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The study of mixture component effects in the presence of process variables has been of interest since the work of Scheffé. A key advantage of designed experiments in general is the ability to estimate and interpret interactions. A unique feature of mixture-process experiments is the potential presence of interactions between the mixture components and the process variables. The classic approach to interpret these has been to use contour plots and evaluate individual interaction coefficients in Scheffé mixture-process models. It is proposed to study the interactions along the Cox component axes, which greatly enhances the insight into the nature of these interactions that can be obtained from contour plots. Further, we propose an alternative analysis that produces estimates of the process variable main effects in mixture-process models. Both graphical and analytical methods are presented. This approach provides an overall view of the main effects and interactions that is consistent with how these terms are evaluated in factorial and response surface experiments with only process variables. Limitations of the classic approach are identified and discussed. Three examples are included to illustrate the approach. [ABSTRACT FROM AUTHOR]

Meta-analytic datasets can be large, especially when in primary studies multiple effect sizes are reported. The visualization of meta-analytic data is therefore useful to summarize data and understand information reported in primary studies. The gold standard figures in meta-analysis are forest and funnel plots. However, none of these plots can yet account for the existence of multiple effect sizes within primary studies. This manuscript describes extensions to the funnel plot, forest plot and caterpillar plot to adapt them to three-level meta-analyses. For forest plots, we propose to plot the study-specific effects and their precision, and to add additional confidence intervals that reflect the sampling variance of individual effect sizes. For caterpillar plots and funnel plots, we recommend to plot individual effect sizes and averaged study-effect sizes in two separate graphs. For the funnel plot, plotting separate graphs might improve the detection of both publication bias and/or selective outcome reporting bias.

Abstract Background Data-visualization methods are essential to explore and communicate meta-analytic data and results. With a large number of novel graphs proposed quite recently, a comprehensive, up-to-date overview of available graphing options for meta-analysis is unavailable. Methods We applied a multi-tiered search strategy to find the meta-analytic graphs proposed and introduced so far. We checked more than 150 retrievable textbooks on research synthesis methodology cover to cover, six different software programs regularly used for meta-analysis, and the entire content of two leading journals on research synthesis. In addition, we conducted Google Scholar and Google image searches and cited-reference searches of prior reviews of the topic. Retrieved graphs were categorized into a taxonomy encompassing 11 main classes, evaluated according to 24 graph-functionality features, and individually presented and described with explanatory vignettes. Results We ascertained more than 200 different graphs and graph variants used to visualize meta-analytic data. One half of these have accrued within the past 10 years alone. The most prevalent classes were graphs for network meta-analysis (45 displays), graphs showing combined effect(s) only (26), funnel plot-like displays (24), displays showing more than one outcome per study (19), robustness, outlier and influence diagnostics (15), study selection and p-value based displays (15), and forest plot-like displays (14). The majority of graphs (130, 62.5%) possessed a unique combination of graph features. Conclusions The rich and diverse set of available meta-analytic graphs offers a variety of options to display many different aspects of meta-analyses. This comprehensive overview of available graphs allows researchers to make better-informed decisions on which graphs suit their needs and therefore facilitates using the meta-analytic tool kit of graphs to its full potential. It also constitutes a roadmap for a goal-driven development of further graphical displays for research synthesis.

Conventional electronic health record information displays are not optimized for efficient information processing. Graphical displays that integrate patient information can improve information processing, especially in data-rich environments such as critical care. We propose an adaptable and reusable approach to patient information display with modular graphical components (widgets). We had two study objectives. First, reduce numerous widget prototype alternatives to preferred designs. Second, derive widget design feature recommendations. Using iterative human-centered design methods, we interviewed experts to hone design features of widgets displaying frequently measured data elements, e.g., heart rate, for acute care patient monitoring and real-time clinical decision-making. Participant responses to design queries were coded to calculate feature-set agreement, average prototype score, and prototype agreement. Two iterative interview cycles covering 64 design queries and 86 prototypes were needed to reach consensus on six feature sets. Interviewers agreed that line graphs with a smoothed or averaged trendline, 24-h timeframe, and gradient coloring for urgency were useful and informative features. Moreover, users agreed that widgets should include key functions: (1) adjustable reference ranges, (2) expandable timeframes, and (3) access to details on demand. Participants stated graphical widgets would be used to identify correlating patterns and compare abnormal measures across related data elements at a specific time. Combining theoretical principles and validated design methods was an effective and reproducible approach to designing widgets for healthcare displays. The findings suggest our widget design features and recommendations match critical care clinician expectations for graphical information display of continuous and frequently updated patient data. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) NMR logging activation sets are acquisition means aiming at obtaining specific formation information, affecting the adaptability of NMR instruments to different types of reservoirs and the reliability of the original data. In this work, a software with the visualization function for designing 2D NMR logging activation sets was developed using the C# language, based on the component elements and working mechanism of activation. The software supported the design of activation sets for 2D NMR logging and NMR core analyzer. Meanwhile, the software provided three kinds of parameter editing methods, observation mode optimization and graphical display functions, and thus realized flexible adjustment of the activation sets. It solved the problems in the former mainstream 2D NMR logging activation sets, such as low signal-to-noise ratio of the raw data, incomplete loading of fluid relaxation characteristics due to inadequately fixed acquisition parameters and inflexible adjustment etc. In addition, the software also provided outputs for acquisition parameters and acquisition timing.

