bluuebunny/crossref_metadata_2025_split huggingface dataset:
Long‐term consequences of breastfeeding on cholesterol, obesity, systolic blood pressure and type 2 diabetes: a systematic review and meta‐analysis
Authors: Horta Bernardo L. , Loret de Mola Christian , Victora Cesar G. 
Published: July 2015

AbstractAimTo systematically review the evidence on the associations between breastfeeding and overweight/obesity, blood pressure, total cholesterol and type 2 diabetes.MethodsTwo independent literature searches were carried out using the MEDLINE, LILACS, SCIELO and Web of Science databases. Studies restricted to infants and those without an internal comparison group were excluded. Fixed‐ and random‐effects models were used to pool the estimates.ResultsBreastfed subjects were less likely to be considered obese/overweight [pooled odds ratio: 0.74 (95% confidence interval (CI): 0.70; 0.78)] (n = 113). Among the 11 high‐quality studies, the association was smaller [pooled odds ratio: 0.87 (95%CI: 0.76; 0.99)]. Total cholesterol (n = 46) was independent of breastfeeding [pooled mean difference: −0.01 mmol/L (95%CI: −0.05; 0.02)]. Systolic blood pressure (n = 43) was lower among breastfed subjects [mean difference: −0.80 (95%CI: −1.17; −0.43)], but no association was observed among larger studies, and for diastolic blood pressure (n = 38) [mean difference: −0.24 (95%CI: −0.50; 0.02)]. For type 2 diabetes (n = 11), the odds ratio was lower among those subjects who had been breastfed [pooled odds ratio: 0.65 (95%CI: 0.49; 0.86)].ConclusionBreastfeeding decreased the odds of type 2 diabetes and based on high‐quality studies, decreased by 13% the odds of overweight/obesity. No associations were found for total cholesterol or blood pressure.
Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes
Authors: Zhang Yichi , Laber Eric B. , Tsiatis Anastasios , Davidian Marie 
Published: July 2015

SummaryA treatment regime formalizes personalized medicine as a function from individual patient characteristics to a recommended treatment. A high-quality treatment regime can improve patient outcomes while reducing cost, resource consumption, and treatment burden. Thus, there is tremendous interest in estimating treatment regimes from observational and randomized studies. However, the development of treatment regimes for application in clinical practice requires the long-term, joint effort of statisticians and clinical scientists. In this collaborative process, the statistician must integrate clinical science into the statistical models underlying a treatment regime and the clinician must scrutinize the estimated treatment regime for scientific validity. To facilitate meaningful information exchange, it is important that estimated treatment regimes be interpretable in a subject-matter context. We propose a simple, yet flexible class of treatment regimes whose members are representable as a short list of if–then statements. Regimes in this class are immediately interpretable and are therefore an appealing choice for broad application in practice. We derive a robust estimator of the optimal regime within this class and demonstrate its finite sample performance using simulation experiments. The proposed method is illustrated with data from two clinical trials.
Merging Multiple Longitudinal Studies with Study-Specific Missing Covariates: A Joint Estimating Function Approach
Authors: Wang Fei , Song Peter X.-K. , Wang Lu 
Published: July 2015

SummaryMerging multiple datasets collected from studies with identical or similar scientific objectives is often undertaken in practice to increase statistical power. This article concerns the development of an effective statistical method that enables to merge multiple longitudinal datasets subject to various heterogeneous characteristics, such as different follow-up schedules and study-specific missing covariates (e.g., covariates observed in some studies but missing in other studies). The presence of study-specific missing covariates presents great statistical methodology challenge in data merging and analysis. We propose a joint estimating function approach to addressing this challenge, in which a novel nonparametric estimating function constructed via splines-based sieve approximation is utilized to bridge estimating equations from studies with missing covariates to those with fully observed covariates. Under mild regularity conditions, we show that the proposed estimator is consistent and asymptotically normal. We evaluate finite-sample performances of the proposed method through simulation studies. In comparison to the conventional multiple imputation approach, our method exhibits smaller estimation bias. We provide an illustrative data analysis using longitudinal cohorts collected in Mexico City to assess the effect of lead exposures on children's somatic growth.
Precortical Phase of Alzheimer's Disease (AD)‐Related Tau Cytoskeletal Pathology
Authors: Stratmann Katharina , Heinsen Helmut , Korf Horst‐Werner , Del Turco Domenico , Ghebremedhin Estifanos , Seidel Kay , Bouzrou Mohamed , Grinberg Lea T. , Bohl Jürgen , Wharton Stephen B. , den Dunnen Wilfred , Rüb Udo 
Published: July 2015

