Assessment of correlations between risk factors and symptom presentation among defined at-risk groups following a confirmed COVID-19 diagnosis

This study analyzes the specific linkages between symptoms within individual COVID patients belonging to at-risk groups. The goal was to determine how strongly linked patient symptoms are within these at-risk groups to find any associations between factors such as comorbidities and COVID symptoms. In this study, de-identified patient data from the N3C database was utilized in order to link representative immunocompromised states with specific symptoms, and non-immunocompromised state with the same, to determine if the strength of the correlation changes for these at-risk groups. Multiple autoimmune disorders resulting in immunocompromised state were analyzed, to determine if severity of immune response and inflammatory action plays a role in any potential differences. An exploratory approach using statistical methods and visualization techniques appropriate to multidimensional data sets was taken. The identified correlations may allow pattern analysis in disease presentation specific to a given population, potentially informing pattern recognition, symptom presentation, and treatment approaches in patients with immune comorbidities.

Fast Iterative Filtering: a new, fast and robust decomposition method for nonlinear and nonstationary processes

<p>The analysis of nonlinear and nonstationary processes is, in general, a challenging task.<br>One way to tackle it is to first decompose the signal into simpler components and then analyze them separately. This is the idea behind the Empirical Mode Decomposition (EMD) method, published originally in 1998. EMD had a big impact in many filed of research as testified by the more than 15300 citations (based on Scopus). However, the mathematical properties of EMD and its generalizations, like the Ensemble EMD, are still under investigation. For this reason an alternative technique, called Iterative Filtering (IF), was proposed in 2009.</p><p>In this talk we introduce the IF method and present new insights in its mathematical properties. In particular, we show its robustness to noise, its ability to avoid mode mixing, and its speed up in what is called the Fast Iterative Filtering (FIF).<br>Both IF and FIF have been extened to handle multivariate and multidimensional data sets, outperforming, in terms of computational time, any alternative method proposed so far in the literature for the decomposition of nonstationary signals.</p><p>This is a joint work with H. Zhou (Georgia Tech).</p>

Relevant Independent Variables on MOBA Video Games to Train Machine Learning Algorithms

Popularity of Multiplayer Online Battle Arena (MOBA) video games has grown considerably, its popularity as well as the complexity of their playability, have attracted the attention in recent years of researchers from various areas of knowledge and in particular how they have resorted to different machine learning techniques. The papers reviewed mainly look for patterns in multidimensional data sets. Furthermore, these previous researches do not present a way to select the independent variables (predictors) to train the models. For this reason, this paper proposes a list of variables based on the techniques used and the objectives of the research. It allows to provide a set of variables to find patterns applied in MOBA videogames. In order to get the mentioned list, the consulted works were grouped by the used machine learning techniques, ranging from rule-based systems to complex neural network architectures. Also, a grouping technique is applied based on the objective of each research proposed.

A comparative user study of visualization techniques for cluster analysis of multidimensional data sets

This article presents an empirical user study that compares eight multidimensional projection techniques for supporting the estimation of the number of clusters, [Formula: see text], embedded in six multidimensional data sets. The selection of the techniques was based on their intended design, or use, for visually encoding data structures, that is, neighborhood relations between data points or groups of data points in a data set. Concretely, we study: the difference between the estimates of [Formula: see text] as given by participants when using different multidimensional projections; the accuracy of user estimations with respect to the number of labels in the data sets; the perceived usability of each multidimensional projection; whether user estimates disagree with [Formula: see text] values given by a set of cluster quality measures; and whether there is a difference between experienced and novice users in terms of estimates and perceived usability. The results show that: dendrograms (from Ward’s hierarchical clustering) are likely to lead to estimates of [Formula: see text] that are different from those given with other multidimensional projections, while Star Coordinates and Radial Visualizations are likely to lead to similar estimates; t-Stochastic Neighbor Embedding is likely to lead to estimates which are closer to the number of labels in a data set; cluster quality measures are likely to produce estimates which are different from those given by users using Ward and t-Stochastic Neighbor Embedding; U-Matrices and reachability plots will likely have a low perceived usability; and there is no statistically significant difference between the answers of experienced and novice users. Moreover, as data dimensionality increases, cluster quality measures are likely to produce estimates which are different from those perceived by users using any of the assessed multidimensional projections. It is also apparent that the inherent complexity of a data set, as well as the capability of each visual technique to disclose such complexity, has an influence on the perceived usability.

Applying Visual Analysis Procedures to Multidimensional Medical Data

The paper considers the tasks of visual analysis of multidimensional data sets of medical origin. For visual analysis, the approach of building elastic maps is used. The elastic maps are used as the methods of original data points mapping to enclosed manifolds having less dimensionality. Diminishing the elasticity parameters one can design map surface which approximates the multidimensional dataset in question much better. To improve the results, a number of previously developed procedures are used - preliminary data filtering, removal of separated clusters (flotation). To solve the scalability problem, when the elastic map is adjusted both to the region of condensation of data points and to separately located points of the data cloud, the quasi-Zoom approach is applied. The illustrations of applying elastic maps to various sets of medical data are presented.

Imaging, Visualization, and Computation in Developmental Biology

Embryonic development is highly complex and dynamic, requiring the coordination of numerous molecular and cellular events at precise times and places. Advances in imaging technology have made it possible to follow developmental processes at cellular, tissue, and organ levels over time as they take place in the intact embryo. Parallel innovations of in vivo probes permit imaging to report on molecular, physiological, and anatomical events of embryogenesis, but the resulting multidimensional data sets pose significant challenges for extracting knowledge. In this review, we discuss recent and emerging advances in imaging technologies, in vivo labeling, and data processing that offer the greatest potential for jointly deciphering the intricate cellular dynamics and the underlying molecular mechanisms. Our discussion of the emerging area of “image-omics” highlights both the challenges of data analysis and the promise of more fully embracing computation and data science for rapidly advancing our understanding of biology.

THE PROCEDURES OF VISUAL ANALYSIS FOR MULTIDIMENSIONAL DATA VOLUMES

<p><strong>Abstract.</strong> The paper is devoted to problems of visual analysis of multidimensional data sets using an approach based on the construction of elastic maps. This approach is quite suitable for processing and visualizing of multidimensional datasets. The elastic maps are used as the methods of original data points mapping to enclosed manifolds having less dimensionality. Diminishing the elasticity parameters one can design map surface which approximates the multidimensional dataset in question much better. Then the points of dataset in question are projected to the map. The extension of designed map to a flat plane allows one to get an insight about the structure of multidimensional dataset. The paper presents the results of applying elastic maps for visual analysis of multidimensional data sets of medical origin. Previously developed data processing procedures are applied to improve the results obtained - pre-filtering of data, removal of separated clusters (flotation), quasi-Zoom.</p>

Visualization of Multidimensional Data in Nursing Science

Nursing scientists have long been interested in complex, context-dependent questions addressing individual- and population-level challenges in health and illness. These critical questions require multilevel data (e.g., genetic, physiologic, biologic, behavioral, affective, and social). Advances in data-gathering methods have resulted in the collection of large sets of complex, multifaceted, and often non-comparable data. Scientific visualization is a powerful methodological tool for facilitating understanding of these multidimensional data sets. Our purpose is to demonstrate the utility of scientific visualization as a method for identifying associations, patterns, and trends in multidimensional data as exemplified in two studies. We describe a brief history of visual analysis, processes involved in scientific visualization, and opportunities and challenges in the use of visualization methods. Scientific visualization can play a crucial role in helping nurse scientists make sense of the structure and underlying patterns in their data to answer vital questions in the field.