bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dysregulated growth hormone‐insulin‐like growth factor‐1 axis in adult type 1 diabetes with long duration
Authors: Gutefeldt Kerstin , Hedman Christina A. , Thyberg Ingrid S.M. , Bachrach‐Lindström Margareta , Spångeus Anna , Arnqvist Hans J. 
Published: July 2018

SummaryContextIn type 1 diabetes (T1D), dysregulation of the GH‐IGF‐1 axis has been reported. Whether this is related to upper extremity impairments (UEI) is unknown.ObjectiveExamine differences in GH‐IGF‐1 axis between T1D on subcutaneous insulin treatment and matched controls without diabetes and possible associations between GH‐IGF‐1 axis and UEI.DesignCross‐sectional population‐based study. Patients with T1D, onset <35 years, duration ≥ 20 years, <67 years old and controls were invited to answer questionnaires and take blood samples.SubjectsA total of 605 patients with T1D and 533 controls accepted to participate.OutcomesFasting levels of IGF‐1, IGF‐1 Z‐score, IGFBP‐1, IGFBP‐3, C‐peptide, GH and UEI.ResultsPatients with T1D had lower IGF‐1 and IGFBP‐3 and higher IGFBP‐1 and GH than controls. The difference in IGF‐1 persisted with age. Insulin dose was associated with increasing IGF‐1 Z‐score but even at a very high insulin dose (>1U/kg) IGF‐1 Z‐score was subnormal compared to controls. IGF‐1 Z‐score was unaffected by glycaemic control (HbA1c) but increased with residual insulin secretion, (C‐peptide 1‐99 pmol/L). IGFBP‐1 was associated with fasting blood glucose, negatively in controls and positively in patients with T1D probably reflecting insulin resistance and insulin deficiency, respectively. There was no association between lower IGF‐1 Z‐score and UEI in T1D.ConclusionIn adult T1D with fair glycaemic control, the GH‐IGF‐1 axis is dysregulated exhibiting GH resistance, low IGF‐1 and elevated IGFBP‐1. Subcutaneous insulin cannot normalize these changes while endogenous insulin secretion has marked effects on IGF‐1 pointing to a role of portal insulin.
Representative Consensus from Limited‐Size Ensembles
Authors: Mirzargar Mahsa , Whitaker Ross T. 
Published: July 2018

AbstractCharacterizing the uncertainty and extracting reliable visual information from ensemble data have been persistent challenges in various disciplines, specifically in simulation sciences. Many ensemble analysis and visualization techniques take a probabilistic approach to this problem with the assumption that the ensemble size is large enough to extract reliable statistical or probabilistic summaries. However, many real‐life ensembles are rather limited in size, with only a handful of members, due to various restrictions such as storage, computational power, or sampling limitations. As a result, probabilistic inference is subject to imprecision and can potentially result in untrustworthy information in the presence of a limited sample‐size ensemble. In this case, a more reliable approach is to fuse the information present in an ensemble with a limited number of members with minimal assumptions. In this paper, we propose a technique to construct a representative consensus that is particularly suited for ensembles of a relatively small size. The proposed technique casts the problem as an ordering problem in which at each point in the domain, the ensemble members are ranked based on the local neighborhood. This local approach allows us to provide shape and irregularity sensitivity. The local order statistics will then be fused to construct a global consensus using a Bayesian approach to ensure spatial coherency of the local information. We demonstrate the utility of the proposed technique using a synthetic and two real‐life examples.
Key Time Steps Selection for Large‐Scale Time‐Varying Volume Datasets Using an Information‐Theoretic Storyboard
Authors: Zhou Bo , Chiang Yi‐Jen 
Published: July 2018

AbstractKey time steps selection is essential for effective and efficient scientific visualization of large‐scale time‐varying datasets. We present a novel approach that can decide the number of most representative time steps while selecting them to minimize the difference in the amount of information from the original data. We use linear interpolation to reconstruct the data of intermediate time steps between selected time steps. We propose an evaluation of selected time steps by computing the difference in the amount of information (called information difference) using variation of information (VI) from information theory, which compares the interpolated time steps against the original data. In the one‐time preprocessing phase, a dynamic programming is applied to extract the subset of time steps that minimize the information difference. In the run‐time phase, a novel chart is used to present the dynamic programming results, which serves as a storyboard of the data to guide the user to select the best time steps very efficiently. We extend our preprocessing approach to a novel out‐of‐core approximate algorithm to achieve optimal I/O cost, which also greatly reduces the in‐core computing time and exhibits a nice trade‐off between computing speed and accuracy. As shown in the experiments, our approximate method outperforms the previous globally optimal DTW approach [TLS12] on out‐of‐core data by significantly improving the running time while keeping similar qualities, and is our major contribution.
Chart Constellations: Effective Chart Summarization for Collaborative and Multi‐User Analyses
Authors: Xu Shenyu , Bryan Chris , Li Jianping Kelvin , Zhao Jian , Ma Kwan‐Liu 
Published: July 2018

