bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment and predictors of fatigue in men with prostate cancer receiving radiotherapy and androgen deprivation therapy
Authors: Bandara Vindya , Capp Anne , Ahmed Gias , Arm Jameen , Martin Jarad 
Published: July 2019

AbstractIntroductionFatigue is a commonly reported symptom in men receiving radiation therapy and androgen deprivation therapy (ADT) for prostate cancer. Despite this, the complex mechanisms remain unclear. This study aims to investigate factors which correlate with development of fatigue.MethodsTwenty‐seven men with high‐risk prostate cancer undergoing radiation therapy and 18 months of ADT were assessed for fatigue, haemoglobin (Hb), testosterone, magnetic resonance imaging (MRI) fat fraction (FF) and apparent diffusion coefficient (ADC), at baseline and at intervals after radiotherapy. Changes from baseline were analysed using paired t‐tests. Linear time trends were assessed using linear mixed effect models.ResultsOverall, mean fatigue score increased from baseline to the 18‐month time interval (difference 4.5, P = 0.0114). The mean value for Hb significantly decreased (P < 0.001) from baseline to 18 months. The mean value for testosterone significantly decreased (P < 0.001) from baseline to 12 months, and remained low. Mean for MRI FF showed a significant increase (P < 0.001) from baseline to 6 months. MRI ADC showed a non‐significant decrease from baseline to 6 months (P = 0.4416).ConclusionRadiotherapy and ADT resulted in a significant increase in fatigue scores. Statistically significant changes were noted in Hb, testosterone and MRI FF and ADC, however, none were shown to have a strong association with worsening fatigue. Further investigation in a larger cohort is required to assess the interaction between fatigue and possible biological factors.
Strategic inventories under limited commitment
Authors: Antoniou Fabio , Fiocco Raffaele 
Published: July 2019

AbstractIn a dynamic storable good market where demand changes over time, we investigate the producer's strategic incentives to hold inventories in response to the possibility of buyer stockpiling. The literature on storable goods has demonstrated that buyer stockpiling in anticipation of higher future prices harms the producer's profitability, particularly when the producer cannot commit to future prices. We show that the producer's inventories act as a strategic device to mitigate the loss from the lack of commitment. Our results provide a rationale for the producer's inventory behavior that sheds new light on the well‐documented empirical evidence about inventories.
Peroxisome proliferator‐activated receptor γ coactivator 1α regulates mitochondrial calcium homeostasis, sarcoplasmic reticulum stress, and cell death to mitigate skeletal muscle aging
Authors: Gill Jonathan F. , Delezie Julien , Santos Gesa , McGuirk Shawn , Schnyder Svenia , Frank Stephan , Rausch Martin , St‐Pierre Julie , Handschin Christoph 
Published: July 2019

AbstractAge‐related impairment of muscle function severely affects the health of an increasing elderly population. While causality and the underlying mechanisms remain poorly understood, exercise is an efficient intervention to blunt these aging effects. We thus investigated the role of the peroxisome proliferator‐activated receptor γ coactivator 1α (PGC‐1α), a potent regulator of mitochondrial function and exercise adaptation, in skeletal muscle during aging. We demonstrate that PGC‐1α overexpression improves mitochondrial dynamics and calcium buffering in an estrogen‐related receptor α‐dependent manner. Moreover, we show that sarcoplasmic reticulum stress is attenuated by PGC‐1α. As a result, PGC‐1α prevents tubular aggregate formation and cell death pathway activation in old muscle. Similarly, the pro‐apoptotic effects of ceramide and thapsigargin were blunted by PGC‐1α in muscle cells. Accordingly, mice with muscle‐specific gain‐of‐function and loss‐of‐function of PGC‐1α exhibit a delayed and premature aging phenotype, respectively. Together, our data reveal a key protective effect of PGC‐1α on muscle function and overall health span in aging.
A Resilience‐Based Intervention Programme to Enhance Psychological Well‐Being and Protective Factors for Rural‐to‐Urban Migrant Children in China
Authors: Tam Cheuk Chi , Li Xiaoming , Benotsch Eric G. , Lin Danhua 
Published: July 2019

