Type 2 Diabetes Mellitus and Neurodegenerative Disorders: The Mitochondrial Connection.

The incidence of type 2 diabetes mellitus (T2DM) has increased in our society in recent decades as the population ages, and this trend is not expected to revert. This is the same for the incidence of the main neurodegenerative disorders, including the two most common ones, which are, Alzheimer's and Parkinson's disease. Currently, no pharmacological therapies have been developed to revert or cure any of these pathologies. Interestingly, in recent years, an increased number of studies have shown a high co-morbidity between T2DM and neurodegeneration, as well as some common molecular pathways that are affected in both types of diseases. For example, while the etiopathology of T2DM and neurodegenerative disorders is highly complex, mitochondrial dysfunction has been broadly described in the early steps of both diseases; accordingly, this dysfunction has emerged as a plausible molecular link between them. In fact, the prominent role played by mitochondria in the mammalian metabolism of glucose places the physiology of the organelle in a central position to regulate many cellular processes that are affected in both T2DM and neurodegenerative disorders. In this collaborative review, we critically describe the relationship between T2DM and neurodegeneration; making a special emphasis on the mitochondrial mechanisms that link these diseases. A better understanding of the role of mitochondria on the etiopathology of T2DM and neurodegeneration could pave the way for the development of new pharmacological therapies focused on the regulation of the physiology of the organelle. These therapies could, ultimately, contribute to increase health span.

Mind Drifts, Data Shifts: Utilizing Mind Wandering to Track the Evolution of User Experience with Data Visualizations.

User experience in data visualization is typically assessed through post-viewing self-reports, but these overlook the dynamic cognitive processes during interaction. This study explores the use of mind wandering- a phenomenon where attention spontaneously shifts from a primary task to internal, task-related thoughts or unrelated distractions- as a dynamic measure during visualization exploration. Participants reported mind wandering while viewing visualizations from a pre-labeled visualization database and then provided quantitative ratings of trust, engagement, and design quality, along with qualitative descriptions and short-term/long-term recall assessments. Results show that mind wandering negatively affects short-term visualization recall and various post-viewing measures, particularly for visualizations with little text annotation. Further, the type of mind wandering impacts engagement and emotional response. Mind wandering also functions as an intermediate process linking visualization design elements to post-viewing measures, influencing how viewers engage with and interpret visual information over time. Overall, this research underscores the importance of incorporating mind wandering as a dynamic measure in visualization design and evaluation, offering novel avenues for enhancing user engagement and comprehension.

Defogger: A Visual Analysis Approach for Data Exploration of Sensitive Data Protected by Differential Privacy.

Differential privacy ensures the security of individual privacy but poses challenges to data exploration processes because the limited privacy budget incapacitates the flexibility of exploration and the noisy feedback of data requests leads to confusing uncertainty. In this study, we take the lead in describing corresponding exploration scenarios, including underlying requirements and available exploration strategies. To facilitate practical applications, we propose a visual analysis approach to the formulation of exploration strategies. Our approach applies a reinforcement learning model to provide diverse suggestions for exploration strategies according to the exploration intent of users. A novel visual design for representing uncertainty in correlation patterns is integrated into our prototype system to support the proposed approach. Finally, we implemented a user study and two case studies. The results of these studies verified that our approach can help develop strategies that satisfy the exploration intent of users.

Image or Information? Examining the Nature and Impact of Visualization Perceptual Classification.

