bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unveiling epigenetic regulation in cancer, aging, and rejuvenation with in vivo reprogramming technology
Authors: Sogabe Yuko , Seno Hiroshi , Yamamoto Takuya , Yamada Yasuhiro 
Published: July 2018

Reprogramming technology has enabled the fate conversion of terminally differentiated somatic cells into pluripotent stem cells or into another differentiated state. A dynamic reorganization of epigenetic regulation takes place during cellular reprogramming. Given that reprogramming does not require changes in the underlying genome, the technology can be used to actively modify epigenetic regulation. Although reprogramming has been investigated mostly at the cellular level in vitro, studies have reported that somatic cells are reprogrammable in multicellular organisms in vivo. In vivo reprogramming provides a potential strategy for regenerative medicine. Notably, recent studies using in vivo reprogramming technology to alter epigenetic regulation at organismal levels have revealed unappreciated epigenetic mechanisms in various biological phenomena, including cancer development, tissue regeneration, aging, and rejuvenation in mammals. Moreover, in vivo reprogramming technology can be applied to abrogate epigenetic aberrations associated with aging and cancer, which raises the possibility that the technology could provide a potential strategy to control the fate of detrimental cells such as senescent cells and cancer cells in vivo. Here, we review recent progress and future perspectives of in vivo reprogramming.
Identification of ENTPD8 and cytidine in pancreatic cancer by metabolomic and transcriptomic conjoint analysis
Authors: An Yong , Cai Huihua , Yang Yong , Zhang Yue , Liu Shengyong , Wu Xinquan , Duan Yunfei , Sun Donglin , Chen Xuemin 
Published: July 2018

To identify metabolic pathways that were perturbed in pancreatic cancer (PC), we investigated gene‐metabolite networks by integration of metabolomic and transcriptomic. In this research, we undertook the metabolomic study of 43 paired human PC samples, aiming to identify key metabolic alterations in PC. We also carried out in vitro experiments to validate that the key metabolite cytidine and its related gene ENTPD8 played an important role in PC cell proliferation. We screened out 13 metabolites differentially expressed in PC tissue (PCT) by liquid chromatography/mass spectrometry analysis on 34 metabolites, and the partial least square discrimination analysis results revealed that 9 metabolites among them were remarkably altered in PCT compared to adjacent noncancerous tissue (variable importance in projection >1, P < .05). Among the 9 metabolites, 7 might be potential biomarkers. The most significantly enriched metabolic pathway was pyrimidine metabolism. We analyzed 351 differentially expressed genes from The Cancer Genome Atlas and intersected them with Kyoto Encyclopedia of Genes and Genomes metabolic pathways. We found that ENTPD8 had a gene‐metabolite association with cytidine in the CTP dephosphorylation pathway. We verified by in vitro experiments that the CTP dephosphorylation pathway was changed in PCT compared with adjacent noncancerous tissue. ENTPD8 was downregulated in PCT, causing a reduction in cytidine formation and hence weakened CTP dephosphorylation in pyrimidine metabolism.
Inflammation and immune resolution
Authors: Taams Leonie S 
Published: July 2018

Summary
Inspired by the advances presented at the Inflammation and Immune Resolution Plenary Session at the British Society for Immunology Congress, December 2017, in this issue of Clinical & Experimental Immunology we present a Review Series on Inflammation and Immune Resolution. Our selection ranges from an overview of current genetic understanding of the similarities and differences between immune-mediated inflammatory diseases (IMIDs); discussion of several biological mechanisms underlying the aberrant activation of myeloid cells in RA, and how myeloid cell relevant anti-inflammatory mediators may contribute to immune resolution; presentation of fascinating evidence for the existence of innate immune memory in stromal cells and how this may exacerbate or restrain inflammatory disease; and a review of how the interleukin (IL)-6 family members IL-6 and IL-27 may drive or regulate inflammation. Inflammation and immune resolution are two sides of the same coin: the reviews presented in this series aim to equip readers with greater insight into the delicate balance between the two.
Hierarchical Correlation Clustering in Multiple 2D Scalar Fields
Authors: Liebmann Tom , Weber Gunther H. , Scheuermann Gerik 
Published: July 2018