AbstractBackgroundThe current study evaluated the preliminary efficacy of a pilot trial of a resilience‐based intervention programme designed to decrease depression symptoms and improve protective factors (e.g. personal assets, social resources, cultural adaptation self‐efficacy, interpreting adversity, and self‐esteem) among rural‐to‐urban migrant children in China.MethodsParticipants were 285 fourth and fifth graders from two migrant children schools in Beijing. One school was cluster‐randomly assigned as the intervention group (n = 127) and the other was assigned as the wait‐list control group (n = 158). Data were collected at baseline and post‐intervention with a follow‐up rate of 96.8 per cent.ResultsMultivariate analyses found significantly greater increases in social resources, cultural adaptation self‐efficacy, and making positive sense of adversity in the intervention group than in the control group. In the intervention group, migrant children who had moved more often reported a higher increase in personal assets than those who had fewer mobility experience.ConclusionThe findings suggest that the resilience‐based intervention can be efficacious in improving resilience among migrant children in China.
Policy networks, performance metrics and platform markets: Charting the expanding data infrastructure of higher education
Authors: Williamson Ben 
Published: July 2019

AbstractDigital data are transforming higher education (HE) to be more student‐focused and metrics‐centred. In the UK, capturing detailed data about students has become a government priority, with an emphasis on using student data to measure, compare and assess university performance. The purpose of this paper is to examine the governmental and commercial drivers of current large‐scale technological efforts to collect and analyse student data in UK HE. The result is an expanding data infrastructure which includes large‐scale and longitudinal datasets, learning analytics services, student apps, data dashboards and digital learning platforms powered by artificial intelligence (AI). Education data scientists have built positive pedagogic cases for student data analysis, learning analytics and AI. The politicization and commercialization of the wider HE data infrastructure is translating them into performance metrics in an increasingly market‐driven sector, raising the need for policy frameworks for ethical, pedagogically valuable uses of student data in HE.Practitioner NotesWhat is already known about this topicLearning analytics, education data science and artificial intelligence are opening up new ways of collecting and analysing student data in higher education.UK government policies emphasize the use of student data for improvements to teaching and learning.What this paper addsA conceptual framework from “infrastructure studies” demonstrates how political objectives and commercial aims are fused to HE data systems, with data infrastructure becoming a key tool of government reform.A critical infrastructure analysis shows that student data processing technologies are being developed and deployed to measure university performance through student data.Implications for practice and/or policyEducators and managers in universities need to prepare robust institutional frameworks to govern their use of student data.Learning analytics practitioners, data scientists, learning scientists and social science researchers need to collaborate with the policy community and education technology developers on new policy frameworks to challenge narrow uses of student data as performance metrics.
Mortality salience leads to greater consumption of an ostensibly alcoholic beverage on Friday versus other weekdays
Authors: McCabe Simon , Bartholow Bruce D. 
Published: July 2019

ObjectivesWe build on findings from terror management theory to examine how non‐conscious mortality concerns may lead individuals to adhere to cultural meanings yoked to discrete time periods, in this case influencing consumption of an ostensibly alcoholic beverage.DesignThe study took the form of a 2 (death vs. uncertainty reminder) × 3 (Monday vs. Wednesday vs. Friday) between‐subjects laboratory‐based quasi‐experimental design.MethodsA total of 210 participants (age: M = 21.92 years, SD = 5.33; 103 males and 107 females) recruited from a UK university answered either an open‐ended question to prime mortality or uncertainty cognition on either a Monday, Wednesday, or Friday. As part of an ostensible taste test, they then consumed as much or as little of a purportedly alcoholic drink as they desired.ResultsDeath reminders (vs. control topic) were found to result in more consumption of the beverage on a Friday, less consumption on a Monday, and no difference in consumption on a Wednesday.ConclusionsFindings point to the flexible, time‐contingent nature of culture‐oriented defences against mortality concerns with potential implications for the efficacy of alcohol health warnings featuring mortality‐related stimuli.
Statement of contribution

What is already known on this subject?