AbstractMany data problems in the real world are complex and require multiple analysts working together to uncover embedded insights by creating chart‐driven data stories. How, as a subsequent analysis step, do we interpret and learn from these collections of charts? We present Chart Constellations, a system to interactively support a single analyst in the review and analysis of data stories created by other collaborative analysts. Instead of iterating through the individual charts for each data story, the analyst can project, cluster, filter, and connect results from all users in a meta‐visualization approach. Constellations supports deriving summary insights about prior investigations and supports the exploration of new, unexplored regions in the dataset. To evaluate our system, we conduct a user study comparing it against data science notebooks. Results suggest that Constellations promotes the discovery of both broad and high‐level insights, including theme and trend analysis, subjective evaluation, and hypothesis generation.
Exploring the Visualization Design Space with Repertory Grids
Authors: Kurzhals Kuno , Weiskopf Daniel 
Published: July 2018

AbstractThere is an ongoing discussion in the visualization community about the relevant factors that render a visualization effective, expressive, memorable, aesthetically pleasing, etc. These factors lead to a large design space for visualizations. To explore this design space, qualitative research methods based on observations and interviews are often necessary. We describe an interview method that allows us to systematically acquire and assess important factors from subjective answers by interviewees. To this end, we adopt the repertory grid methodology in the context of visualization. It is based on the personal construct theory: each personality interprets a topic based on a set of personal, basic constructs expressed as contrasts. For the individual interpretation of visualizations, this means that these personal terms can be very different, depending on numerous influences, such as the prior experiences of the interviewed person. We present an interviewing process, visual interface, and qualitative and quantitative analysis procedures that are specifically devised to fit the needs of visualization applications. A showcase interview with 15 typical static information visualizations and 10 participants demonstrates that our approach is effective in identifying common constructs as well as individual differences. In particular, we investigate differences between expert and nonexpert interviewees. Finally, we discuss the differences to other qualitative methods and how the repertory grid can be embedded in existing theoretical frameworks of visualization research for the design process.
The Perception of Graph Properties in Graph Layouts
Authors: Soni Utkarsh , Lu Yafeng , Hansen Brett , Purchase Helen C. , Kobourov Stephen , Maciejewski Ross 
Published: July 2018

AbstractWhen looking at drawings of graphs, questions about graph density, community structures, local clustering and other graph properties may be of critical importance for analysis. While graph layout algorithms have focused on minimizing edge crossing, symmetry, and other such layout properties, there is not much known about how these algorithms relate to a user's ability to perceive graph properties for a given graph layout. In this study, we apply previously established methodologies for perceptual analysis to identify which graph drawing layout will help the user best perceive a particular graph property. We conduct a large scale (n = 588) crowdsourced experiment to investigate whether the perception of two graph properties (graph density and average local clustering coefficient) can be modeled using Weber's law. We study three graph layout algorithms from three representative classes (Force Directed ‐ FD, Circular, and Multi‐Dimensional Scaling ‐ MDS), and the results of this experiment establish the precision of judgment for these graph layouts and properties. Our findings demonstrate that the perception of graph density can be modeled with Weber's law. Furthermore, the perception of the average clustering coefficient can be modeled as an inverse of Weber's law, and the MDS layout showed a significantly different precision of judgment than the FD layout.
Bladder Runner: Visual Analytics for the Exploration of RT‐Induced Bladder Toxicity in a Cohort Study
Authors: Raidou R.G. , Casares‐Magaz O. , Amirkhanov A. , Moiseenko V. , Muren L.P. , Einck J.P. , Vilanova A. , Gröller M.E. 
Published: July 2018