AbstractSets of multiple scalar fields can be used to model many types of variation in data, such as uncertainty in measurements and simulations or time‐dependent behavior of scalar quantities. Many structural properties of such fields can be explained by dependencies between different points in the scalar field. Although these dependencies can be of arbitrary complexity, correlation, i.e., the linear dependency, already provides significant structural information. Existing methods for correlation analysis are usually limited to positive correlation, handle only local dependencies, or use combinatorial approximations to this continuous problem. We present a new approach for computing and visualizing correlated regions in sets of 2‐dimensional scalar fields. This paper describes the following three main contributions: (i) An algorithm for hierarchical correlation clustering resulting in a dendrogram, (ii) a generalization of topological landscapes for dendrogram visualization, and (iii) a new method for incorporating negative correlation values in the clustering and visualization. All steps are designed to preserve the special properties of correlation coefficients. The results are visualized in two linked views, one showing the cluster hierarchy as 2D landscape and the other providing a spatial context in the scalar field's domain. Different coloring and texturing schemes coupled with interactive selection support an exploratory data analysis.
Time Lattice: A Data Structure for the Interactive Visual Analysis of Large Time Series
Authors: Miranda Fabio , Lage Marcos , Doraiswamy Harish , Mydlarz Charlie , Salamon Justin , Lockerman Yitzchak , Freire Juliana , Silva Claudio T. 
Published: July 2018

AbstractAdvances in technology coupled with the availability of low‐cost sensors have resulted in the continuous generation of large time series from several sources. In order to visually explore and compare these time series at different scales, analysts need to execute online analytical processing (OLAP) queries that include constraints and group‐by's at multiple temporal hierarchies. Effective visual analysis requires these queries to be interactive. However, while existing OLAP cube‐based structures can support interactive query rates, the exponential memory requirement to materialize the data cube is often unsuitable for large data sets. Moreover, none of the recent space‐efficient cube data structures allow for updates. Thus, the cube must be re‐computed whenever there is new data, making them impractical in a streaming scenario. We propose Time Lattice, a memory‐efficient data structure that makes use of the implicit temporal hierarchy to enable interactive OLAP queries over large time series. Time Lattice is a subset of a fully materialized cube and is designed to handle fast updates and streaming data. We perform an experimental evaluation which shows that the space efficiency of the data structure does not hamper its performance when compared to the state of the art. In collaboration with signal processing and acoustics research scientists, we use the Time Lattice data structure to design the Noise Profiler, a web‐based visualization framework that supports the analysis of noise from cities. We demonstrate the utility of Noise Profiler through a set of case studies.
ChangeCatcher: Increasing Inter‐author Awareness for Visualization Development
Authors: Loorak M. , Tory M. , Carpendale S. 
Published: July 2018

AbstractWe introduce an approach for explicitly revealing changes between versions of a visualization workbook to support version comparison tasks. Visualization authors may need to understand version changes for a variety of reasons, analogous to document editing. An author who has been away for a while may need to catch up on the changes made by their co‐author, or a person responsible for formatting compliance may need to check formatting changes that occurred since the last time they reviewed the work. We introduce ChangeCatcher, a prototype tool to help people find and understand changes in a visualization workbook, specifically, a Tableau workbook. Our design is based on interviews we conducted with experts to investigate user needs and practices around version comparison. ChangeCatcher provides an overview of changes across six categories, and employs a multi‐level details‐on‐demand approach to progressively reveal details. Our qualitative study showed that ChangeCatcher's methods for explicitly revealing and categorizing version changes were helpful in version comparison tasks.
Towards Easy Comparison of Local Businesses Using Online Reviews
Authors: Wang Yong , Haleem Hammad , Shi Conglei , Wu Yanhong , Zhao Xun , Fu Siwei , Qu Huamin 
Published: July 2018

AbstractWith the rapid development of e‐commerce, there is an increasing number of online review websites, such as Yelp, to help customers make better purchase decisions. Viewing online reviews, including the rating score and text comments by other customers, and conducting a comparison between different businesses are the key to making an optimal decision. However, due to the massive amount of online reviews, the potential difference of user rating standards, and the significant variance of review time, length, details and quality, it is difficult for customers to achieve a quick and comprehensive comparison. In this paper, we present E‐Comp, a carefully‐designed visual analytics system based on online reviews, to help customers compare local businesses at different levels of details. More specifically, intuitive glyphs overlaid on maps are designed for quick candidate selection. Grouped Sankey diagram visualizing the rating difference by common customers is chosen for more reliable comparison of two businesses. Augmented word cloud showing adjective‐noun word pairs, combined with a temporal view, is proposed to facilitate in‐depth comparison of businesses in terms of different time periods, rating scores and features. The effectiveness and usability of E‐Comp are demonstrated through a case study and in‐depth user interviews.
Visualizing Expanded Query Results
Authors: Mazurek Michael , Waldner Manuela 
Published: July 2018