AbstractAlzheimer's disease (AD) represents the most frequent progressive neuropsychiatric disorder worldwide leading to dementia. We systematically investigated the presence and extent of the AD‐related cytoskeletal pathology in serial thick tissue sections through all subcortical brain nuclei that send efferent projections to the transentorhinal and entorhinal regions in three individuals with Braak and Braak AD stage 0 cortical cytoskeletal pathology and fourteen individuals with Braak and Braak AD stage I cortical cytoskeletal pathology by means of immunostainings with the anti‐tau antibody AT8. These investigations revealed consistent AT8 immunoreactive tau cytoskeletal pathology in a subset of these subcortical nuclei in the Braak and Braak AD stage 0 individuals and in all of these subcortical nuclei in the Braak and Braak AD stage I individuals. The widespread affection of the subcortical nuclei in Braak and Braak AD stage I shows that the extent of the early subcortical tau cytoskeletal pathology has been considerably underestimated previously. In addition, our novel findings support the concept that subcortical nuclei become already affected during an early ‘pre‐cortical’ evolutional phase before the first AD‐related cytoskeletal changes occur in the mediobasal temporal lobe (i.e. allocortical transentorhinal and entorhinal regions). The very early involved subcortical brain regions may represent the origin of the AD‐related tau cytoskeletal pathology, from where the neuronal cytoskeletal pathology takes an ascending course toward the secondarily affected allocortex and spreads transneuronally along anatomical pathways in predictable sequences.
Congenital adrenal hyperplasia with localized aggressive periodontitis and amelogenesis imperfecta
Authors: Ajlan Sumaiah Abdulbaqi 
Published: July 2015

AbstractCongenital adrenal hyperplasia (CAH) is an inherited medical condition that implies defects in steroid biosynthesis. The dental findings of a female patient with CAH are reported. The patient suffered from severe periodontal tissue destruction, obvious enamel defects, as well as some occlusal problems. The management approach is presented and the possibility of interrelation of her dental findings with her medical condition is discussed.
Visual Analytics for the Exploration of Tumor Tissue Characterization
Authors: Raidou R.G. , van der Heide U.A. , Dinh C.V. , Ghobadi G. , Kallehauge J.F. , Breeuwer M. , Vilanova A. 
Published: July 2015

AbstractTumors are heterogeneous tissues consisting of multiple regions with distinct characteristics. Characterization of these intra‐tumor regions can improve patient diagnosis and enable a better targeted treatment. Ideally, tissue characterization could be performed non‐invasively, using medical imaging data, to derive per voxel a number of features, indicative of tissue properties. However, the high dimensionality and complexity of this imaging‐derived feature space is prohibiting for easy exploration and analysis ‐ especially when clinical researchers require to associate observations from the feature space to other reference data, e.g., features derived from histopathological data. Currently, the exploratory approach used in clinical research consists of juxtaposing these data, visually comparing them and mentally reconstructing their relationships. This is a time consuming and tedious process, from which it is difficult to obtain the required insight. We propose a visual tool for: (1) easy exploration and visual analysis of the feature space of imaging‐derived tissue characteristics and (2) knowledge discovery and hypothesis generation and confirmation, with respect to reference data used in clinical research. We employ, as central view, a 2D embedding of the imaging‐derived features. Multiple linked interactive views provide functionality for the exploration and analysis of the local structure of the feature space, enabling linking to patient anatomy and clinical reference data. We performed an initial evaluation with ten clinical researchers. All participants agreed that, unlike current practice, the proposed visual tool enables them to identify, explore and analyze heterogeneous intra‐tumor regions and particularly, to generate and confirm hypotheses, with respect to clinical reference data.
Learning Probabilistic Transfer Functions: A Comparative Study of Classifiers
Authors: Soundararajan K. P. , Schultz T. 
Published: July 2015

