Transcriptional profiling reliably discriminates between PBCs from SSc and normal donors despite the fact that they represent a heterogeneous cell population. Multiple biological pathways were differentially regulated in SSc PBCs, but a common thread across these pathways was alterations in protein tyrosine kinase 2beta and mitogen-activated protein kinase signalling. Although the SSc PBC gene expression profile demonstrated some parallels with the lupus interferon gene signature, there was also increased expression of transcripts encoding proteins that target PBCs to the endothelium, which might be relevant to the vasculopathy of SSc.

The extent to which older and younger people do different activities when they are with others and when they are alone is examined in this article. I leverage interpersonal data in combination with information on activities from the American Time Use Survey to shed light on the long held finding that older people have less social contact than younger people. The results show that, net of intervening factors, age is associated with declines in time spent with others for virtually all types of time use. However, the variety of activities that older and younger people do also differs. Using leisure activities to probe this finding reveals that, when older people spend time with others it tends to be during activities that are sui generis social activities—such as attending parties—but that this is not necessarily the case for younger people. The literature on time use and aging is discussed in light of these findings and a new hypothesis on agency in the life course is proposed.

The informR package greatly simplifies the analysis of complex event histories in R by providing user friendly tools to build sufficient statistics for the relevent package. Historically, building sufficient statistics to model event sequences (of the form a / / b) using the egocentric generalization of Butts' (2008) relational event framework for modeling social action has been cumbersome. The informR package simplifies the construction of the complex list of arrays needed by the rem() model fitting for a variety of cases involving egocentric event data, multiple event types, and/or support constraints. This paper introduces these tools using examples from real data extracted from the American Time Use Survey.

This chapter provides an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within the R/statnet platform. We begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We then discuss estimation for dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. Statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac).

The delegation of decision-making capacity from one actor to another-known variously as authority or control-is a central phenomenon of organizational sociology. Despite its theoretical and practical significance, however, the dynamics of control within disrupted settings (such as disasters) remain poorly understood. Here, we shed light on this question by a reexamination of historical data on multiorganizational disaster response networks, using recently developed statistical methods for robust inference from error-prone informant reports. Specifically, we test competing hypotheses about the relationship of control during the response process to the structure of interorganizational communication. We find that both the realized and normative response hierarchies are likely shaped by coordination among both nonadjacent alters and along indirect channels. Our results suggested that the communication structure of these networks is consistent with a control at a distance model of command. This article makes a substantial contribution to understanding the role of network structure in the emergence of control between organizations in disrupted settings. Additionally, our innovative approach to network inference will guide researchers in dealing with error-prone data in their own research on policy networks. 516 0190-292X