AbstractWhen performing queries in web search engines, users often face difficulties choosing appropriate query terms. Search engines therefore usually suggest a list of expanded versions of the user query to disambiguate it or to resolve potential term mismatches. However, it has been shown that users find it difficult to choose an expanded query from such a list. In this paper, we describe the adoption of set‐based text visualization techniques to visualize how query expansions enrich the result space of a given user query and how the result sets relate to each other. Our system uses a linguistic approach to expand queries and topic modeling to extract the most informative terms from the results of these queries. In a user study, we compare a common text list of query expansion suggestions to three set‐based text visualization techniques adopted for visualizing expanded query results – namely, Compact Euler Diagrams, Parallel Tag Clouds, and a List View – to resolve ambiguous queries using interactive query expansion. Our results show that text visualization techniques do not increase retrieval efficiency, precision, or recall. Overall, users rate Parallel Tag Clouds visualizing key terms of the expanded query space lowest. Based on the results, we derive recommendations for visualizations of query expansion results, text visualization techniques in general, and discuss alternative use cases of set‐based text visualization techniques in the context of web search.
Towards User‐Centered Active Learning Algorithms
Authors: Bernard Jürgen , Zeppelzauer Matthias , Lehmann Markus , Müller Martin , Sedlmair Michael 
Published: July 2018

AbstractThe labeling of data sets is a time‐consuming task, which is, however, an important prerequisite for machine learning and visual analytics. Visual‐interactive labeling (VIAL) provides users an active role in the process of labeling, with the goal to combine the potentials of humans and machines to make labeling more efficient. Recent experiments showed that users apply different strategies when selecting instances for labeling with visual‐interactive interfaces. In this paper, we contribute a systematic quantitative analysis of such user strategies. We identify computational building blocks of user strategies, formalize them, and investigate their potentials for different machine learning tasks in systematic experiments. The core insights of our experiments are as follows. First, we identified that particular user strategies can be used to considerably mitigate the bootstrap (cold start) problem in early labeling phases. Second, we observed that they have the potential to outperform existing active learning strategies in later phases. Third, we analyzed the identified core building blocks, which can serve as the basis for novel selection strategies. Overall, we observed that data‐based user strategies (clusters, dense areas) work considerably well in early phases, while model‐based user strategies (e.g., class separation) perform better during later phases. The insights gained from this work can be applied to develop novel active learning approaches as well as to better guide users in visual interactive labeling.
Design Factors for Summary Visualization in Visual Analytics
Authors: Sarikaya A. , Gleicher M. , Szafir D. A. 
Published: July 2018

AbstractData summarization allows analysts to explore datasets that may be too complex or too large to visualize in detail. Designers face a number of design and implementation choices when using summarization in visual analytics systems. While these choices influence the utility of the resulting system, there are no clear guidelines for the use of these summarization techniques. In this paper, we codify summarization use in existing systems to identify key factors in the design of summary visualizations. We use quantitative content analysis to systematically survey examples of visual analytics systems and enumerate the use of these design factors in data summarization. Through this analysis, we expose the relationship between design considerations, strategies for data summarization in visualization systems, and how different summarization methods influence the analyses supported by systems. We use these results to synthesize common patterns in real‐world use of summary visualizations and highlight open challenges and opportunities that these patterns offer for designing effective systems. This work provides a more principled understanding of design practices for summary visualization and offers insight into underutilized approaches.
Explorative Blood Flow Visualization using Dynamic Line Filtering based on Surface Features
Authors: Behrendt B. , Berg P. , Beuing O. , Preim B. , Saalfeld S. 
Published: July 2018

AbstractRupture risk assessment is a key to devise patient‐specific treatment plans of cerebral aneurysms. To understand and predict the development of aneurysms and other vascular diseases over time, both hemodynamic flow patterns and their effect on the vessel surface need to be analyzed. Flow structures close to the vessel wall often correlate directly with local changes in surface parameters, such as pressure or wall shear stress. Yet, in many existing applications, the analyses of flow and surface features are either somewhat detached from one another or only globally available. Especially for the identification of specific blood flow characteristics that cause local startling parameters on the vessel surface, like elevated pressure values, an interactive analysis tool is missing.The explorative visualization of flow data is challenging due to the complexity of the underlying data. In order to find meaningful structures in the entirety of the flow, the data has to be filtered based on the respective explorative aim. In this paper, we present a combination of visualization, filtering and interaction techniques for explorative analysis of blood flow with a focus on the relation of local surface parameters and underlying flow structures. Coherent bundles of pathlines can be interactively selected based on their relation to features of the vessel wall and further refined based on their own hemodynamic features. This allows the user to interactively select and explore flow structures locally affecting a certain region on the vessel wall and therefore to understand the cause and effect relationship between these entities. Additionally, multiple selected flow structures can be compared with respect to their quantitative parameters, such as flow speed. We confirmed the usefulness of our approach by conducting an informal interview with two expert neuroradiologists and an expert in flow simulation. In addition, we recorded several insights the neuroradiologists were able to gain with the help of our tool.
Visual and quantitative analysis of great arteries’ blood flow jets in cardiac 4D PC‐MRI data
Authors: Köhler B. , Grothoff M. , Gutberlet M. , Preim B. 
Published: July 2018

