the cluster outskirts, where we see clear azimuthal variations in temperature and surface brightness. In one of the clusters, we are able to measure the diffuse X-ray emission well beyond r_200, and we find that the entropy profile and the gas fraction are consistent with expectations from theory and numerical simulations. These results stand in contrast to recent studies which point to gas clumping in the outskirts; the extent to which differences of cluster environment or instrumental effects factor in this difference remains unclear. From a broader perspective, this project will produce a sizeable fiducial data set for detailed comparison with high-resolution numerical simulations.Comment: 8 pages, 6 figures. To appear in the proceedings of the Suzaku 2011 Conference, "Exploring the X-ray Universe: Suzaku and Beyond.