AbstractComplex volume rendering tasks require high‐dimensional transfer functions, which are notoriously difficult to design. One solution to this is to learn transfer functions from scribbles that the user places in the volumetric domain in an intuitive and natural manner. In this paper, we explicitly model and visualize the uncertainty in the resulting classification. To this end, we extend a previous intelligent system approach to volume rendering, and we systematically compare five supervised classification techniques – Gaussian Naive Bayes, k Nearest Neighbor, Support Vector Machines, Neural Networks, and Random Forests – with respect to probabilistic classification, support for multiple materials, interactive performance, robustness to unreliable input, and easy parameter tuning, which we identify as key requirements for the successful use in this application. Based on theoretical considerations, as well as quantitative and visual results on volume datasets from different sources and modalities, we conclude that, while no single classifier can be expected to outperform all others under all circumstances, random forests are a useful off‐the‐shelf technique that provides fast, easy, robust and accurate results in many scenarios.
Rule‐Enhanced Transfer Function Generation for Medical Volume Visualization
Authors: Cai Li‐Le , Nguyen Binh P. , Chui Chee‐Kong , Ong Sim‐Heng 
Published: July 2015

AbstractIn volume visualization, transfer functions are used to classify the volumetric data and assign optical properties to the voxels. In general, transfer functions are generated in a transfer function space, which is the feature space constructed by data values and properties derived from the data. If volumetric objects have the same or overlapping data values, it would be difficult to separate them in the transfer function space. In this paper, we present a rule‐enhanced transfer function design method that allows important structures of the volume to be more effectively separated and highlighted. We define a set of rules based on the local frequency distribution of volume attributes. A rule‐selection method based on a genetic algorithm is proposed to learn the set of rules that can distinguish the user‐specified target tissue from other tissues. In the rendering stage, voxels satisfying these rules are rendered with higher opacities in order to highlight the target tissue. The proposed method was tested on various volumetric datasets to enhance the visualization of important structures that are difficult to be visualized by traditional transfer function design methods. The results demonstrate the effectiveness of the proposed method.
Edge‐Aware Volume Smoothing Using L0 Gradient Minimization
Authors: Wang Qichao , Tao Yubo , Lin Hai 
Published: July 2015

AbstractIn volume visualization, noise in regions of homogeneous material and at boundaries between different materials poses a great challenge in extracting, analyzing and rendering features of interest. In this paper, we present a novel volume denoising / smoothing method based on the L0 gradient minimization framework. This framework globally controls how many voxels with a non‐zero gradient are in the result in order to approximate important features’ structures in a sparse way. This procedure can be solved quickly by the alternating optimization strategy with half‐quadratic splitting. While the proposed L0 volume gradient minimization method can effectively remove noise in homogeneous materials, a blurring‐sharpening strategy is proposed to diminish noise or smooth local details on the boundaries. This generates salient features with smooth boundaries and visually pleasing structures. We compare our method with the bilateral filter and anisotropic diffusion, and demonstrate the effectiveness and efficiency of our method with several volumes in different modalities.
Photoelasticity Raycasting
Authors: Bußler M. , Ertl T. , Sadlo F. 
Published: July 2015

