bluuebunny/crossref_metadata_2025_split huggingface dataset:
Leptospiral lipopolysaccharide stimulates the expression of toll‐like receptor 2 and cytokines in pig fibroblasts
Authors: Guo Yijie , Fukuda Tomokazu , Donai Kenichiro , Kuroda Kengo , Masuda Mizuki , Nakamura Shuichi , Yoneyama Hiroshi , Isogai Emiko 
Published: July 2014

AbstractPigs throughout the world are afflicted with leptospirosis, causing serious economic losses and potential hazards to human health. Although it has been known that leptospiral lipopolysaccharide (L‐LPS) is involved in an immunological reaction between an antigen and a host cell, little is known about how the immune system of pigs can respond to L‐LPS. Here, we stimulated pig fibroblasts by L‐LPS and then quantitatively measured gene and protein expression levels of two toll‐like receptors (TLRs), TLR2 and TLR4, by real‐time PCR and Western blotting. As a result, expression of TLR2 was found to be significantly up‐regulated within 24 h after L‐LPS stimulation whereas induction of TLR4 expression was relatively weak. We also revealed that of myeloid differentiation primary response gene 88 (MyD88), interleukin 6 (IL‐6) and IL‐8 gene expressions were markedly up‐regulated by L‐LPS stimulation. These results may suggest that the pig cell can activate TLR2 rather than TLR4 by L‐LPS stimulation, thereby inducing expression of cytokines.
Perceived therapist genuineness predicts therapeutic alliance in cognitive behavioural therapy for psychosis
Authors: Jung Esther , Wiesjahn Martin , Rief Winfried , Lincoln Tania M. 
Published: July 2014

ObjectivesThe quality of therapeutic alliance is a consistent and stable predictor of therapy outcome. Recent studies have shown therapist characteristics to be relevant predictors of the alliance in psychological therapies in general. However, little is known about the specific therapist characteristics that explain differences in therapeutic alliance in cognitive behavioural therapy for psychosis (CBTp). The aim of this study was to identify relevant therapist characteristics that predict early therapeutic alliance in CBTp.Design and methodsForty‐eight patients with a DSM‐IV diagnosis of a psychotic disorder participating in a CBTp trial and 11 therapists were included in the analysis. Therapist characteristics as perceived by the patients (empathy, genuineness, positive regard, competence, and convincingness) were assessed at baseline. Alliance was assessed after the fifth therapy session. Data were analyzed using bivariate correlations and multivariate hierarchic regression analysis.ResultsAll therapist characteristics were positively associated with patient‐rated alliance. Patient characteristics were not significantly associated with alliance and did not predict alliance in the multivariate analysis. Regression analysis revealed therapist genuineness and competence to significantly predict higher patient‐rated alliance.ConclusionsOur results suggest that perceived therapist genuineness is the most relevant predictor of patient‐rated therapeutic alliance in CBTp. Future trials using control samples with other mental disorders could clarify whether this finding is specific to CBTp. Therapist training concepts for increasing beneficial therapist qualities are needed.Practitioner points

The patients' perception of the therapist as empathic, genuine, accepting, competent and convincing is associated with therapeutic alliance in CBTp.

Perceived therapist genuineness and competence are the most relevant predictors of patient‐rated therapeutic alliance.

Training and supervision should focus on increasing basic therapist qualities.

Limitations of this study include homogeneity of therapists, small sample size, and lack of a control group.
Development and validation of a model to predict platelet response to romiplostim in patients with lower‐risk myelodysplastic syndromes
Authors: Sekeres Mikkael A. , Giagounidis Aristoteles , Kantarjian Hagop , Mufti Ghulam J. , Fenaux Pierre , Jia Catherine , Yang Allen S. , Platzbecker Uwe 
Published: July 2014