Death reminders can spur people to adhere to cultural scripts for behaviour in the service of maintaining existential security.

Death reminders can lead people to consume alcohol (particularly for those who are low in self‐esteem).

What does this study add?

Death reminders may lead people to adhere to temporally yoked scripts related to drinking behaviour.

Specifically, death reminders led to more positive drinking attitudes and more alcohol consumed on Friday compared to Monday or Wednesday.

Cultural script following may be specifically tied to non‐conscious mortality awareness.
Multiple Views: different meanings and collocated words
Authors: Roberts J. C. , Al‐maneea H. , Butcher P. W. S. , Lew R. , Rees G. , Sharma N. , Frankenberg‐Garcia A. 
Published: July 2019

AbstractWe report on an in‐depth corpus linguistic study on ‘multiple views’ terminology and word collocation. We take a broad interpretation of these terms, and explore the meaning and diversity of their use in visualisation literature. First we explore senses of the term ‘multiple views’ (e.g., ‘multiple views’ can mean juxtaposition, many viewport projections or several alternative opinions). Second, we investigate term popularity and frequency of occurrences, investigating usage of ‘multiple’ and ‘view’ (e.g., multiple views, multiple visualisations, multiple sets). Third, we investigate word collocations and terms that have a similar sense (e.g., multiple views, side‐by‐side, small multiples). We built and used several corpora, including a 6‐million‐word corpus of all IEEE Visualisation conference articles published in IEEE Transactions on Visualisation and Computer Graphics 2012 to 2017. We draw on our substantial experience from early work in coordinated and multiple views, and with collocation analysis develop several lists of terms. This research provides insight into term use, a reference for novice and expert authors in visualisation, and contributes a taxonomy of ‘multiple view’ terms.
VIAN: A Visual Annotation Tool for Film Analysis
Authors: Halter Gaudenz , Ballester‐Ripoll Rafael , Flueckiger Barbara , Pajarola Renato 
Published: July 2019

AbstractWhile color plays a fundamental role in film design and production, existing solutions for film analysis in the digital humanities address perceptual and spatial color information only tangentially. We introduce VIAN, a visual film annotation system centered on the semantic aspects of film color analysis. The tool enables expert‐assessed labeling, curation, visualization and Classification of color features based on their perceived context and aesthetic quality. It is the first of its kind that incorporates foreground‐background information made possible by modern deep learning segmentation methods. The proposed tool seamlessly integrates a multimedia data management system, so that films can undergo a full color‐oriented analysis pipeline.
Investigating Effects of Visual Anchors on Decision‐Making about Misinformation
Authors: Wesslen R. , Santhanam S. , Karduni A. , Cho I. , Shaikh S. , Dou W. 
Published: July 2019

AbstractCognitive biases are systematic errors in judgment due to an over‐reliance on rule‐of‐thumb heuristics. Recent research suggests that cognitive biases, like numerical anchoring, transfers to visual analytics in the form of visual anchoring. However, it is unclear how visualization users can be visually anchored and how the anchors affect decision‐making. To investigate, we performed a between‐subjects laboratory experiment with 94 participants to analyze the effects of visual anchors and strategy cues using a visual analytics system. The decision‐making task was to identify misinformation from Twitter news accounts. Participants were randomly assigned to conditions that modified the scenario video (visual anchor) and/or strategy cues provided. Our findings suggest that such interventions affect user activity, speed, confidence, and, under certain circumstances, accuracy. We discuss implications of our results on the forking paths problem and raise concerns on how visualization researchers train users to avoid unintentionally anchoring users and affecting the end result.
Oui! Outlier Interpretation on Multi‐dimensional Data via Visual Analytics
Authors: Zhao Xun , Cui Weiwei , Wu Yanhong , Zhang Haidong , Qu Huamin , Zhang Dongmei 
Published: July 2019

