Bongshin Lee

Naturally Occurring Dominant Resistance Mutations to Hepatitis C Virus Protease and Polymerase Inhibitors in Treatment-Naïve Patients

Resistance mutations to hepatitis C virus (HCV) nonstructural protein 3 (NS3) protease inhibitors in <1% of the viral quasispecies may still allow >1000‐fold viral load reductions upon treatment, consistent with their reported reduced replicative fitness in vitro. Recently, however, an R155K protease mutation was reported as the dominant quasispecies in a treatment‐naïve individual, raising concerns about possible full drug resistance. To investigate the prevalence of dominant resistance mutations against specifically targeted antiviral therapy for HCV (STAT‐C) in the population, we analyzed HCV genome sequences from 507 treatment‐naïve patients infected with HCV genotype 1 from the United States, Germany, and Switzerland. Phylogenetic sequence analysis and viral load data were used to identify the possible spread of replication‐competent, drug‐resistant viral strains in the population and to infer the consequences of these mutations upon viral replication in vivo. Mutations described to confer resistance to the protease inhibitors Telaprevir, BILN2061, ITMN‐191, SCH6 and Boceprevir; the NS5B polymerase inhibitor AG‐021541; and to the NS4A antagonist ACH‐806 were observed mostly as sporadic, unrelated cases, at frequencies between 0.3% and 2.8% in the population, including two patients with possible multidrug resistance. Collectively, however, 8.6% of the patients infected with genotype 1a and 1.4% of those infected with genotype 1b carried at least one dominant resistance mutation. Viral loads were high in the majority of these patients, suggesting that drug‐resistant viral strains might achieve replication levels comparable to nonresistant viruses in vivo. Conclusion: Naturally occurring dominant STAT‐C resistance mutations are common in treatment‐naïve patients infected with HCV genotype 1. Their influence on treatment outcome should further be characterized to evaluate possible benefits of drug resistance testing for individual tailoring of drug combinations when treatment options are limited due to previous nonresponse to peginterferon and ribavirin. (HEPATOLOGY 2008;48:1769–1778.)

Home automation in the wild: challenges and opportunities

Visions of smart homes have long caught the attention of researchers and considerable effort has been put toward enabling home automation. However, these technologies have not been widely adopted despite being available for over three decades. To gain insight into this state of affairs, we conducted semi-structured home visits to 14 households with home automation. The long term experience, both positive and negative, of the households we interviewed illustrates four barriers that need to be addressed before home automation becomes amenable to broader adoption. These barriers are high cost of ownership, inflexibility, poor manageability, and difficulty achieving security. Our findings also provide several directions for further research, which include eliminating the need for structural changes for installing home automation, providing users with simple security primitives that they can confidently configure, and enabling composition of home devices.

Effectiveness of Animation in Trend Visualization

Animation has been used to show trends in multi-dimensional data. This technique has recently gained new prominence for presentations, most notably with Gapminder Trendalyzer. In Trendalyzer, animation together with interesting data and an engaging presenter helps the audience understand the results of an analysis of the data. It is less clear whether trend animation is effective for analysis. This paper proposes two alternative trend visualizations that use static depictions of trends: one which shows traces of all trends overlaid simultaneously in one display and a second that uses a small multiples display to show the trend traces side-by-side. The paper evaluates the three visualizations for both analysis and presentation. Results indicate that trend animation can be challenging to use even for presentations; while it is the fastest technique for presentation and participants find it enjoyable and exciting, it does lead to many participant errors. Animation is the least effective form for analysis; both static depictions of trends are significantly faster than animation, and the small multiples display is more accurate.

Task taxonomy for graph visualization

Our goal is to define a list of tasks for graph visualization that has enough detail and specificity to be useful to: 1) designers who want to improve their system and 2) to evaluators who want to compare graph visualization systems. In this paper, we suggest a list of tasks we believe are commonly encountered while analyzing graph data. We define graph specific objects and demonstrate how all complex tasks could be seen as a series of low-level tasks performed on those objects. We believe that our taxonomy, associated with benchmark datasets and specific tasks, would help evaluators generalize results collected through a series of controlled experiments.

Research directions in data wrangling: visuatizations and transformations for usable and credible data

In spite of advances in technologies for working with data, analysts still spend an inordinate amount of time diagnosing data quality issues and manipulating data into a usable form. This process of ‘data wrangling’ often constitutes the most tedious and time-consuming aspect of analysis. Though data cleaning and integration arelongstanding issues in the database community, relatively little research has explored how interactive visualization can advance the state of the art. In this article, we review the challenges and opportunities associated with addressing data quality issues. We argue that analysts might more effectively wrangle data through new interactive systems that integrate data verification, transformation, and visualization. We identify a number of outstanding research questions, including how appropriate visual encodings can facilitate apprehension of missing data, discrepant values, and uncertainty; how interactive visualizations might facilitate data transform specification; and how recorded provenance and social interaction might enable wider reuse, verification, and modification of data transformations.

EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers

Machine learning is an increasingly used computational tool within human-computer interaction research. While most researchers currently utilize an iterative approach to refining classifier models and performance, we propose that ensemble classification techniques may be a viable and even preferable alternative. In ensemble learning, algorithms combine multiple classifiers to build one that is superior to its components. In this paper, we present EnsembleMatrix, an interactive visualization system that presents a graphical view of confusion matrices to help users understand relative merits of various classifiers. EnsembleMatrix allows users to directly interact with the visualizations in order to explore and build combination models. We evaluate the efficacy of the system and the approach in a user study. Results show that users are able to quickly combine multiple classifiers operating on multiple feature sets to produce an ensemble classifier with accuracy that approaches best-reported performance classifying images in the CalTech-101 dataset.

Beyond Mouse and Keyboard: Expanding Design Considerations for Information Visualization Interactions

The importance of interaction to Information Visualization (InfoVis) and, in particular, of the interplay between interactivity and cognition is widely recognized [12, 15, 32, 55, 70]. This interplay, combined with the demands from increasingly large and complex datasets, is driving the increased significance of interaction in InfoVis. In parallel, there have been rapid advances in many facets of interaction technologies. However, InfoVis interactions have yet to take full advantage of these new possibilities in interaction technologies, as they largely still employ the traditional desktop, mouse, and keyboard setup of WIMP (Windows, Icons, Menus, and a Pointer) interfaces. In this paper, we reflect more broadly about the role of more “natural” interactions for InfoVis and provide opportunities for future research. We discuss and relate general HCI interaction models to existing InfoVis interaction classifications by looking at interactions from a novel angle, taking into account the entire spectrum of interactions. Our discussion of InfoVis-specific interaction design considerations helps us identify a series of underexplored attributes of interaction that can lead to new, more “natural,” interaction techniques for InfoVis.

Understanding research trends in conferences using paperLens

PaperLens is a novel visualization that reveals trends, connections, and activity throughout a conference community. It tightly couples views across papers, authors, and references. PaperLens was developed to visualize 8 years (1995-2002) of InfoVis conference proceedings and was then extended to visualize 23 years (1982-2004) of the CHI conference proceedings. This paper describes how we analyzed the data and designed PaperLens. We also describe a user study to focus our redesign efforts along with the design changes we made to address usability issues. We summarize lessons learned in the process of design and scaling up to the larger set of CHI conference papers.

SketchStory: Telling More Engaging Stories with Data through Freeform Sketching

Presenting and communicating insights to an audience-telling a story-is one of the main goals of data exploration. Even though visualization as a storytelling medium has recently begun to gain attention, storytelling is still underexplored in information visualization and little research has been done to help people tell their stories with data. To create a new, more engaging form of storytelling with data, we leverage and extend the narrative storytelling attributes of whiteboard animation with pen and touch interactions. We present SketchStory, a data-enabled digital whiteboard that facilitates the creation of personalized and expressive data charts quickly and easily. SketchStory recognizes a small set of sketch gestures for chart invocation, and automatically completes charts by synthesizing the visuals from the presenter-provided example icon and binding them to the underlying data. Furthermore, SketchStory allows the presenter to move and resize the completed data charts with touch, and filter the underlying data to facilitate interactive exploration. We conducted a controlled experiment for both audiences and presenters to compare SketchStory with a traditional presentation system, Microsoft PowerPoint. Results show that the audience is more engaged by presentations done with SketchStory than PowerPoint. Eighteen out of 24 audience participants preferred SketchStory to PowerPoint. Four out of five presenter participants also favored SketchStory despite the extra effort required for presentation.

A Comparison of User-Generated and Automatic Graph Layouts

The research presented in this paper compares user-generated and automatic graph layouts. Following the methods suggested by van Ham et al. (2008), a group of users generated graph layouts using both multi-touch interaction on a tabletop display and mouse interaction on a desktop computer. Users were asked to optimize their layout for aesthetics and analytical tasks with a social network. We discuss characteristics of the user-generated layouts and interaction methods employed by users in this process. We then report on a web-based study to compare these layouts with the output of popular automatic layout algorithms. Our results demonstrate that the best of the user-generated layouts performed as well as or better than the physics-based layout. Orthogonal and circular automatic layouts were found to be considerably less effective than either the physics-based layout or the best of the user-generated layouts. We highlight several attributes of the various layouts that led to high accuracy and improved task completion time, as well as aspects in which traditional automatic layout methods were unsuccessful for our tasks.