SummaryLow endogenous erythropoietin levels and limited red blood cell transfusion history can predict response to erythropoiesis‐stimulating agents in anaemic patients with myelodysplastic syndromes (MDS). The relationship between endogenous thrombopoietin (THPO) levels and platelet response to romiplostim is unknown. Variables including baseline endogenous THPO levels, transfusion needs, and platelet response were analysed in a randomized trial of 250 thrombocytopenic, lower‐risk MDS patients (International Prognostic Scoring System low/intermediate‐1). A predictive scoring system was developed based on log–likelihood ratios and logistic coefficients. Patients with HI–P (haematological improvement – platelets) responses had lower mean baseline THPO levels (P = 0·0497) and were more likely to have <6 platelet units transfused in the past year (P = 0·0027), as did patients with platelet responses ≥50% of weeks on romiplostim (P = 0·001 and P = 0·0037, respectively). A model for predicting response to romiplostim was developed and validated in a separate MDS cohort (N = 72). Patients in low‐, intermediate‐, and high‐response groups had response rates of 17·4%, 29·6%, and 50·7%, respectively, for HI‐P, and 17·4%, 33·8%, and 65·2%, respectively, for ≥50% response. For thrombocytopenic patients with lower‐risk MDS, lower baseline THPO levels (<500 pg/ml) and limited platelet transfusion history predicted a greater likelihood of a subsequent platelet response to romiplostim.
Metabolic characteristics of 13‐cis‐retinoic acid (isotretinoin) and anti‐tumour activity of the 13‐cis‐retinoic acid metabolite 4‐oxo‐13‐cis‐retinoic acid in neuroblastoma
Authors: Sonawane Poonam , Cho Hwang Eui , Tagde Ashujit , Verlekar Dattesh , Yu Alice L , Reynolds C Patrick , Kang Min H 
Published: July 2014

Background and PurposeIsotretinoin (13‐cis‐retinoic acid; 13‐cRA) is a differentiation inducer used to treat minimal residual disease after myeloablative therapy for high‐risk neuroblastoma. However, more than 40% of children develop recurrent disease during or after 13‐cRA treatment. The plasma concentrations of 13‐cRA in earlier studies were considered subtherapeutic while 4‐oxo‐13‐cis‐RA (4‐oxo‐13‐cRA), a metabolite of 13‐cRA considered by some investigators as inactive, were greater than threefold higher than 13‐cRA. We sought to define the metabolic pathways of 13‐cRA and investigated the anti‐tumour activity of its major metabolite, 4‐oxo‐13‐cRA.Experimental ApproachEffects of 13‐cRA and 4‐oxo‐13‐cRA on human neuroblastoma cell lines were assessed by DIMSCAN and flow cytometry for cell proliferation, MYCN down‐regulation by reverse transcription PCR and immunoblotting, and neurite outgrowth by confocal microscopy. 13‐cRA metabolism was determined using tandem MS in human liver microsomes and in patient samples.Key ResultsSix major metabolites of 13‐cRA were identified in patient samples. Of these, 4‐oxo‐13‐cRA was the most abundant, and 4‐oxo‐13‐cRA glucuronide was also detected at a higher level in patients. CYP3A4 was shown to play a major role in catalysing 13‐cRA to 4‐oxo‐13‐cRA. In human neuroblastoma cell lines, 4‐oxo‐13‐cRA and 13‐cRA were equi‐effective at inducing neurite outgrowth, inhibiting proliferation, decreasing MYCN mRNA and protein, and increasing the expression of retinoic acid receptor‐β mRNA and protein levels.Conclusions and ImplicationsWe showed that 4‐oxo‐13‐cRA is as active as 13‐cRA against neuroblastoma cell lines. Plasma levels of both 13‐cRA and 4‐oxo‐13‐cRA should be evaluated in pharmacokinetic studies of isotretinoin in neuroblastoma.
Attachment‐Related Biases in Adolescents’ Memory
Authors: Dykas Matthew J. , Woodhouse Susan S. , Jones Jason D. , Cassidy Jude 
Published: July 2014