Abstract Background Presenting results of diagnostic test accuracy research so that it is accessible to users is challenging. Commonly used accuracy measures (e.g. sensitivity and specificity) are poorly understood by health professionals and the public. Evidence suggests that presenting probabilities as natural frequencies rather than percentages facilitates understanding. We present a test consequence graphic to display results based on natural frequencies and test consequences. Methods The graphic was developed as part of a project to develop guidance for writing plain language summaries for Cochrane diagnostic test accuracy (DTA) reviews. Using a mixed methods approach (focus groups, user testing, web-based surveys, public engagement and piloting), the graphic emerged as a clear preference out of a range of methods for presenting probabilistic information (text only, numbers embedded in text, annotated graphic) across patient representatives, media representatives and health professionals. The structure of the graphic was refined during the research process. Results The test consequence graphic displays the results of diagnostic test accuracy study or review as natural frequencies based on a hypothetical cohort of 1000 patients receiving the test. Conclusions The test consequence graphic provides a tool to help researchers communicate the results of diagnostic research in a simple, easy to access format and encourage meaningful application of research findings to practice. Key to this is linking estimates of test accuracy to potential downstream consequences of testing.

Based on simultaneous voice and electroglottograph (EGG) signals, to gain a better understanding of human voice production process, to make pitch-synchronous segmentation of voice signals, and to make visual representations of pitch marks and timbre spectra with high resolution. The traditional spectrogram segments the voice signals with a process window of fixed size and fixed shift, then performs fast Fourier transformation after multiplied with a window function, typically a Hamming window. Then display power spectrum in both frequency and time. Pitch information and timbre information are mixed. The new design segments the signals into pitch periods, either using the derivatives of the EGG signals or based on the voice signals, then performs Fourier analysis to the segment of signals in each pitch period without using a window function. The pitch information and the timbre information are cleanly separated. The graphical representations of both pitch marks and timbre spectra exhibit high resolution and high accuracy. Detailed analysis of simultaneously acquired voice and EGG signals provides a more precise understanding of human-voice production process. The transient theory of voice production, proposed by Leonhard Euler in early 18th century, is substantiated with modern data. Based on the transient theory of voice production, a pitch-synchronous spectrogram software is developed, which makes a visual representation of pitch marks and timbre spectra. In addition, the timbre spectrum and the power evolution pattern in each pitch period can be displayed individually. Simultaneously acquired voice and EGG signals indicates that each glottal closing triggers a decaying elementary wave in the vocal tract. A superposition of those elementary waves constitutes voice. Based on that concept and using EGG data, a pitch-synchronous voice signal processing method is developed. The voice signal is first segmented into pitch periods, then the two ends are equalized. Fourier analysis is applied to obtain the timbre spectrum of each pitch period. High resolution display of timbre spectrum is generated. The power evolution pattern in each pitch period is also displayed. [ABSTRACT FROM AUTHOR]

Background: Data-visualization methods are essential to explore and communicate meta-analytic data and results. With a large number of novel graphs proposed quite recently, a comprehensive, up-to-date overview of available graphing options for meta-analysis is unavailable.Methods: We applied a multi-tiered search strategy to find the meta-analytic graphs proposed and introduced so far. We checked more than 150 retrievable textbooks on research synthesis methodology cover to cover, six different software programs regularly used for meta-analysis, and the entire content of two leading journals on research synthesis. In addition, we conducted Google Scholar and Google image searches and cited-reference searches of prior reviews of the topic. Retrieved graphs were categorized into a taxonomy encompassing 11 main classes, evaluated according to 24 graph-functionality features, and individually presented and described with explanatory vignettes.Results: We ascertained more than 200 different graphs and graph variants used to visualize meta-analytic data. One half of these have accrued within the past 10 years alone. The most prevalent classes were graphs for network meta-analysis (45 displays), graphs showing combined effect(s) only (26), funnel plot-like displays (24), displays showing more than one outcome per study (19), robustness, outlier and influence diagnostics (15), study selection and p-value based displays (15), and forest plot-like displays (14). The majority of graphs (130, 62.5%) possessed a unique combination of graph features.Conclusions: The rich and diverse set of available meta-analytic graphs offers a variety of options to display many different aspects of meta-analyses. This comprehensive overview of available graphs allows researchers to make better-informed decisions on which graphs suit their needs and therefore facilitates using the meta-analytic tool kit of graphs to its full potential. It also constitutes a roadmap for a goal-driven development of further graphical displays for research synthesis. [ABSTRACT FROM AUTHOR]

