bluuebunny/crossref_metadata_2025_split huggingface dataset:
Functions of G protein‐coupled receptor 56 in health and disease
Authors: Su Tiantian , Guan Qiuyun , Cheng Huijuan , Zhu Zhenduo , Jiang Chunru , Guo Paipai , Tai Yu , Sun Hanfei , Wang Manman , Wei Wei , Wang Qingtong 
Published: August 2022

AbstractHuman G protein‐coupled receptor 56 (GPR56) is encoded by gene ADGRG1 from chromosome 16q21 and is homologously encoded in mice, at chromosome 8. Both 687 and 693 splice forms are present in humans and mice. GPR56 has a 381 amino acid‐long N‐terminal extracellular segment and a GPCR proteolysis site upstream from the first transmembrane domain. GPR56 is mainly expressed in the heart, brain, thyroid, platelets, and peripheral blood mononuclear cells. Accumulating evidence indicates that GPR56 promotes the formation of myelin sheaths and the development of oligodendrocytes in the cerebral cortex of the central nervous system. Moreover, GPR56 contributes to the development and differentiation of hematopoietic stem cells, induces adipogenesis, and regulates the function of immune cells. The lack of GPR56 leads to nervous system dysfunction, platelet disorders, and infertility. Abnormal expression of GPR56 is related to the malignant transformation and tumor metastasis of several cancers including melanoma, neuroglioma, and gastrointestinal cancer. Metabolic disorders and cardiovascular diseases are also associated with dysregulation of GPR56 expression, and GPR56 is involved in the pharmacological resistance to some antidepressant and cancer drug treatments. In this review, the molecular structure, expression profile, and signal transduction of GPR56 are introduced, and physiological and pathological functions of GRP56 are comprehensively summarized. Attributing to its significant biological functions and its long N‐terminal extracellular region that interacts with multiple ligands, GPR56 is becoming an attractive therapeutic target in treating neurological and hematopoietic diseases.
Is beef quality affected by the inclusion of distiller grains in cattle diets?
Authors: Merayo Manuela , Pighin Darío , Cunzolo Sebastián , Grigioni Gabriela 
Published: August 2022

AbstractThis study aimed to analyze the effect of including increasing levels of distiller grains (DG) in beef cattle feeding diets on meat quality from an integral approach. To this end, we analyzed the meat from 36 yearling steers fed with four dietary treatments: 0DG (control corn‐based diet), 15DG, 30DG, and 45DG (containing 15%, 30%, or 45% of DG on DM basis). Crude protein, ash, and fat contents of diets increased with DG level. The fatty acid profile of DG diets was reflected in the fatty acid profile of plasma samples. Feeding diets with DG had no effect on the biochemical parameters analyzed in plasma. In addition, it did not influence the water holding capacity, the muscle or fat color at 72 h post mortem or at retail display, or the contents of SFAs, MUFAs, and PUFAs in beef. The DG diets led to lower values of cooking loss. Meat from 15DG and 30DG showed greater percentage of troponin C and fragments of 30 to 27 kDa than meat from 45DG. Also, meat from 30DG showed the lowest values of Warner‐Bratzler shear force. Hence, including up to 30% corn DG in beef cattle feeding diets had positive effects on meat quality.
Conservative management versus systematic suture of isolated vaginal or first‐degree perineal tears after delivery: A preliminary randomized efficacy trial
Authors: Lallemant Marine , D'Antona Aurélie , Vidal Chrystelle , Bourtembourg Aude , Toubin Claire , Chehab Malek , Vilchez Marie , Boiteux Guillaume , Ramanah Rajeev , Pazart Lionel , Riethmuller Didier , Mottet Nicolas 
Published: August 2022