AbstractFlow in the great arteries (aorta, pulmonary artery) is normally laminar with a parabolic velocity profile. Eccentric flow jets are linked to various diseases like aneurysms. Cardiac 4D PC‐MRI data provide spatio‐temporally resolved blood flow information for the whole cardiac cycle. In this work, we establish a time‐dependent visualization and quantification of flow jets. For this purpose, equidistant measuring planes are automatically placed along the vessel's centerline. The flow jet position and region with highest velocities are extracted for every plane in each time step. This is done during pre‐processing and without user‐defined parameters. We visualize the main flow jet as geometric tube. High‐velocity areas are depicted as a net around this tube. Both geometries are time‐dependent and can be animated. Quantitative values are provided during cross‐sectional measuring plane‐based evaluation. Moreover, we offer a plot visualization as summary of flow jet characteristics for the selected plane. Our physiologically plausible results are in accordance with medical findings. Our clinical collaborators appreciate the possibility to view the flow jet in the whole vessel at once, which normally requires repeated pathline filtering due to varying velocities along the vessel course. The overview plots are considered as valuable for documentation purposes.
Hypersliceplorer: Interactive visualization of shapes in multiple dimensions
Authors: Torsney‐Weir T. , Möller T. , Sedlmair M. , Kirby R. M. 
Published: July 2018

AbstractIn this paper we present Hypersliceplorer, an algorithm for generating 2D slices of multi‐dimensional shapes defined by a simplical mesh. Often, slices are generated by using a parametric form and then constraining parameters to view the slice. In our case, we developed an algorithm to slice a simplical mesh of any number of dimensions with a two‐dimensional slice. In order to get a global appreciation of the multi‐dimensional object, we show multiple slices by sampling a number of different slicing points and projecting the slices into a single view per dimension pair. These slices are shown in an interactive viewer which can switch between a global view (all slices) and a local view (single slice). We show how this method can be used to study regular polytopes, differences between spaces of polynomials, and multi‐objective optimization surfaces.
Exploring High‐Dimensional Structure via Axis‐Aligned Decomposition of Linear Projections
Authors: Thiagarajan J. J. , Liu S. , Ramamurthy K. N. , Bremer P.‐T. 
Published: July 2018

AbstractTwo‐dimensional embeddings remain the dominant approach to visualize high dimensional data. The choice of embeddings ranges from highly non‐linear ones, which can capture complex relationships but are difficult to interpret quantitatively, to axis‐aligned projections, which are easy to interpret but are limited to bivariate relationships. Linear project can be considered as a compromise between complexity and interpretability, as they allow explicit axes labels, yet provide significantly more degrees of freedom compared to axis‐aligned projections. Nevertheless, interpreting the axes directions, which are often linear combinations of many non‐trivial components, remains difficult. To address this problem we introduce a structure aware decomposition of (multiple) linear projections into sparse sets of axis‐aligned projections, which jointly capture all information of the original linear ones. In particular, we use tools from Dempster‐Shafer theory to formally define how relevant a given axis‐aligned project is to explain the neighborhood relations displayed in some linear projection. Furthermore, we introduce a new approach to discover a diverse set of high quality linear projections and show that in practice the information of k linear projections is often jointly encoded in ∼ k axis‐aligned plots. We have integrated these ideas into an interactive visualization system that allows users to jointly browse both linear projections and their axis‐aligned representatives. Using a number of case studies we show how the resulting plots lead to more intuitive visualizations and new insights.
Visualizing Multidimensional Data with Order Statistics
Authors: Raj M. , Whitaker R. T. 
Published: July 2018

AbstractMultidimensional data sets are common in many domains, and dimensionality reduction methods that determine a lower dimensional embedding are widely used for visualizing such data sets. This paper presents a novel method to project data onto a lower dimensional space by taking into account the order statistics of the individual data points, which are quantified by their depth or centrality in the overall set. Thus, in addition to conveying relative distances in the data, the proposed method also preserves the order statistics, which are often lost or misrepresented by existing visualization methods. The proposed method entails a modification of the optimization objective of conventional multidimensional scaling (MDS) by introducing a term that penalizes discrepancies between centrality structures in the original space and the embedding. We also introduce two strategies for visualizing lower dimensional embeddings of multidimensional data that takes advantage of the coherent representation of centrality provided by the proposed projection method. We demonstrate the effectiveness of our visualization with comparisons on different kinds of multidimensional data, including categorical and multimodal, from a variety of domains such as botany and health care.