The ability to exit host cells at the end of their developmental growth is a critical step for the intracellular bacterium Chlamydia. One exit strategy, extrusion, is mediated by host signaling pathways involved with actin polymerization. Here, we show that actin is recruited to the chlamydial inclusion as a late event, occurring after 20 hours post-infection (hpi) and only within a subpopulation of cells. This event increases significantly in prevalence and extent from 20 to 68 hpi, and actin coats strongly correlated with extrusions. In contrast to what has been reported for other intracellular pathogens, actin nucleation on Chlamydia inclusions did not ‘flash’, but rather exhibited moderate depolymerization dynamics. By using small molecule agents to selectively disrupt host signaling pathways involved with actin nucleation, modulate actin polymerization dynamics and also to disable the synthesis and secretion of chlamydial proteins, we further show that host and bacterial proteins are required for actin coat formation. Transient disruption of either host or bacterial signaling pathways resulted in rapid loss of coats in all infected cells and a reduction in extrusion formation. Inhibition of Chlamydia type III secretion also resulted in rapid loss of actin association on inclusions, thus implicating chlamydial effector proteins(s) as being central factors for engaging with host actin nucleating factors, such as formins. In conclusion, our data illuminate the host and bacterial driven process by which a dense actin matrix is dynamically nucleated and maintained on the Chlamydia inclusion. This late stage event is not ubiquitous for all infected cells in a population, and escalates in prevalence and extent throughout the developmental cycle of Chlamydia, culminating with their exit from the host cell by extrusion. The initiation of actin recruitment by Chlamydia appears to be novel, and may serve as an upstream determinant of the extrusion mechanism.

Local learning methods, such as local linear regression and nearest neighbor classifiers, base estimates on nearby training samples, neighbors. Usually, the number of neighbors used in estimation is fixed to be a global "optimal" value, chosen by cross validation. This paper proposes adapting the number of neighbors used for estimation to the local geometry of the data, without need for cross validation. The term enclosing neighborhood is introduced to describe a set of neighbors whose convex hull contains the test point when possible. It is proven that enclosing neighborhoods yield bounded estimation variance under some assumptions. Three such enclosing neighborhood definitions are presented: natural neighbors, natural neighbors inclusive, and enclosing k-NN. The effectiveness of these neighborhood definitions with local linear regression is tested for estimating lookup tables for color management. Significant improvements in error metrics are shown, indicating that enclosing neighborhoods may be a promising adaptive neighborhood definition for other local learning tasks as well, depending on the density of training samples.

A popular color management standard for controlling color reproduction is the ICC color profile. The core of the ICC profile is a look-up-table which maps a regular grid of deviceindependent colors to the printer colorspace. To estimate the look-up-table from sample input-output colors, local linear regression has been shown to work better than other methods. An open problem in local linear regression is how to define the locality or neighborhood for each of the local linear regressions. In this paper, new adaptive neighborhood definitions and regularized local linear regression are proposed to address this problem. The adaptive neighborhood definitions enclose the test sample, and are motivated by a result showing they yield bounded estimation variance. An experiment shows that both regularization and the proposed neighborhoods can lead to a significant reduction in error.