AbstractOutliers, the data instances that do not conform with normal patterns in a dataset, are widely studied in various domains, such as cybersecurity, social analysis, and public health. By detecting and analyzing outliers, users can either gain insights into abnormal patterns or purge the data of errors. However, different domains usually have different considerations with respect to outliers. Understanding the defining characteristics of outliers is essential for users to select and filter appropriate outliers based on their domain requirements. Unfortunately, most existing work focuses on the efficiency and accuracy of outlier detection, neglecting the importance of outlier interpretation. To address these issues, we propose Oui, a visual analytic system that helps users understand, interpret, and select the outliers detected by various algorithms. We also present a usage scenario on a real dataset and a qualitative user study to demonstrate the effectiveness and usefulness of our system.
SurgeryCuts: Embedding Additional Information in Maps without Occluding Features
Authors: Angelini M. , Buchmüller Juri , Keim Daniel A. , Meschenmoser Philipp , Santucci G. 
Published: July 2019

AbstractVisualizing contextual information to a map often comes at the expense of overplotting issues. Especially for use cases with relevant map features in the immediate vicinity of an information to add, occlusion of the relevant map context should be avoided. We present SurgeryCuts, a map manipulation technique for the creation of additional canvas area for contextual visualizations on maps. SurgeryCuts is occlusion‐free and does not shift, zoom or alter the map viewport. Instead, relevant parts of the map can be cut apart. The affected area is controlledly distorted using a parameterizable warping function fading out the map distortion depending on the distance to the cut. We define extended metrics for our approach and compare to related approaches. As well, we demonstrate the applicability of our approach at the example of tangible use cases and a comparative user study.
Visualization of Equivalence in 2D Bivariate Fields
Authors: Zheng Boyan , Rieck Bastian , Leitte Heike , Sadlo Filip 
Published: July 2019

AbstractIn this paper, we show how the equivalence property leads to the novel concept of equivalent regions in mappings from ℝn to ℝn. We present a technique for obtaining these regions both in the domain and the codomain of such a mapping, and determine their correspondence. This enables effective investigation of variation equivalence within mappings, and between mappings in terms of comparative visualization. We implement our approach for n = 2, and demonstrate its utility using different examples.
ChronoCorrelator: Enriching Events with Time Series
Authors: van Dortmont M.A.M.M. , van den Elzen S. , van Wijk J.J. 
Published: July 2019

AbstractEvent sequences and time series are widely recorded in many application domains; examples are stock market prices, electronic health records, server operation and performance logs. Common goals for recording are monitoring, root cause analysis and predictive analytics. Current analysis methods generally focus on the exploration of either event sequences or time series. However, deeper insights are gained by combining both. We present a visual analytics approach where users can explore both time series and event data simultaneously, combining visualization, automated methods and human interaction. We enable users to iteratively refine the visualization. Correlations between event sequences and time series can be found by means of an interactive algorithm, which also computes the presence of monotonic effects. We illustrate the effectiveness of our method by applying it to real world and synthetic data sets.
Scalable Ray Tracing Using the Distributed FrameBuffer
Authors: Usher Will , Wald Ingo , Amstutz Jefferson , Günther Johannes , Brownlee Carson , Pascucci Valerio 
Published: July 2019