AbstractWe present a novel physically‐based method to visualize stress tensor fields. By incorporating photoelasticity into traditional raycasting and extending it with reflection and refraction, taking into account polarization, we obtain the virtual counterpart to traditional experimental polariscopes. This allows us to provide photoelastic analysis of stress tensor fields in arbitrary domains. In our model, the optical material properties, such as stress‐optic coefficient and refractive index, can either be chosen in compliance with the subject under investigation, or, in case of stress problems that do not model optical properties or that are not transparent, be chosen according to known or even new transparent materials. This enables direct application of established polariscope methodology together with respective interpretation. Using a GPU‐based implementation, we compare our technique to experimental data, and demonstrate its utility with several simulated datasets.
Interactive Visual Analysis for Vehicle Detector Data
Authors: Chen Yi‐Cheng , Wang Yu‐Shuen , Lin Wen‐Chieh , Huang Wei‐Xiang , Lin I‐Chen 
Published: July 2015

AbstractVisualization of vehicle detection (VD) data is essential because the data play an important role in traffic control and policy development. Most previous works focus on visualizing trajectories obtained from global positioning system (GPS), which are detailed but less representative. In contrast, VD data report the traffic statistic at each sensing site during a time span, including speed, flow, and occupancy of each lane, which contain comprehensive traffic information for analysis. In this work, we visualize three‐year VD data of freeways in Taiwan. The visualization depicts the traffic situation at a site over time using a color‐coded chart that extends from left to right over time. The charts are vertically stacked and horizontally aligned according to VD's located mileage and data time, respectively, to provide global insight. Our system allows semantic zoom, which changes the chart appearance in a continuous manner, to enable macro‐ and micro‐ scopic visualizations. Analysts can explore events that span an area with different sizes and that persist a time span with various lengths. To ensure the feasibility of our visualization, before the system design, we conducted a study with experts who work in the national freeway bureau and the institute of transportation of Taiwan. We also showed our results to the experts after the prototype system was built. The feedback shows that our VD data visualization is helpful to traffic control and policy development.
Rationale Visualization for Safety and Security
Authors: Scheepens Roeland , Michels Steffen , van de Wetering Huub , van Wijk Jarke J. 
Published: July 2015

AbstractIn safety and security domains where objects of interest (OOI), such as people, vessels, or transactions, are continuously monitored, automated reasoning is required due to their sheer number and volume of information. We present a method to visually explain the rationale of a reasoning engine that raises an alarm if a certain situation is reached. Based both on evidence from heterogeneous and possibly unreliable sources, and on a domain specific reasoning structure, this engine concludes with a certain probability that, e.g., the OOI is suspected of smuggling. To support decision making, we visualize the rationale, an abstraction of the complicated reasoning structure. The evidence is displayed in a color‐coded matrix that easily reveals if and where observations contradict. In it, domain and operational experts can quickly understand and find complicated patterns and relate them to real‐world situations. Also, two groups of these experts evaluate our system through maritime use cases based on real data.
Interactive Fusion and Tracking For Multi‐Modal Spatial Data Visualization
Authors: Elshehaly M. , Gračanin D. , Gad M. , Elmongui H. G. , Matković K. 
Published: July 2015

AbstractScientific data acquired through sensors which monitor natural phenomena, as well as simulation data that imitate time‐identified events, have fueled the need for interactive techniques to successfully analyze and understand trends and patterns across space and time. We present a novel interactive visualization technique that fuses ground truth measurements with simulation results in real‐time to support the continuous tracking and analysis of spatiotemporal patterns. We start by constructing a reference model which densely represents the expected temporal behavior, and then use GPU parallelism to advect measurements on the model and track their location at any given point in time. Our results show that users can interactively fill the spatio‐temporal gaps in real world observations, and generate animations that accurately describe physical phenomena.
Visual Exploration of High‐Dimensional Data through Subspace Analysis and Dynamic Projections
Authors: Liu S. , Wang B. , Thiagarajan J. J. , Bremer P.‐T. , Pascucci V. 
Published: July 2015

