Objective:To define the natural history of X-linked myotubular myopathy (MTM).Methods:We performed a cross-sectional study that included an online survey (n = 35) and a prospective, 1-year longitudinal investigation using a phone survey (n = 33).Results:We ascertained data from 50 male patients with MTM and performed longitudinal assessments on 33 affected individuals. Consistent with existing knowledge, we found that MTM is a disorder associated with extensive morbidities, including wheelchair (86.7% nonambulant) and ventilator (75% requiring >16 hours of support) dependence. However, unlike previous reports and despite the high burden of disease, mortality was lower than anticipated (approximate rate 10%/y). Seventy-six percent of patients with MTM enrolled (mean age 10 years 11 months) were alive at the end of the study. Nearly all deaths in the study were associated with respiratory failure. In addition, the disease course was more stable than expected, with few adverse events reported during the prospective survey. Few non–muscle-related morbidities were identified, although an unexpectedly high incidence of learning disability (43%) was noted. Conversely, MTM was associated with substantial burdens on patient and caregiver daily living, reflected by missed days of school and lost workdays.Conclusions:MTM is one of the most severe neuromuscular disorders, with affected individuals requiring extensive mechanical interventions for survival. However, among study participants, the disease course was more stable than predicted, with more individuals surviving infancy and early childhood. These data reflect the disease burden of MTM but offer hope in terms of future therapeutic intervention.
During asynchronous collaborative analysis, handoff of partial findings is challenging because externalizations produced by analysts may not adequately communicate their investigative process. To address this challenge, we developed techniques to automatically capture and help encode tacit aspects of the investigative process based on an analyst's interactions, and streamline explicit authoring of handoff annotations. We designed our techniques to mediate awareness of analysis coverage, support explicit communication of progress and uncertainty with annotation, and implicit communication through playback of investigation histories. To evaluate our techniques, we developed an interactive visual analysis system, KTGraph, that supports an asynchronous investigative document analysis task. We conducted a two-phase user study to characterize a set of handoff strategies and to compare investigative performance with and without our techniques. The results suggest that our techniques promote the use of more effective handoff strategies, help increase an awareness of prior investigative process and insights, as well as improve final investigative outcomes.
International audienceThe egocentric analysis of dynamic networks focuses on discovering the temporal patterns of a subnetwork around a specific central actor (i.e., an ego-network). These types of analyses are useful in many application domains, such as social science and business intelligence, providing insights about how the central actor interacts with the outside world. We present EgoLines, an interactive visualization to support the egocentric analysis of dynamic networks. Using a "subway map" metaphor, a user can trace an individual actor over the evolution of the ego-network. The design of EgoLines is grounded in a set of key analytical questions pertinent to egocentric analysis, derived from our interviews with three domain experts and general network analysis tasks. We demonstrate the effectiveness of EgoLines in egocentric analysis tasks through a controlled experiment with 18 participants and a use-case developed with a domain expert
User-authored annotations of data can support analysts in the activity of hypothesis generation and sensemaking, where it is not only critical to document key observations, but also to communicate insights between analysts. We present annotation graphs, a dynamic graph visualization that enables meta-analysis of data based on user-authored annotations. The annotation graph topology encodes annotation semantics, which describe the content of and relations between data selections, comments, and tags. We present a mixed-initiative approach to graph layout that integrates an analyst's manual manipulations with an automatic method based on similarity inferred from the annotation semantics. Various visual graph layout styles reveal different perspectives on the annotation semantics. Annotation graphs are implemented within C8, a system that supports authoring annotations during exploratory analysis of a dataset. We apply principles of Exploratory Sequential Data Analysis (ESDA) in designing C8, and further link these to an existing task typology in the visualization literature. We develop and evaluate the system through an iterative user-centered design process with three experts, situated in the domain of analyzing HCI experiment data. The results suggest that annotation graphs are effective as a method of visually extending user-authored annotations to data meta-analysis for discovery and organization of ideas.
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