AbstractWe present the Bladder Runner, a novel tool to enable detailed visual exploration and analysis of the impact of bladder shape variation on the accuracy of dose delivery, during the course of prostate cancer radiotherapy (RT). Our tool enables the investigation of individual patients and cohorts through the entire treatment process, and it can give indications of RT‐induced complications for the patient. In prostate cancer RT treatment, despite the design of an initial plan prior to dose administration, bladder toxicity remains very common. The main reason is that the dose is delivered in multiple fractions over a period of weeks, during which, the anatomical variation of the bladder – due to differences in urinary filling – causes deviations between planned and delivered doses. Clinical researchers want to correlate bladder shape variations to dose deviations and toxicity risk through cohort studies, to understand which specific bladder shape characteristics are more prone to side effects. This is currently done with Dose‐Volume Histograms (DVHs), which provide limited, qualitative insight. The effect of bladder variation on dose delivery and the resulting toxicity cannot be currently examined with the DVHs. To address this need, we designed and implemented the Bladder Runner, which incorporates visualization strategies in a highly interactive environment with multiple linked views. Individual patients can be explored and analyzed through the entire treatment period, while inter‐patient and temporal exploration, analysis and comparison are also supported. We demonstrate the applicability of our presented tool with a usage scenario, employing a dataset of 29 patients followed through the course of the treatment, across 13 time points. We conducted an evaluation with three clinical researchers working on the investigation of RT‐induced bladder toxicity. All participants agreed that Bladder Runner provides better understanding and new opportunities for the exploration and analysis of the involved cohort data.
Track Xplorer: A System for Visual Analysis of Sensor‐based Motor Activity Predictions
Authors: Cavallo Marco , Demiralp Çağatay 
Published: July 2018

AbstractWith the rapid commoditization of wearable sensors, detecting human movements from sensor datasets has become increasingly common over a wide range of applications. To detect activities, data scientists iteratively experiment with different classifiers before deciding which model to deploy. Effective reasoning about and comparison of alternative classifiers are crucial in successful model development. This is, however, inherently difficult in developing classifiers for sensor data, where the intricacy of long temporal sequences, high prediction frequency, and imprecise labeling make standard evaluation methods relatively ineffective and even misleading.We introduce Track Xplorer, an interactive visualization system to query, analyze, and compare the predictions of sensor‐data classifiers. Track Xplorer enables users to interactively explore and compare the results of different classifiers, and assess their accuracy with respect to the ground‐truth labels and video. Through integration with a version control system, Track Xplorer supports tracking of models and their parameters without additional workload on model developers. Track Xplorer also contributes an extensible algebra over track representations to filter, compose, and compare classification outputs, enabling users to reason effectively about classifier performance. We apply Track Xplorer in a collaborative project to develop classifiers to detect movements from multisensor data gathered from Parkinson's disease patients. We demonstrate how Track Xplorer helps identify early on possible systemic data errors, effectively track and compare the results of different classifiers, and reason about and pinpoint the causes of misclassifications.
ThreadReconstructor: Modeling Reply‐Chains to Untangle Conversational Text through Visual Analytics
Authors: El‐Assady Mennatallah , Sevastjanova Rita , Keim Daniel , Collins Christopher 
Published: July 2018

AbstractWe present ThreadReconstructor, a visual analytics approach for detecting and analyzing the implicit conversational structure of discussions, e.g., in political debates and forums. Our work is motivated by the need to reveal and understand single threads in massive online conversations and verbatim text transcripts. We combine supervised and unsupervised machine learning models to generate a basic structure that is enriched by user‐defined queries and rule‐based heuristics. Depending on the data and tasks, users can modify and create various reconstruction models that are presented and compared in the visualization interface. Our tool enables the exploration of the generated threaded structures and the analysis of the untangled reply‐chains, comparing different models and their agreement. To understand the inner‐workings of the models, we visualize their decision spaces, including all considered candidate relations. In addition to a quantitative evaluation, we report qualitative feedback from an expert user study with four forum moderators and one machine learning expert, showing the effectiveness of our approach.
A General Illumination Model for Molecular Visualization
Authors: Hermosilla P. , Vázquez P. , Vinacua A. , Ropinski T. 
Published: July 2018

AbstractSeveral visual representations have been developed over the years to visualize molecular structures, and to enable a better understanding of their underlying chemical processes. Today, the most frequently used atom‐based representations are the Space‐filling, the Solvent Excluded Surface, the Balls‐and‐Sticks, and the Licorice models. While each of these representations has its individual benefits, when applied to large‐scale models spatial arrangements can be difficult to interpret when employing current visualization techniques. In the past it has been shown that global illumination techniques improve the perception of molecular visualizations; unfortunately existing approaches are tailored towards a single visual representation. We propose a general illumination model for molecular visualization that is valid for different representations. With our illumination model, it becomes possible, for the first time, to achieve consistent illumination among all atom‐based molecular representations. The proposed model can be further evaluated in real‐time, as it employs an analytical solution to simulate diffuse light interactions between objects. To be able to derive such a solution for the rather complicated and diverse visual representations, we propose the use of regression analysis together with adapted parameter sampling strategies as well as shape parametrization guided sampling, which are applied to the geometric building blocks of the targeted visual representations. We will discuss the proposed sampling strategies, the derived illumination model, and demonstrate its capabilities when visualizing several dynamic molecules.
Hunting High and Low: Visualising Shifting Correlations in Financial Markets
Authors: Simon P. M. , Turkay C. 
Published: July 2018