AbstractWe introduce a novel interactive framework for visualizing and exploring high‐dimensional datasets based on subspace analysis and dynamic projections. We assume the high‐dimensional dataset can be represented by a mixture of low‐dimensional linear subspaces with mixed dimensions, and provide a method to reliably estimate the intrinsic dimension and linear basis of each subspace extracted from the subspace clustering. Subsequently, we use these bases to define unique 2D linear projections as viewpoints from which to visualize the data. To understand the relationships among the different projections and to discover hidden patterns, we connect these projections through dynamic projections that create smooth animated transitions between pairs of projections. We introduce the view transition graph, which provides flexible navigation among these projections to facilitate an intuitive exploration. Finally, we provide detailed comparisons with related systems, and use real‐world examples to demonstrate the novelty and usability of our proposed framework.
Visualization of Object‐Centered Vulnerability to Possible Flood Hazards
Authors: Cornel D. , Konev A. , Sadransky B. , Horváth Z. , Gröller E. , Waser J. 
Published: July 2015

AbstractAs flood events tend to happen more frequently, there is a growing demand for understanding the vulnerability of infrastructure to flood‐related hazards. Such demand exists both for flood management personnel and the general public. Modern software tools are capable of generating uncertainty‐aware flood predictions. However, the information addressing individual objects is incomplete, scattered, and hard to extract. In this paper, we address vulnerability to flood‐related hazards focusing on a specific building. Our approach is based on the automatic extraction of relevant information from a large collection of pre‐simulated flooding events, called a scenario pool. From this pool, we generate uncertainty‐aware visualizations conveying the vulnerability of the building of interest to different kinds of flooding events. On the one hand, we display the adverse effects of the disaster on a detailed level, ranging from damage inflicted on the building facades or cellars to the accessibility of the important infrastructure in the vicinity. On the other hand, we provide visual indications of the events to which the building of interest is vulnerable in particular. Our visual encodings are displayed in the context of urban 3D renderings to establish an intuitive relation between geospatial and abstract information. We combine all the visualizations in a lightweight interface that enables the user to study the impacts and vulnerabilities of interest and explore the scenarios of choice. We evaluate our solution with experts involved in flood management and public communication.
VIMTEX: A Visualization Interface for Multivariate, Time‐Varying, Geological Data Exploration
Authors: Dasgupta A. , Kosara R. , Gosink L. 
Published: July 2015

AbstractObserving interactions among chemical species and microorganisms in the earth's sub‐surface is a common task in the field of geology. Bioremediation experiments constitute one such class of interactions which focus on getting rid of pollutants through processes such as carbon sequestration. The main goal of scientists’ observations is to analyze the dynamics of the chemical reactions and understand how they collectively affect the carbon content of the soil. In our work, we extract the high‐level goals of geologists and propose a visual analytics solution which helps scientists in deriving insights about multivariate, temporal behavior of these chemical species. Specifically, our key contributions are the following: i) characterization of the domain‐specific goals and their translation to exploratory data analysis tasks, ii) developing an analytical abstraction in the form of perceptually motivated screen‐space metrics for bridging the gap between the tasks and the visualization, and iii) realization of the tasks and metrics in the form of VIMTEX, which is a set of coordinated multiple views for letting scientists observe multivariate, temporal relationships in the data. We provide several examples and case studies along with expert feedback for demonstrating the efficacy of our solution.
Quantitative Measures for Cartogram Generation Techniques
Authors: Alam Md. Jawaherul , Kobourov Stephen G. , Veeramoni Sankar 
Published: July 2015

AbstractCartograms are used to visualize geographically distributed data by scaling the regions of a map (e.g., US states) such that their areas are proportional to some data associated with them (e.g., population). Thus the cartogram computation problem can be considered as a map deformation problem where the input is a planar polygonal map M and an assignment of some positive weight for each region. The goal is to create a deformed map M′, where the area of each region realizes the weight assigned to it (no cartographic error) while the overall map remains readable and recognizable (e.g., the topology, relative positions and shapes of the regions remain as close to those before the deformation as possible). Although several such measures of cartogram quality are well‐known, different cartogram generation methods optimize different features and there is no standard set of quantitative metrics. In this paper we define such a set of seven quantitative measures, designed to evaluate how faithfully a cartogram represents the desired weights and to estimate the readability of the final representation. We then study several cartogram‐generation algorithms and compare them in terms of these quantitative measures.