The association between non­high­density lipoprotein cholesterol to high­density lipoprotein cholesterol ratio (NHHR) and low muscle mass in adults aged 20-59: a population-based study in the United States.

BACKGROUND: The ratio between non-high-density lipoprotein cholesterol and high-density lipoprotein cholesterol (NHHR) is a reliable marker for assessing the risk linked to lipid metabolism disorders. Sarcopenia, characterized by age-related loss of muscle mass and strength/function, includes the assessment of muscle mass, muscle strength, and muscle-specific strength. However, research into NHHR's relationship with low muscle mass risk remains unexplored. METHODS: Our study utilized a cross-sectional approach, examining data derived from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2018. Through multivariable linear and logistic regression, we investigated the relationships of the NHHR with muscle mass and low muscle mass. We visualized the results using smoothing curves and assessed threshold effects. We also performed various subgroup and sensitivity analyses. RESULTS: This research encompassed 9,012 participants and demonstrated significant nonlinear associations between NHHR and ALMBMI or low muscle mass risk in a generalized additive model (GAM), pinpointing critical NHHR values (3.328 and 3.367) where changes in NHHR significantly impacted ALMBMI and low muscle mass risk. CONCLUSIONS: The NHHR demonstrates a significant association with an increased risk of low muscle mass among middle-aged Americans. This ratio has potential as a predictive marker for low muscle mass. Further exploration of NHHR is expected to aid in advancing preventive and therapeutic measures for this condition.

Visualizing the knowledge structure and evolution of bioinformatics.

BACKGROUND: Bioinformatics has gained much attention as a fast growing interdisciplinary field. Several attempts have been conducted to explore the field of bioinformatics by bibliometric analysis, however, such works did not elucidate the role of visualization in analysis, nor focus on the relationship between sub-topics of bioinformatics. RESULTS: First, the hotspot of bioinformatics has moderately shifted from traditional molecular biology to omics research, and the computational method has also shifted from mathematical model to data mining and machine learning. Second, DNA-related topics are bridge topics in bioinformatics research. These topics gradually connect various sub-topics that are relatively independent at first. Third, only a small part of topics we have obtained involves a number of computational methods, and the other topics focus more on biological aspects. Fourth, the proportion of computing-related topics hit a trough in the 1980s. During this period, the use of traditional calculation methods such as mathematical model declined in a large proportion while the new calculation methods such as machine learning have not been applied in a large scale. This proportion began to increase gradually after the 1990s. Fifth, although the proportion of computing-related topics is only slightly higher than the original, the connection between other topics and computing-related topics has become closer, which means the support of computational methods is becoming increasingly important for the research of bioinformatics. CONCLUSIONS: The results of our analysis imply that research on bioinformatics is becoming more diversified and the ranking of computational methods in bioinformatics research is also gradually improving.

Gaze Information Channel in Van Gogh's Paintings.

This paper uses quantitative eye tracking indicators to analyze the relationship between images of paintings and human viewing. First, we build the eye tracking fixation sequences through areas of interest (AOIs) into an information channel, the gaze channel. Although this channel can be interpreted as a generalization of a first-order Markov chain, we show that the gaze channel is fully independent of this interpretation, and stands even when first-order Markov chain modeling would no longer fit. The entropy of the equilibrium distribution and the conditional entropy of a Markov chain are extended with additional information-theoretic measures, such as joint entropy, mutual information, and conditional entropy of each area of interest. Then, the gaze information channel is applied to analyze a subset of Van Gogh paintings. Van Gogh artworks, classified by art critics into several periods, have been studied under computational aesthetics measures, which include the use of Kolmogorov complexity and permutation entropy. The gaze information channel paradigm allows the information-theoretic measures to analyze both individual gaze behavior and clustered behavior from observers and paintings. Finally, we show that there is a clear correlation between the gaze information channel quantities that come from direct human observation, and the computational aesthetics measures that do not rely on any human observation at all.

EvoVis: A Visual Analytics Method to Understand the Labeling Iterations in Data Programming.

Obtaining high-quality labeled training data poses a significant bottleneck in the domain of machine learning. Data programming has emerged as a new paradigm to address this issue by converting human knowledge into labeling functions(LFs) to quickly produce low-cost probabilistic labels. To ensure the quality of labeled data, data programmers commonly iterate LFs for many rounds until satisfactory performance is achieved. However, the challenge in understanding the labeling iterations stems from interpreting the intricate relationships between data programming elements, exacerbated by their many-to-many and directed characteristics, inconsistent formats, and the large scale of data typically involved in labeling tasks. These complexities may impede the evaluation of label quality, identification of areas for improvement, and the effective optimization of LFs for acquiring high-quality labeled data. In this paper, we introduce EvoVis, a visual analytics method for multi-class text labeling tasks. It seamlessly integrates relationship analysis and temporal overview to display contextual and historical information on a single screen, aiding in explaining the labeling iterations in data programming. We assessed its utility and effectiveness through case studies and user studies. The results indicate that EvoVis can effectively assist data programmers in understanding labeling iterations and improving the quality of labeled data, as evidenced by an increase of 0.16 in the average F1 score when compared to the default analysis tool.