AbstractBackgroundThe objective of this study was to assess the preliminary efficacy and safety of conservative management compared with systematic suture in isolated vaginal or first‐degree perineal tears after birth.MethodsWe conducted a preliminary efficacy, open‐label, randomized, controlled, and prospective trial. This study implemented Simon's 2‐step plan (interim analysis and final analysis) to test the success rate of the digital compression strategy group. Primiparous women aged ≥18 years with isolated vaginal or first‐degree perineal tears after spontaneous vaginal birth of a cephalic presenting term (≥37 weeks) neonate were randomly allocated to the conservative management (CM) group (digital compression if bleeding followed by suture if persistent bleeding) or a systematic suture (SS) group. The primary outcome was the success of the intervention 10 days after delivery, defined by pain as evaluated using a visual analog scale < 3, satisfactory healing defined by a REEDA score ≤ 2, and no bleeding or infection. Sexual well‐being was assessed at 2 and 6 months postpartum.ResultsAmong 861/2209 eligible women, 143 consenting women with a superficial perineal tear were randomized: 72 in the systematic suture group and 71 in the conservative management group. Success rate was 87.8% (90% CI [70.5‐93.54]) (42/55) in the systematic suture group vs 90% (90% CI [78.3‐93.8]) (53/61) in the conservative management group. The REEDA score was significantly higher in the systematic suture group (1.4 vs 0.9; P = 0.036). Perineal pain was significantly higher at day 1 in the systematic suture group (2.38 vs 1.69; P = 0.034). For the Female Sexual Functional Index score, no significant difference was found between the two groups at inclusion or at 2 and 6 months postpartum.ConclusionsConservative management of superficial perineal tears shows an efficacy rate ≥90%. Women in the conservative management group had less pain at the 1st day follow‐up and lower REEDA scores at the 10th day follow‐up.
‘One size doesn't fit all’: Lessons from interaction analysis on tailoring Open Science practices to qualitative research
Authors: Huma Bogdana , Joyce Jack B. 
Published: August 2022

AbstractThe Open Science Movement aims to enhance the soundness, transparency, and accessibility of scientific research, and at the same time increase public trust in science. Currently, Open Science practices are mainly presented as solutions to the ‘reproducibility crisis’ in hypothetico‐deductive quantitative research. Increasing interest has been shown towards exploring how these practices can be adopted by qualitative researchers. In reviewing this emerging body of work, we conclude that the issue of diversity within qualitative research has not been adequately addressed. Furthermore, we find that many of these endeavours start with existing solutions for which they are trying to find matching problems to be solved. We contrast this approach with a natural incorporation of Open Science practices within interaction analysis and its constituent research traditions: conversation analysis, discursive psychology, ethnomethodology, and membership categorisation analysis. Zooming in on the development of conversation analysis starting in the 1960s, we highlight how practices for opening up and sharing data and analytic thinking have been embedded into its methodology. On the basis of this presentation, we propose a series of lessons learned for adopting Open Science practices in qualitative research.
White children's empathy‐related responding and prosocial behavior toward White and Black children
Authors: Spinrad Tracy L. , Eisenberg Nancy , Xiao Sonya Xinyue , Xu Jingyi , Berger Rebecca H. , Pierotti Sarah L. , Laible Deborah J. , Carlo Gustavo , Gal‐Szabo Diana E. , Janssen Jayley , Fraser Ashley , Xu Xiaoye , Wang Wen , Lopez Jamie 
Published: August 2022

AbstractRelations among White (non‐Latinx) children's empathy‐related responding, prosocial behaviors, and racial attitudes toward White and Black peers were examined. In 2017, 190 (54% boys) White 5‐ to 9‐year‐old children (M = 7.09 years, SD = 0.94) watched a series of videos that depicted social rejection of either a White or Black child. Empathy‐related responses, prosocial behaviors, and racial attitudes were measured using multiple methods. Results showed that younger children showed less facial concern toward Black than White peers and greater increases with age in concern and prosocial behaviors (sharing a desirable prize) for Black, compared to White, targets. Children's facial anger increased with age for White but not Black targets. The findings can extend our understanding children's anti‐racism development.
How accessible is my visualization? Evaluating visualization accessibility with Chartability
Authors: Elavsky Frank , Bennett Cynthia , Moritz Dominik 
Published: August 2022