AbstractImage‐ and data‐parallel rendering across multiple nodes on high‐performance computing systems is widely used in visualization to provide higher frame rates, support large data sets, and render data in situ. Specifically for in situ visualization, reducing bottlenecks incurred by the visualization and compositing is of key concern to reduce the overall simulation runtime. Moreover, prior algorithms have been designed to support either image‐ or data‐parallel rendering and impose restrictions on the data distribution, requiring different implementations for each configuration. In this paper, we introduce the Distributed FrameBuffer, an asynchronous image‐processing framework for multi‐node rendering. We demonstrate that our approach achieves performance superior to the state of the art for common use cases, while providing the flexibility to support a wide range of parallel rendering algorithms and data distributions. By building on this framework, we extend the open‐source ray tracing library OSPRay with a data‐distributed API, enabling its use in data‐distributed and in situ visualization applications.
Analysis of Decadal Climate Predictions with User‐guided Hierarchical Ensemble Clustering
Authors: Kappe C.P. , Böttinger M. , Leitte H. 
Published: July 2019

AbstractIn order to gain probabilistic results, ensemble simulation techniques are increasingly applied in the weather and climate sciences (as well as in various other scientific disciplines). In many cases, however, only mean results or other abstracted quantities such as percentiles are used for further analyses and dissemination of the data. In this work, we aim at a more detailed visualization of the temporal development of the whole ensemble that takes the variability of all single members into account. We propose a visual analytics tool that allows an effective analysis process based on a hierarchical clustering of the time‐dependent scalar fields. The system includes a flow chart that shows the ensemble members' cluster affiliation over time, reflecting the whole cluster hierarchy. The latter one can be dynamically explored using a visualization derived from a dendrogram. As an aid in linking the different views, we have developed an adaptive coloring scheme that takes into account cluster similarity and the containment relationships. Finally, standard visualizations of the involved field data (cluster means, ground truth data, etc.) are also incorporated. We include results of our work on real‐world datasets to showcase the utility of our approach.
Segmentifier: Interactive Refinement of Clickstream Data
Authors: Dextras‐Romagnino K. , Munzner T. 
Published: July 2019

AbstractClickstream data has the potential to provide insights into e‐commerce consumer behavior, but previous techniques fall short of handling the scale and complexity of real‐world datasets because they require relatively clean and small input. We present Segmentifier, a novel visual analytics interface that supports an iterative process of refining collections of action sequences into meaningful segments. We present task and data abstractions for clickstream data analysis, leading to a high‐level model built around an iterative view‐refine‐record loop with outcomes of conclude with an answer, export segment for further analysis in downstream tools, or abandon the question for a more fruitful analysis path. Segmentifier supports fast and fluid refinement of segments through tightly coupled visual encoding and interaction with a rich set of views that show evocative derived attributes for segments, sequences, and actions in addition to underlying raw sequences. These views support fast and fluid refinement of segments through filtering and partitioning attribute ranges. Interactive visual queries on custom action sequences are aggregated according to a three‐level hierarchy. Segmentifier features a detailed glyph‐based visual history of the automatically recorded analysis process showing the provenance of each segment as an analysis path of attribute constraints. We demonstrate the effectiveness of our approach through a usage scenario with real‐world data and a case study documenting the insights gained by a corporate e‐commerce analyst.
Capture & Analysis of Active Reading Behaviors for Interactive Articles on the Web
Authors: Conlen Matthew , Kale Alex , Heer Jeffrey 
Published: July 2019

AbstractJournalists, educators, and technical writers are increasingly publishing interactive content on the web. However, popular analytics tools provide only coarse information about how readers interact with individual pages, and laboratory studies often fail to capture the variability of a real‐world audience. We contribute extensions to the Idyll markup language to automate the detailed instrumentation of interactive articles and corresponding visual analysis tools for inspecting reader behavior at both micro‐ and macro‐levels. We present three case studies of interactive articles that were instrumented, posted online, and promoted via social media to reach broad audiences, and share data from over 50,000 reader sessions. We demonstrate the use of our tools to characterize article‐specific interaction patterns, compare behavior across desktop and mobile devices, and reveal reading patterns common across articles. Our contributed findings, tools, and corpus of behavioral data can help advance and inform more comprehensive studies of narrative visualization.