Exploring the impact of protein intake on the association between oxidative balance score and lean mass in adults aged 20-59: NHANES 2011-2018.

BACKGROUND: Previous studies have established a correlation between the pathogenesis of oxidative stress and sarcopenia. The Oxidative Balance Score (OBS) is an integrated measure that reflects the overall balance of antioxidants and pro-oxidants in dietary components and lifestyle. However, there are limited reports on the association between OBS and lean mass and the impact of protein intake on the association between OBS and lean mass. METHODS: Using data from the National Health and Nutrition Examination Survey from 2011 to 2018, multivariate linear and logistic regression analyses were conducted to explore the associations between OBS and outcomes. The findings were then illustrated through fitted smoothing curves and threshold effect analyses. RESULTS: This study included 2,441 participants, demonstrating that higher OBS is significantly associated with an increased ratio of appendicular lean mass to body mass index. Key inflection points at OBS 31 mark pronounced changes in these associations, with age and protein intake notably affecting the association. The effect of OBS on lean mass varies among populations with high and low protein intake. CONCLUSIONS: Our findings suggest that OBS is significantly and positively associated with lean mass. A high protein intake of more than 84.5 g/day may enhance the role of OBS in influencing muscle health to improve muscle outcomes.

Dual Space Coupling Model Guided Overlap-Free Scatterplot.

The overdraw problem of scatterplots seriously interferes with the visual tasks. Existing methods, such as data sampling, node dispersion, subspace mapping, and visual abstraction, cannot guarantee the correspondence and consistency between the data points that reflect the intrinsic original data distribution and the corresponding visual units that reveal the presented data distribution, thus failing to obtain an overlap-free scatterplot with unbiased and lossless data distribution. A dual space coupling model is proposed in this paper to represent the complex bilateral relationship between data space and visual space theoretically and analytically. Under the guidance of the model, an overlap-free scatterplot method is developed through integration of the following: a geometry-based data transformation algorithm, namely DistributionTranscriptor; an efficient spatial mutual exclusion guided view transformation algorithm, namely PolarPacking; an overlap-free oriented visual encoding configuration model and a radius adjustment tool, namely frdraw. Our method can ensure complete and accurate information transfer between the two spaces, maintaining consistency between the newly created scatterplot and the original data distribution on global and local features. Quantitative evaluation proves our remarkable progress on computational efficiency compared with the state-of-the-art methods. Three applications involving pattern enhancement, interaction improvement, and overdraw mitigation of trajectory visualization demonstrate the broad prospects of our method.

Towards Visual Explainable Active Learning for Zero-Shot Classification.

Zero-shot classification is a promising paradigm to solve an applicable problem when the training classes and test classes are disjoint. Achieving this usually needs experts to externalize their domain knowledge by manually specifying a class-attribute matrix to define which classes have which attributes. Designing a suitable class-attribute matrix is the key to the subsequent procedure, but this design process is tedious and trial-and-error with no guidance. This paper proposes a visual explainable active learning approach with its design and implementation called semantic navigator to solve the above problems. This approach promotes human-AI teaming with four actions (ask, explain, recommend, respond) in each interaction loop. The machine asks contrastive questions to guide humans in the thinking process of attributes. A novel visualization called semantic map explains the current status of the machine. Therefore analysts can better understand why the machine misclassifies objects. Moreover, the machine recommends the labels of classes for each attribute to ease the labeling burden. Finally, humans can steer the model by modifying the labels interactively, and the machine adjusts its recommendations. The visual explainable active learning approach improves humans' efficiency of building zero-shot classification models interactively, compared with the method without guidance. We justify our results with user studies using the standard benchmarks for zero-shot classification.

Face Inverse Rendering via Hierarchical Decoupling.

Previous face inverse rendering methods often require synthetic data with ground truth and/or professional equipment like a lighting stage. However, a model trained on synthetic data or using pre-defined lighting priors is typically unable to generalize well for real-world situations, due to the gap between synthetic data/lighting priors and real data. Furthermore, for common users, the professional equipment and skill make the task expensive and complex. In this paper, we propose a deep learning framework to disentangle face images in the wild into their corresponding albedo, normal, and lighting components. Specifically, a decomposition network is built with a hierarchical subdivision strategy, which takes image pairs captured from arbitrary viewpoints as input. In this way, our approach can greatly mitigate the pressure from data preparation, and significantly broaden the applicability of face inverse rendering. Extensive experiments are conducted to demonstrate the efficacy of our design, and show its superior performance in face relighting over other state-of-the-art alternatives. Our code is available at https://github.com/AutoHDR/HD-Net.git.

Large Relics Scenario-Based Visualization Using Head-Mounted Displays.