How do people internalize visualizations: as images or information? In this study, we investigate the nature of internalization for visualizations (i.e., how the mind encodes visualizations in memory) and how memory encoding affects its retrieval. This exploratory work examines the influence of various design elements on a user's perception of a chart. Specifically, which design elements lead to perceptions of visualization as an image (aims to provide visual references, evoke emotions, express creativity, and inspire philosophic thought) or as information (aims to present complex data, information, or ideas concisely and promote analytical thinking)? Understanding how design elements contribute to viewers perceiving a visualization more as an image or information will help designers decide which elements to include to achieve their communication goals. For this study, we annotated 500 visualizations and analyzed the responses of 250 online participants, who rated the visualizations on a bilinear scale as 'image' or 'information.' We then conducted an in-person study ( n = 101) using a free recall task to examine how the image/information ratings and design elements impacted memory. The results revealed several interesting findings: Image-rated visualizations were perceived as more aesthetically 'appealing,' 'enjoyable,' and 'pleasing.' Information-rated visualizations were perceived as less 'difficult to understand' and more aesthetically 'likable' and 'nice,' though participants expressed higher 'positive' sentiment when viewing image-rated visualizations and felt less 'guided to a conclusion.' The presence of axes and text annotations heavily influenced the likelihood of participants rating the visualization as 'information.' We also found different patterns among participants that were older. Importantly, we show that visualizations internalized as 'images' are less effective in conveying trends and messages, though they elicit a more positive emotional judgment, while 'informative' visualizations exhibit annotation focused recall and elicit a more positive design judgment. We discuss the implications of this dissociation between aesthetic pleasure and perceived ease of use in visualization design.

ChartStory: Automated Partitioning, Layout, and Captioning of Charts into Comic-Style Narratives.

Visual data storytelling is gaining importance as a means of presenting data-driven information or analysis results, especially to the general public. This has resulted in design principles being proposed for data-driven storytelling, and new authoring tools being created to aid such storytelling. However, data analysts typically lack sufficient background in design and storytelling to make effective use of these principles and authoring tools. To assist this process, we present ChartStory for crafting data stories from a collection of user-created charts, using a style akin to comic panels to imply the underlying sequence and logic of data-driven narratives. Our approach is to operationalize established design principles into an advanced pipeline that characterizes charts by their properties and similarities to each other, and recommends ways to partition, layout, and caption story pieces to serve a narrative. ChartStory also augments this pipeline with intuitive user interactions for visual refinement of generated data comics. We extensively and holistically evaluate ChartStory via a trio of studies. We first assess how the tool supports data comic creation in comparison to a manual baseline tool. Data comics from this study are subsequently compared and evaluated to ChartStory's automated recommendations by a team of narrative visualization practitioners. This is followed by a pair of interview studies with data scientists using their own datasets and charts who provide an additional assessment of the system. We find that ChartStory provides cogent recommendations for narrative generation, resulting in data comics that compare favorably to manually-created ones.

PMU Tracker: A Visualization Platform for Epicentric Event Propagation Analysis in the Power Grid.

The electrical power grid is a critical infrastructure, with disruptions in transmission having severe repercussions on daily activities, across multiple sectors. To identify, prevent, and mitigate such events, power grids are being refurbished as 'smart' systems that include the widespread deployment of GPS-enabled phasor measurement units (PMUs). PMUs provide fast, precise, and time-synchronized measurements of voltage and current, enabling real-time wide-area monitoring and control. However, the potential benefits of PMUs, for analyzing grid events like abnormal power oscillations and load fluctuations, are hindered by the fact that these sensors produce large, concurrent volumes of noisy data. In this paper, we describe working with power grid engineers to investigate how this problem can be addressed from a visual analytics perspective. As a result, we have developed PMU Tracker, an event localization tool that supports power grid operators in visually analyzing and identifying power grid events and tracking their propagation through the power grid's network. As a part of the PMU Tracker interface, we develop a novel visualization technique which we term an epicentric cluster dendrogram, which allows operators to analyze the effects of an event as it propagates outwards from a source location. We robustly validate PMU Tracker with: (1) a usage scenario demonstrating how PMU Tracker can be used to analyze anomalous grid events, and (2) case studies with power grid operators using a real-world interconnection dataset. Our results indicate that PMU Tracker effectively supports the analysis of power grid events; we also demonstrate and discuss how PMU Tracker's visual analytics approach can be generalized to other domains composed of time-varying networks with epicentric event characteristics.