Attachment theorists propose that individuals’ internal working models influence their social information processing. This study explored links between attachment representations and social information processing by examining adolescents’ (n = 189; Mage = 16.5 years) attachment‐related memory biases. Participants completed laboratory tasks assessing memory for (a) emotionally salient childhood events, (b) adjectives describing their parents, and (c) generalized parent‐related characteristics not specific to their own parents. As expected, dismissing attachment (assessed using the Adult Attachment Interview) was linked across tasks to a deactivating strategy in which memory for emotional childhood events and attachment‐relevant stimuli was reduced. In contrast, evidence that preoccupied attachment was linked to a hyperactivating strategy in which memory was heightened emerged only in relation to emotional childhood events.
Sets of Globally Optimal Stream Surfaces for Flow Visualization
Authors: Schulze M. , Esturo J. Martinez , Günther T. , Rössl C. , Seidel H.‐P. , Weinkauf T. , Theisel H. 
Published: July 2014

AbstractStream surfaces are a well‐studied and widely used tool for the visualization of 3D flow fields. Usually, stream surface seeding is carried out manually in time‐consuming trial and error procedures. Only recently automatic selection methods were proposed. Local methods support the selection of a set of stream surfaces, but, contrary to global selection methods, they evaluate only the quality of the seeding lines but not the quality of the whole stream surfaces. Global methods, on the other hand, only support the selection of a single optimal stream surface until now. However, for certain flow fields a single stream surface is not sufficient to represent all flow features.In our work, we overcome this limitation by introducing a global selection technique for a set of stream surfaces. All selected surfaces optimize global stream surface quality measures and are guaranteed to be mutually distant, such that they can convey different flow features. Our approach is an efficient extension of the most recent global selection method for single stream surfaces. We illustrate its effectiveness on a number of analytical and simulated flow fields and analyze the quality of the results in a user study.
Opacity Optimization for Surfaces
Authors: Günther Tobias , Schulze Maik , Esturo Janick Martinez , Rössl Christian , Theisel Holger 
Published: July 2014

AbstractIn flow visualization, integral surfaces rapidly tend to expand, fold and produce vast amounts of occlusion. While silhouette enhancements and local transparency mappings proved useful for semi‐transparent depictions, they still introduce visual clutter when surfaces grow more complex. An effective visualization of the flow requires a balance between the presentation of interesting surface parts and the avoidance of occlusions that hinder the view. In this paper, we extend the concept of opacity optimization to surfaces to obtain a global approach to the occlusion problem. Starting with a partition of the surfaces into patches, we compute per‐patch opacity as minimizer of a bounded‐variable least‐squares problem. For the final rendering, opacity is interpolated on the surfaces. The resulting visualization technique is interactive, frame‐coherent, view‐dependent and driven by domain knowledge.
Parallel Irradiance Caching for Interactive Monte‐Carlo Direct Volume Rendering
Authors: Khlebnikov Rostislav , Voglreiter Philip , Steinberger Markus , Kainz Bernhard , Schmalstieg Dieter 
Published: July 2014

AbstractWe propose a technique to build the irradiance cache for isotropic scattering simultaneously with Monte Carlo progressive direct volume rendering on a single GPU, which allows us to achieve up to four times increased convergence rate for complex scenes with arbitrary sources of light. We use three procedures that run concurrently on a single GPU. The first is the main rendering procedure. The second procedure computes new cache entries, and the third one corrects the errors that may arise after creation of new cache entries. We propose two distinct approaches to allow massive parallelism of cache entry creation. In addition, we show a novel extrapolation approach which outputs high quality irradiance approximations and a suitable prioritization scheme to increase the convergence rate by dedicating more computational power to more complex rendering areas.
Distortion‐Guided Structure‐Driven Interactive Exploration of High‐Dimensional Data
Authors: Liu S. , Wang B. , Bremer P.‐T. , Pascucci V. 
Published: July 2014

