A measure of local uniqueness to identify linchpins in a social network with node attributes.

Network centrality measures assign importance to influential or key nodes in a network based on the topological structure of the underlying adjacency matrix. In this work, we define the importance of a node in a network as being dependent on whether it is the only one of its kind among its neighbors' ties. We introduce linchpin score, a measure of local uniqueness used to identify important nodes by assessing both network structure and a node attribute. We explore linchpin score by attribute type and examine relationships between linchpin score and other established network centrality measures (degree, betweenness, closeness, and eigenvector centrality). To assess the utility of this measure in a real-world application, we measured the linchpin score of physicians in patient-sharing networks to identify and characterize important physicians based on being locally unique for their specialty. We hypothesized that linchpin score would identify indispensable physicians who would not be easily replaced by another physician of their specialty type if they were to be removed from the network. We explored differences in rural and urban physicians by linchpin score compared with other network centrality measures in patient-sharing networks representing the 306 hospital referral regions in the United States. We show that linchpin score is uniquely able to make the distinction that rural specialists, but not rural general practitioners, are indispensable for rural patient care. Linchpin score reveals a novel aspect of network importance that can provide important insight into the vulnerability of health care provider networks. More broadly, applications of linchpin score may be relevant for the analysis of social networks where interdisciplinary collaboration is important.

Iterative screen optimization maximizes the efficiency of macromolecular crystallization.

Advances in X-ray crystallography have streamlined the process of determining high-resolution three-dimensional macromolecular structures. However, a rate-limiting step in this process continues to be the generation of crystals that are of sufficient size and quality for subsequent diffraction experiments. Here, iterative screen optimization (ISO), a highly automated process in which the precipitant concentrations of each condition in a crystallization screen are modified based on the results of a prior crystallization experiment, is described. After designing a novel high-throughput crystallization screen to take full advantage of this method, the value of ISO is demonstrated by using it to successfully crystallize a panel of six diverse proteins. The results suggest that ISO is an effective method to obtain macromolecular crystals, particularly for proteins that crystallize under a narrow range of precipitant concentrations.