Boxwood blight caused by Calonectria pseudonaviculata is typically expressed as a foliage disease with aboveground symptoms including defoliation, dieback and formation of dark narrow stem cankers. Whether this pathogen behaves like other Calonectria spp. and has a significant soil phase in the epidemiology of boxwood blight is not known. In this study we observed experimentally that (1) the boxwood blight pathogen consistently forms microsclerotia in artificially inoculated leaves and roots of Buxus spp., (2) soil artificially inoculated with conidia and microsclerotia of this pathogen can cause foliar blight, (3) conidia and microsclerotia can remain viable in soil for up to 3 and at least 40 weeks, respectively (4) and the pathogen can cause crown and root rot to plants only when roots and crowns are directly exposed to relatively high inoculum levels. Our results suggest that C. pseudonaviculata is primarily a foliar pathogen with a potentially epidemiologically significant soil phase.

Much research has been done regarding how to visualize and interact with observations and attributes of high-dimensional data for exploratory data analysis. From the analyst's perceptual and cognitive perspective, current visualization approaches typically treat the observations of the high-dimensional dataset very differently from the attributes. Often, the attributes are treated as inputs (e.g., sliders), and observations as outputs (e.g., projection plots), thus emphasizing investigation of the observations. However, there are many cases in which analysts wish to investigate both the observations and the attributes of the dataset, suggesting a symmetry between how analysts think about attributes and observations. To address this, we define SIRIUS (Symmetric Interactive Representations In a Unified System), a symmetric, dual projection technique to support exploratory data analysis of high-dimensional data. We provide an example implementation of SIRIUS and demonstrate how this symmetry affords additional insights.

Exploratory data analysis is challenging given the complexity of data. Models find structure in the data lessening the complexity for users. These models have parameters that can be adjusted to explore the data from many different angles providing more ways to learn about the data. "Human in the loop" means users can interact with the parameters to explore alternative structures. This exploration allows for discovery. This paper examines usability issues of Human-Model Interaction (HMI) for data analytics. In particular, we bridge the gaps between a user's intention and the parameters of a WMDS model during HMI communication. CCS Concepts • Human-centered computing → Human computer interaction (HCI) → HCI design and evaluation methods → User studies.

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