AbstractNovices and experts have struggled to evaluate the accessibility of data visualizations because there are no common shared guidelines across environments, platforms, and contexts in which data visualizations are authored. Between non‐specific standards bodies like WCAG, emerging research, and guidelines from specific communities of practice, it is hard to organize knowledge on how to evaluate accessible data visualizations. We present Chartability, a set of heuristics synthesized from these various sources which enables designers, developers, researchers, and auditors to evaluate data‐driven visualizations and interfaces for visual, motor, vestibular, neurological, and cognitive accessibility. In this paper, we outline our process of making a set of heuristics and accessibility principles for Chartability and highlight key features in the auditing process. Working with participants on real projects, we found that data practitioners with a novice level of accessibility skills were more confident and found auditing to be easier after using Chartability. Expert accessibility practitioners were eager to integrate Chartability into their own work. Reflecting on Chartability's development and the preliminary user evaluation, we discuss tradeoffs of open projects, working with high‐risk evaluations like auditing projects in the wild, and challenge future research projects at the intersection of visualization and accessibility to consider the broad intersections of disabilities.
Interactively Assessing Disentanglement in GANs
Authors: Jeong Sangwon , Liu Shusen , Berger Matthew 
Published: August 2022

AbstractGenerative adversarial networks (GAN) have witnessed tremendous growth in recent years, demonstrating wide applicability in many domains. However, GANs remain notoriously difficult for people to interpret, particularly for modern GANs capable of generating photo‐realistic imagery. In this work we contribute a visual analytics approach for GAN interpretability, where we focus on the analysis and visualization of GAN disentanglement. Disentanglement is concerned with the ability to control content produced by a GAN along a small number of distinct, yet semantic, factors of variation. The goal of our approach is to shed insight on GAN disentanglement, above and beyond coarse summaries, instead permitting a deeper analysis of the data distribution modeled by a GAN. Our visualization allows one to assess a single factor of variation in terms of groupings and trends in the data distribution, where our analysis seeks to relate the learned representation space of GANs with attribute‐based semantic scoring of images produced by GANs. Through use‐cases, we show that our visualization is effective in assessing disentanglement, allowing one to quickly recognize a factor of variation and its overall quality. In addition, we show how our approach can highlight potential dataset biases learned by GANs.
Infographics Wizard: Flexible Infographics Authoring and Design Exploration
Authors: Tyagi Anjul , Zhao Jian , Patel Pushkar , Khurana Swasti , Mueller Klaus 
Published: August 2022

AbstractInfographics are an aesthetic visual representation of information following specific design principles of human perception. Designing infographics can be a tedious process for non‐experts and time‐consuming, even for professional designers. With the help of designers, we propose a semi‐automated infographic framework for general structured and flow‐based infographic design generation. For novice designers, our framework automatically creates and ranks infographic designs for a user‐provided text with no requirement for design input. However, expert designers can still provide custom design inputs to customize the infographics. We will also contribute an individual visual group (VG) designs dataset (in SVG), along with a 1k complete infographic image dataset with segmented VGs in this work. Evaluation results confirm that by using our framework, designers from all expertise levels can generate generic infographic designs faster than existing methods while maintaining the same quality as hand‐designed infographics templates.
HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters
Authors: Appleby G. , Espadoto M. , Chen R. , Goree S. , Telea A. C. , Anderson E. W. , Chang R. 
Published: August 2022

AbstractProjection algorithms such as t‐SNE or UMAP are useful for the visualization of high dimensional data, but depend on hyperparameters which must be tuned carefully. Unfortunately, iteratively recomputing projections to find the optimal hyperparameter values is computationally intensive and unintuitive due to the stochastic nature of such methods. In this paper we propose HyperNP, a scalable method that allows for real‐time interactive hyperparameter exploration of projection methods by training neural network approximations. A HyperNP model can be trained on a fraction of the total data instances and hyperparameter configurations that one would like to investigate and can compute projections for new data and hyperparameters at interactive speeds. HyperNP models are compact in size and fast to compute, thus allowing them to be embedded in lightweight visualization systems. We evaluate the performance of HyperNP across three datasets in terms of performance and speed. The results suggest that HyperNP models are accurate, scalable, interactive, and appropriate for use in real‐world settings.
AirLens: Multi‐Level Visual Exploration of Air Quality Evolution in Urban Agglomerations
Authors: Qu Dezhan , Lv Cheng , Lin Yiming , Zhang Huijie , Wang Rong 
Published: August 2022