Most visitors come to visit museums; in reality, few immersive solutions support the senses experience. Virtual reality (VR) technology attaches the virtual information from the real environment. Applying the VR technology in the 3D relic information display and visualization in the museum field is a hot research issue. However, most current solutions of relics are one-sided, only focusing on the virtual exhibition, lack of associations with actual function, and senses experience, especially the large artistic cultural relics. The scenario-based virtual exhibition solution is an available approach to allow visitors to imitate ancient artist and provide relatively experience in the form of content and sense organ of ancient art. It converts large relics into "digital large relics" and enables experiencing performance of ancient civilization in person. The solution presents relics to the visitors in a more direct and vivid manner and with innovative forms, strong interaction, and intelligence, thereby improving the interests and satisfaction among visitors in this type of relic exhibition. Besides, it also provides visitors with a convenient way to experience and learn ritual and culture. Evaluation and conclusion can be drawn that most participants appreciated this solution in clear interface and completion aspects.

Galex: Exploring the Evolution and Intersection of Disciplines.

Revealing the evolution of science and the intersections among its sub-fields is extremely important to understand the characteristics of disciplines, discover new topics, and predict the future. The current work focuses on either building the skeleton of science, lacking interaction, detailed exploration and interpretation or on the lower topic level, missing high-level macro-perspective. To fill this gap, we design and implement Galaxy Evolution Explorer (Galex), a hierarchical visual analysis system, in combination with advanced text mining technologies, that could help analysts to comprehend the evolution and intersection of one discipline rapidly. We divide Galex into three progressively fine-grained levels: discipline, area, and institution levels. The combination of interactions enables analysts to explore an arbitrary piece of history and an arbitrary part of the knowledge space of one discipline. Using a flexible spotlight component, analysts could freely select and quickly understand an exploration region. A tree metaphor allows analysts to perceive the expansion, decline, and intersection of topics intuitively. A synchronous spotlight interaction aids in comparing research contents among institutions easily. Three cases demonstrate the effectiveness of our system.

Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data.

Social media data with geotags can be used to track people's movements in their daily lives. By providing both rich text and movement information, visual analysis on social media data can be both interesting and challenging. In contrast to traditional movement data, the sparseness and irregularity of social media data increase the difficulty of extracting movement patterns. To facilitate the understanding of people's movements, we present an interactive visual analytics system to support the exploration of sparsely sampled trajectory data from social media. We propose a heuristic model to reduce the uncertainty caused by the nature of social media data. In the proposed system, users can filter and select reliable data from each derived movement category, based on the guidance of uncertainty model and interactive selection tools. By iteratively analyzing filtered movements, users can explore the semantics of movements, including the transportation methods, frequent visiting sequences and keyword descriptions. We provide two cases to demonstrate how our system can help users to explore the movement patterns.

Convolutional virtual electric field for image segmentation using active contours.

Gradient vector flow (GVF) is an effective external force for active contours; however, it suffers from heavy computation load. The virtual electric field (VEF) model, which can be implemented in real time using fast Fourier transform (FFT), has been proposed later as a remedy for the GVF model. In this work, we present an extension of the VEF model, which is referred to as CONvolutional Virtual Electric Field, CONVEF for short. This proposed CONVEF model takes the VEF model as a convolution operation and employs a modified distance in the convolution kernel. The CONVEF model is also closely related to the vector field convolution (VFC) model. Compared with the GVF, VEF and VFC models, the CONVEF model possesses not only some desirable properties of these models, such as enlarged capture range, u-shape concavity convergence, subject contour convergence and initialization insensitivity, but also some other interesting properties such as G-shape concavity convergence, neighboring objects separation, and noise suppression and simultaneously weak edge preserving. Meanwhile, the CONVEF model can also be implemented in real-time by using FFT. Experimental results illustrate these advantages of the CONVEF model on both synthetic and natural images.

Visual Analysis of Public Utility Service Problems in a Metropolis.

Issues about city utility services reported by citizens can provide unprecedented insights into the various aspects of such services. Analysis of these issues can improve living quality through evidence-based decision making. However, these issues are complex, because of the involvement of spatial and temporal components, in addition to having multi-dimensional and multivariate natures. Consequently, exploring utility service problems and creating visual representations are difficult. To analyze these issues, we propose a visual analytics process based on the main tasks of utility service management. We also propose an aggregate method that transforms numerous issues into legible events and provide visualizations for events. In addition, we provide a set of tools and interaction techniques to explore such issues. Our approach enables administrators to make more informed decisions.

Vis4Heritage: visual analytics approach on Grotto wall painting degradations.

For preserving the Grotto wall paintings and protecting these historic cultural icons from the damage and deterioration in nature environment, a visual analytics framework and a set of tools are proposed for the discovery of degradation patterns. In comparison with the traditional analysis methods that used restricted scales, our method provides users with multi-scale analytic support to study the problems on site, cave, wall and particular degradation area scales, through the application of multidimensional visualization techniques. Several case studies have been carried out using real-world wall painting data collected from a renowned World Heritage site, to verify the usability and effectiveness of the proposed method. User studies and expert reviews were also conducted through by domain experts ranging from scientists such as microenvironment researchers, archivists, geologists, chemists, to practitioners such as conservators, restorers and curators.