AbstractDimension reduction techniques are essential for feature selection and feature extraction of complex high‐dimensional data. These techniques, which construct low‐dimensional representations of data, are typically geometrically motivated, computationally efficient and approximately preserve certain structural properties of the data. However, they are often used as black box solutions in data exploration and their results can be difficult to interpret. To assess the quality of these results, quality measures, such as co‐ranking [LV09], have been proposed to quantify structural distortions that occur between high‐dimensional and low‐dimensional data representations. Such measures could be evaluated and visualized point‐wise to further highlight erroneous regions [MLGH13]. In this work, we provide an interactive visualization framework for exploring high‐dimensional data via its two‐dimensional embeddings obtained from dimension reduction, using a rich set of user interactions. We ask the following question: what new insights do we obtain regarding the structure of the data, with interactive manipulations of its embeddings in the visual space? We augment the two‐dimensional embeddings with structural abstractions obtained from hierarchical clusterings, to help users navigate and manipulate subsets of the data. We use point‐wise distortion measures to highlight interesting regions in the domain, and further to guide our selection of the appropriate level of clusterings that are aligned with the regions of interest. Under the static setting, point‐wise distortions indicate the level of structural uncertainty within the embeddings. Under the dynamic setting, on‐the‐fly updates of point‐wise distortions due to data movement and data deletion reflect structural relations among different parts of the data, which may lead to new and valuable insights.
GuideME: Slice‐guided Semiautomatic Multivariate Exploration of Volumes
Authors: Zhou L. , Hansen C. 
Published: July 2014

AbstractMultivariate volume visualization is important for many applications including petroleum exploration and medicine. State‐of‐the‐art tools allow users to interactively explore volumes with multiple linked parameter‐space views. However, interactions in the parameter space using trial‐and‐error may be unintuitive and time consuming. Furthermore, switching between different views may be distracting. In this paper, we propose GuideME: a novel slice‐guided semiautomatic multivariate volume exploration approach. Specifically, the approach comprises four stages: attribute inspection, guided uncertainty‐aware lasso creation, automated feature extraction and optional spatial fine tuning and visualization. Throughout the exploration process, the user does not need to interact with the parameter views at all and examples of complex real‐world data demonstrate the usefulness, efficiency and ease‐of‐use of our method.
Comparative Exploration of Document Collections: a Visual Analytics Approach
Authors: Oelke D. , Strobelt H. , Rohrdantz C. , Gurevych I. , Deussen O. 
Published: July 2014

AbstractWe present an analysis and visualization method for computing what distinguishes a given document collection from others. We determine topics that discriminate a subset of collections from the remaining ones by applying probabilistic topic modeling and subsequently approximating the two relevant criteria distinctiveness and characteristicness algorithmically through a set of heuristics. Furthermore, we suggest a novel visualization method called DiTop‐View, in which topics are represented by glyphs (topic coins) that are arranged on a 2D plane. Topic coins are designed to encode all information necessary for performing comparative analyses such as the class membership of a topic, its most probable terms and the discriminative relations. We evaluate our topic analysis using statistical measures and a small user experiment and present an expert case study with researchers from political sciences analyzing two real‐world datasets.
A Gaze‐enabled Graph Visualization to Improve Graph Reading Tasks
Authors: Okoe Mershack , Alam Sayeed Safayet , Jianu Radu 
Published: July 2014

AbstractPerforming typical network tasks such as node scanning and path tracing can be difficult in large and dense graphs. To alleviate this problem we use eye‐tracking as an interactive input to detect tasks that users intend to perform and then produce unobtrusive visual changes that support these tasks. First, we introduce a novel fovea based filtering that dims out edges with endpoints far removed from a user's view focus. Second, we highlight edges that are being traced at any given moment or have been the focus of recent attention. Third, we track recently viewed nodes and increase the saliency of their neighborhoods. All visual responses are unobtrusive and easily ignored to avoid unintentional distraction and to account for the imprecise and low‐resolution nature of eye‐tracking. We also introduce a novel gaze‐correction approach that relies on knowledge about the network layout to reduce eye‐tracking error. Finally, we present results from a controlled user study showing that our methods led to a statistically significant accuracy improvement in one of two network tasks and that our gaze‐correction algorithm enables more accurate eye‐tracking interaction.
SimilarityExplorer: A Visual Inter‐Comparison Tool for Multifaceted Climate Data
Authors: Poco J. , Dasgupta A. , Wei Y. , Hargrove W. , Schwalm C. , Cook R. , Bertini E. , Silva C. 
Published: July 2014