AbstractThe analysis of financial assets’ correlations is fundamental to many aspects of finance theory and practice, especially modern portfolio theory and the study of risk. In order to manage investment risk, in‐depth analysis of changing correlations is needed, with both high and low correlations between financial assets (and groups thereof) important to identify. In this paper, we propose a visual analytics framework for the interactive analysis of relations and structures in dynamic, high‐dimensional correlation data. We conduct a series of interviews and review the financial correlation analysis literature to guide our design. Our solution combines concepts from multi‐dimensional scaling, weighted complete graphs and threshold networks to present interactive, animated displays which use proximity as a visual metaphor for correlation and animation stability to encode correlation stability. We devise interaction techniques coupled with context‐sensitive auxiliary views to support the analysis of subsets of correlation networks. As part of our contribution, we also present behaviour profiles to help guide future users of our approach. We evaluate our approach by checking the validity of the layouts produced, presenting a number of analysis stories, and through a user study. We observe that our solutions help unravel complex behaviours and resonate well with study participants in addressing their needs in the context of correlation analysis in finance.
State of the Art of Sports Data Visualization
Authors: Perin C. , Vuillemot R. , Stolper C. D. , Stasko J. T. , Wood J. , Carpendale S. 
Published: July 2018

AbstractIn this report, we organize and reflect on recent advances and challenges in the field of sports data visualization. The exponentially‐growing body of visualization research based on sports data is a prime indication of the importance and timeliness of this report. Sports data visualization research encompasses the breadth of visualization tasks and goals: exploring the design of new visualization techniques; adapting existing visualizations to a novel domain; and conducting design studies and evaluations in close collaboration with experts, including practitioners, enthusiasts, and journalists. Frequently this research has impact beyond sports in both academia and in industry because it is i) grounded in realistic, highly heterogeneous data, ii) applied to real‐world problems, and iii) designed in close collaboration with domain experts. In this report, we analyze current research contributions through the lens of three categories of sports data: box score data (data containing statistical summaries of a sport event such as a game), tracking data (data about in‐game actions and trajectories), and meta‐data (data about the sport and its participants but not necessarily a given game). We conclude this report with a high‐level discussion of sports visualization research informed by our analysis—identifying critical research gaps and valuable opportunities for the visualization community. More information is available at the STAR's website: https://sportsdataviz.github.io/.
Implicit decision framing as an unrecognized source of confusion in endangered species classification
Authors: Cummings Jonathan W. , Converse Sarah J. , Smith David R. , Morey Steve , Runge Michael C. 
Published: July 2018

AbstractLegal classification of species requires scientific and values‐based components, and how those components interact depends on how people frame the decision. Is classification a negotiation of trade‐offs, a decision on how to allocate conservation efforts, or simply a comparison of the biological status of a species to a legal standard? The answers to problem‐framing questions such as these influence decision making in species classifications. In our experience, however, decision makers, staff biologists, and stakeholders often have differing perspectives of the decision problem and assume different framings. In addition to differences between individuals, in some cases it appears individuals themselves are unclear about the decision process, which contributes to regulatory paralysis, litigation, and a loss of trust by agency staff and the public. We present 5 framings: putting species in the right bin, doing right by the species over time, saving the most species on a limited budget, weighing extinction risk against other objectives, and strategic classification to advance conservation. These framings are inspired by elements observed in current classification practices. Putting species in the right bin entails comparing a scientific status assessment with policy thresholds and accounting for potential misclassification costs. Doing right by the species adds a time dimension to the classification decision, and saving the most species on a limited budget classifies a suite of species simultaneously. Weighing extinction risk against other objectives would weigh ecological or socioeconomic concerns in classification decisions, and strategic classification to advance conservation would make negotiation a component of classification. We view these framings as a means to generate thought, discussion, and movement toward selection and application of explicit classification framings. Being explicit about the decision framing could lead decision makers toward more efficient and defensible decisions, reduce internal confusion and external conflict, and support better collaboration between scientists and policy makers.