Background/Aims: Alzheimer's disease (AD) is a chronic neurodegenerative disease that causes memory loss and a decline in cognitive abilities. AD is the sixth leading cause of death in the USA, affecting an estimated 5 million Americans. To assess the association between multiple genetic variants and multiple measurements of structural changes in the brain, a recent study of AD used a multivariate measure of linear dependence, the RV coefficient. The authors decomposed the RV coefficient into contributions from individual variants and displayed these contributions graphically. Methods: We investigate the properties of such a "contribution plot" in terms of an underlying linear model, and discuss shrinkage estimation of the components of the plot when the correlation signal may be sparse. Results: The contribution plot is applied to simulated data and to genomic and brain imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Conclusions: The contribution plot with shrinkage estimation can reveal truly associated explanatory variables. [ABSTRACT FROM AUTHOR]

We will analyze variations in brain anatomy and create the first integrated software environment to extract patterns from brains and target differences related to inter-individual variability, pathology, development, or degeneration. We will evaluate how well these differences can help diagnose and predict treatment outcome for major depressive disorder, which affects millions of Americans, but our work is intended to be applied to any mental illness, such as Alzheimer’s disease, bipolar disorder, schizophrenia – indeed to analyze differences in brain anatomy between any two populations.

The multipath effect is a crucial error source caused by the environment around the station and cannot be eliminated or mitigated by differential algorithms. Theoretically, the maximum value for the carrier phase is a quarter the wavelength, i.e., about 4.8 cm for the GPS L1 signal. Considering the increasing demands of high-precision applications, the multipath error has become a major factor affecting the accuracy and reliability of GPS millimeter-level data processing. This paper proposes a multi-point hemispherical grid model (MHGM) to mitigate the multipath effect. In this method, the hemisphere centered on each station is divided into a grid, and the multipath error at the station is estimated based on the parameterization of the grid points. The double-differenced (DD) observed-minus-calculated (OMC) values on some previous days are treated as the observation values to model the present multipath error. Contrary to the present methods which rely much on the platform of data collection and processing, MHGM can be potentially applied to GPS data processing with the existing hardware and software. Experiments in high-multipath and low-multipath environments are designed by mounting a baffle or not. The experimental results show that MHGM is effective in mitigating the multipath effect. When using data from the previous day, an average improvement of about 63.3% in the RMS of DD OMC can be made compared with that without correction, and this is basically consistent with the sidereal filtering (SF) method which is 63.0%. Furthermore, the effectiveness of the above two methods is better than that of the empirical site model (ESM). The kinematic positioning results are also basically consistent with the statistical results of the RMS values of DD OMC. Historical data from more than one day can more explicitly and effectively model the MHGM. Furthermore, compared with the SF method, the MHGM can be used not only to mitigate the multipath error, but also to orientate the sources of the multipath error around the station, and give guidance in the physical elimination of these sources.

Assessment and communication of toxicology data are fundamental components of the work performed by veterinary anatomic and clinical pathologists involved in toxicology research. In recent years, there has been an evolution in the number and variety of software tools designed to facilitate the evaluation and presentation of toxicity study data. A working group of the Society of Toxicologic Pathology Scientific and Regulatory Policy Committee reviewed existing and emerging visualization technologies. This Points to Consider article reviews some of the currently available data visualization options, describes the utility of different types of graphical displays, and explores potential areas of controversy and ambiguity encountered with the use of these tools. [ABSTRACT FROM AUTHOR]

Cardiopulmonary exercise testing is a well-known, valuable tool in the clinical evaluation of patients with different causes of exercise limitation and unexplained dyspnea. A wealth of data is generated by each individual test. This may be challenging regarding a comprehensive and reliable interpretation of an exercise study in a timely manner. An optimized graphical display of exercise data may substantially help to improve the efficacy and reliability of the interpretation process. However, there are limited and heterogeneous recommendations on standardized graphical display in current exercise testing guidelines. To date, a widely used three-by-three array of specifically arranged graphical panels known as the "nine-panel plot" is probably the most common method of plotting exercise gas exchange data in a standardized way. Furthermore, optimized scaling of the plots, the use of colors and style elements, as well as suitable averaging methods have to be considered to achieve a high level of quality and reproducibility of the results. Specific plots of key parameters may allow a fast and reliable visual determination of important diagnostic and prognostic markers in cardiac and pulmonary diseases. [ABSTRACT FROM AUTHOR]

In demography, a detailed study of the temporal dynamics of the structure of a population is often required to better understand the processes that underline its overall dynamics and the individual’s life histories. Heatmaps, using time and structure (such as size-structure) as x and y coordinates and density as colours, are efficient tools for displaying the dynamics of a structured population. Such representations (structure-time diagrams) reveal the data at several levels, from general outlook to fine details. Despite its efficiency, this type of visual display has been scarcely used in ecology and demography. Using the example of springtail populations maintained in the laboratory and a woodlouse population studied in the field, we explain why this type of representation can be used to analyse the population dynamics of soil organisms and why it should be more widely used in demography. We also present the R package STdiag (for ‘Structure Time diagram’), an interface to complex graphical functions to easily produce and analyse such ‘structure-time diagrams’ from raw datasets. This package is available for all operating systems via R-Forge. Its syntax and options are described, discussed and illustrated using our case studies. This graphical display is a simple and efficient way to make large demographic datasets coherent and to disclose the underlying, often hidden, demographical processes.