AbstractThe precise prevention and control of air pollution is a great challenge faced by environmental experts in recent years. Understanding the air quality evolution in the urban agglomeration is important for coordinated control of air pollution. However, the complex pollutant interactions between different cities lead to the collaborative evolution of air quality. The existing statistical and machine learning methods cannot well support the comprehensive analysis of the dynamic air quality evolution. In this study, we propose AirLens, an interactive visual analytics system that can help domain experts explore and understand the air quality evolution in the urban agglomeration from multiple levels and multiple aspects. To facilitate the cognition of the complex multivariate spatiotemporal data, we first propose a multi‐run clustering strategy with a novel glyph design for summarizing and understanding the typical pollutant patterns effectively. On this basis, the system supports the multi‐level exploration of air quality evolution, namely, the overall level, stage level and detail level. Frequent pattern mining, city community extraction and useful filters are integrated into the system for discovering significant information comprehensively. The case study and positive feedback from domain experts demonstrate the effectiveness and usability of AirLens.
CorpusVis: Visual Analysis of Digital Sheet Music Collections
Authors: Miller Matthias , Rauscher Julius , Keim Daniel A. , El‐Assady Mennatallah 
Published: August 2022

AbstractManually investigating sheet music collections is challenging for music analysts due to the magnitude and complexity of underlying features, structures, and contextual information. However, applying sophisticated algorithmic methods would require advanced technical expertise that analysts do not necessarily have. Bridging this gap, we contribute CorpusVis, an interactive visual workspace, enabling scalable and multi‐faceted analysis. Our proposed visual analytics dashboard provides access to computational methods, generating varying perspectives on the same data. The proposed application uses metadata including composers, type, epoch, and low‐level features, such as pitch, melody, and rhythm. To evaluate our approach, we conducted a pair‐analytics study with nine participants. The qualitative results show that CorpusVis supports users in performing exploratory and confirmatory analysis, leading them to new insights and findings. In addition, based on three exemplary workflows, we demonstrate how to apply our approach to different tasks, such as exploring musical features or comparing composers.
Streaming Approach to In Situ Selection of Key Time Steps for Time‐Varying Volume Data
Authors: Wu Mengxi , Chiang Yi‐Jen , Musco Christopher 
Published: August 2022

AbstractKey time steps selection, i.e., selecting a subset of most representative time steps, is essential for effective and efficient scientific visualization of large time‐varying volume data. In particular, as computer simulations continue to grow in size and complexity, they often generate output that exceeds both the available storage capacity and bandwidth for transferring results to storage, making it indispensable to save only a subset of time steps. At the same time, this subset must be chosen so that it is highly representative, to facilitate post‐processing and reconstruction with high fidelity. The key time steps selection problem is especially challenging in the in situ setting, where we can only process data in one pass in an online streaming fashion, using a small amount of main memory and fast computation. In this paper, we formulate the problem as that of optimal piece‐wise linear interpolation. We first apply a method from numerical linear algebra to compute linear interpolation solutions and their errors in an online streaming fashion. Using that method as a building block, we can obtain a global optimal solution for the piece‐wise linear interpolation problem via a standard dynamic programming (DP) algorithm. However, this approach needs to process the time steps in multiple passes and is too slow for the in situ setting. To address this issue, we introduce a novel approximation algorithm, which processes time steps in one pass in an online streaming fashion, with very efficient computing time and main memory space both in theory and in practice. The algorithm is suitable for the in situ setting. Moreover, we prove that our algorithm, which is based on a greedy update rule, has strong theoretical guarantees on the approximation quality and the number of time steps stored. To the best of our knowledge, this is the first algorithm suitable for in situ key time steps selection with such theoretical guarantees, and is the main contribution of this paper. Experiments demonstrate the efficacy of our new techniques.
Hybrid Touch/Tangible Spatial Selection in Augmented Reality
Authors: Sereno Mickael , Gosset Stéphane , Besançon Lonni , Isenberg Tobias 
Published: August 2022