AbstractInter‐comparison and similarity analysis to gauge consensus among multiple simulation models is a critical visualization problem for understanding climate change patterns. Climate models, specifically, Terrestrial Biosphere Models (TBM) represent time and space variable ecosystem processes, like, simulations of photosynthesis and respiration, using algorithms and driving variables such as climate and land use. While it is widely accepted that interactive visualization can enable scientists to better explore model similarity from different perspectives and different granularity of space and time, currently there is a lack of such visualization tools.In this paper we present three main contributions. First, we propose a domain characterization for the TBM community by systematically defining the domain‐specific intents for analyzing model similarity and characterizing the different facets of the data. Second, we define a classification scheme for combining visualization tasks and multiple facets of climate model data in one integrated framework, which can be leveraged for translating the tasks into the visualization design. Finally, we present SimilarityExplorer, an exploratory visualization tool that facilitates similarity comparison tasks across both space and time through a set of coordinated multiple views. We present two case studies from three climate scientists, who used our tool for a month for gaining scientific insights into model similarity. Their experience and results validate the effectiveness of our tool.
Lyra: An Interactive Visualization Design Environment
Authors: Satyanarayan Arvind , Heer Jeffrey 
Published: July 2014

AbstractWe present Lyra, an interactive environment for designing customized visualizations without writing code. Using drag‐and‐drop interactions, designers can bind data to the properties of graphical marks to author expressive visualization designs. Marks can be moved, rotated and resized using handles; relatively positioned using connectors; and parameterized by data fields using property drop zones. Lyra also provides a data pipeline interface for iterative, visual specification of data transformations and layout algorithms. Visualizations created with Lyra are represented as specifications in Vega, a declarative visualization grammar that enables sharing and reuse. We evaluate Lyra's expressivity and accessibility through diverse examples and studies with journalists and visualization designers. We find that Lyra enables users to rapidly develop customized visualizations, covering a design space comparable to existing programming‐based tools.
Authoring Narrative Visualizations with Ellipsis
Authors: Satyanarayan Arvind , Heer Jeffrey 
Published: July 2014

AbstractData visualization is now a popular medium for journalistic storytelling. However, current visualization tools either lack support for storytelling or require significant technical expertise. Informed by interviews with journalists, we introduce a model of storytelling abstractions that includes state‐based scene structure, dynamic annotations and decoupled coordination of multiple visualization components. We instantiate our model in Ellipsis: a system that combines a domain‐specific language (DSL) for storytelling with a graphical interface for story authoring. User interactions are automatically translated into statements in the Ellipsis DSL. By enabling storytelling without programming, the Ellipsis interface lowers the threshold for authoring narrative visualizations. We evaluate Ellipsis through example applications and user studies with award‐winning journalists. Study participants find Ellipsis to be a valuable prototyping tool that can empower journalists in the creation of interactive narratives.
Visual Analysis of Sets of Heterogeneous Matrices Using Projection‐Based Distance Functions and Semantic Zoom
Authors: Behrisch Michael , Davey James , Fischer Fabian , Thonnard Olivier , Schreck Tobias , Keim Daniel , Kohlhammer Jörn 
Published: July 2014

AbstractMatrix visualization is an established technique in the analysis of relational data. It is applicable to large, dense networks, where node‐link representations may not be effective. Recently, domains have emerged in which the comparative analysis of sets of matrices of potentially varying size is relevant. For example, to monitor computer network traffic a dynamic set of hosts and their peer‐to‐peer connections on different ports must be analysed. A matrix visualization focused on the display of one matrix at a time cannot cope with this task.We address the research problem of the visual analysis of sets of matrices. We present a technique for comparing matrices of potentially varying size. Our approach considers the rows and/or columns of a matrix as the basic elements of the analysis. We project these vectors for pairs of matrices into a low‐dimensional space which is used as the reference to compare matrices and identify relationships among them. Bipartite graph matching is applied on the projected elements to compute a measure of distance. A key advantage of this measure is that it can be interpreted and manipulated as a visual distance function, and serves as a comprehensible basis for ranking, clustering and comparison in sets of matrices. We present an interactive system in which users may explore the matrix distances and understand potential differences in a set of matrices. A flexible semantic zoom mechanism enables users to navigate through sets of matrices and identify patterns at different levels of detail. We demonstrate the effectiveness of our approach through a case study and provide a technical evaluation to illustrate its strengths.