AbstractWe study tangible touch tablets combined with Augmented Reality Head‐Mounted Displays (AR‐HMDs) to perform spatial 3D selections. We are primarily interested in the exploration of 3D unstructured datasets such as cloud points or volumetric datasets. AR‐HMDs immerse users by showing datasets stereoscopically, and tablets provide a set of 2D exploration tools. Because AR‐HMDs merge the visualization, interaction, and the users' physical spaces, users can also use the tablets as tangible objects in their 3D space. Nonetheless, the tablets' touch displays provide their own visualization and interaction spaces, separated from those of the AR‐HMD. This raises several research questions compared to traditional setups. In this paper, we theorize, discuss, and study different available mappings for manual spatial selections using a tangible tablet within an AR‐HMD space. We then study the use of this tablet within a 3D AR environment, compared to its use with a 2D external screen.
Mobile and Multimodal? A Comparative Evaluation of Interactive Workplaces for Visual Data Exploration
Authors: Molina León G. , Lischka M. , Luo W. , Breiter A. 
Published: August 2022

AbstractMobile devices are increasingly being used in the workplace. The combination of touch, pen, and speech interaction with mobile devices is considered particularly promising for a more natural experience. However, we do not yet know how everyday work with multimodal data visualizations on a mobile device differs from working in the standard WIMP workplace setup. To address this gap, we created a visualization system for social scientists, with a WIMP interface for desktop PCs, and a multimodal interface for tablets. The system provides visualizations to explore spatio‐temporal data with consistent WIMP and multimodal interaction techniques. To investigate how the different combinations of devices and interaction modalities affect the performance and experience of domain experts in a work setting, we conducted an experiment with 16 social scientists where they carried out a series of tasks with both interfaces. Participants were significantly faster and slightly more accurate on the WIMP interface. They solved the tasks with different strategies according to the interaction modalities available. The pen was the most used and appreciated input modality. Most participants preferred the multimodal setup and could imagine using it at work. We present our findings, together with their implications for the interaction design of data visualizations.
DanmuVis: Visualizing Danmu Content Dynamics and Associated Viewer Behaviors in Online Videos
Authors: Chen S. , Li S. , Li Y. , Zhu J. , Long J. , Chen S. , Zhang J. , Yuan X. 
Published: August 2022

AbstractDanmu (Danmaku) is a unique social media service in online videos, especially popular in Japan and China, for viewers to write comments while watching videos. The danmu comments are overlaid on the video screen and synchronized to the associated video time, indicating viewers' thoughts of the video clip. This paper introduces an interactive visualization system to analyze danmu comments and associated viewer behaviors in a collection of videos and enable detailed exploration of one video on demand. The watching behaviors of viewers are identified by comparing video time and post time of viewers' danmu. The system supports analyzing danmu content and viewers' behaviors against both video time and post time to gain insights into viewers' online participation and perceived experience. Our evaluations, including usage scenarios and user interviews, demonstrate the effectiveness and usability of our system.
Investigating the Role and Interplay of Narrations and Animations in Data Videos
Authors: Cheng Hao , Wang Junhong , Wang Yun , Lee Bongshin , Zhang Haidong , Zhang Dongmei 
Published: August 2022

AbstractCombining data visualizations, animations, and audio narrations, data videos can increase viewer engagement and effectively communicate data stories. Due to their increasing popularity, data videos have gained growing attention from the visualization research community. However, recent research on data videos has focused on animations, lacking an understanding of narrations. In this work, we study how data videos use narrations and animations to convey information effectively. We conduct a qualitative analysis on 426 clips with visualizations extracted from 60 data videos collected from a variety of media outlets, covering a diverse array of topics. We manually label 816 sentences with 1226 semantic labels and record the composition of 2553 animations through an open coding process. We also analyze how narrations and animations coordinate with each other by assigning links between semantic labels and animations. With 937 (76.4%) semantic labels and 2503 (98.0%) animations linked, we identify four types of narration‐animation relationships in the collected clips. Drawing from the findings, we discuss study implications and future research opportunities of data videos.