ConceptExplainer: Interactive Explanation for Deep Neural Networks from a Concept Perspective.

Traditional deep learning interpretability methods which are suitable for model users cannot explain network behaviors at the global level and are inflexible at providing fine-grained explanations. As a solution, concept-based explanations are gaining attention due to their human intuitiveness and their flexibility to describe both global and local model behaviors. Concepts are groups of similarly meaningful pixels that express a notion, embedded within the network's latent space and have commonly been hand-generated, but have recently been discovered by automated approaches. Unfortunately, the magnitude and diversity of discovered concepts makes it difficult to navigate and make sense of the concept space. Visual analytics can serve a valuable role in bridging these gaps by enabling structured navigation and exploration of the concept space to provide concept-based insights of model behavior to users. To this end, we design, develop, and validate CONCEPTEXPLAINER, a visual analytics system that enables people to interactively probe and explore the concept space to explain model behavior at the instance/class/global level. The system was developed via iterative prototyping to address a number of design challenges that model users face in interpreting the behavior of deep learning models. Via a rigorous user study, we validate how CONCEPTEXPLAINER supports these challenges. Likewise, we conduct a series of usage scenarios to demonstrate how the system supports the interactive analysis of model behavior across a variety of tasks and explanation granularities, such as identifying concepts that are important to classification, identifying bias in training data, and understanding how concepts can be shared across diverse and seemingly dissimilar classes.

Atrazine exposure in zebrafish induces aberrant genome-wide methylation.

Atrazine (ATZ) is the second most common agricultural herbicide used in the United States and is an endocrine disrupting chemical (EDC). Developmental exposure to ATZ can lead to significant behavioral and morphological alterations in exposed animals and their progeny suggesting the involvement of an epigenetic mechanism. Specific epigenetic mechanisms responsible for these alterations, however, are yet to be elucidated. In this study, we exposed zebrafish embryos to 0, 0.3, 3, or 30 ppb (µg/L) of ATZ from 1 to 72 h post fertilization (hpf). Chemical exposure was ceased and zebrafish maintained until 9 months post fertilization (mpf), when whole-genome bisulfite sequencing (WGBS) was performed to assess the effects of embryonic ATZ exposure on DNA methylation in female fish brains. The number of differentially methylated genes (DMGs) increased with increasing treatment concentration. DMGs were enriched in neurological pathways with extensive methylation changes consistently observed in neuroendocrine pathways. Specifically, DMGs with methylation changes in promoter regions showed hypomethylation in estrogen receptor signaling and hypermethylation in androgen signaling. DMGs with methylation changes in genebody were primarily enriched for mitochondrion-related pathways associated with healthy aging. Integrated analysis with transcriptomic data at 9 mpf exhibited a similar trend identifying CABLES1 and NDUFA4 as shared targets at all concentrations. We then compared the predicted upstream regulators of transcriptomic changes with DMGs and identified CALML3 as a common upstream regulator at both 0.3 and 30 ppb that exhibit significant methylation changes. Collectively, our study identified long-lasting DNA methylation changes in genome after embryonic ATZ exposure and elucidated potential gene targets whose aberrant methylation features may drive alterations in gene transcription in long-term.

PMUVis : A Large-Scale Platform to Assist Power System Operators in a Smart Grid.

Electric transmission power grids are being revamped with the widespread deployment of GPS-enabled phasor measurement units (PMUs) for real-time wide-area monitoring and control via precise, time-synchronized measurements of voltage and current. Large, concurrently produced volumes of noisy data hinder PMU usability, particularly for the analysis of power oscillation and load fluctuation events in the grid. We examine visualization challenges for events in the electric power grid and develop PMUVis, a visualization platform that supports scalable analysis of grid network topology and anomalous events in near time. PMUVis incorporates a novel FFT-based approach over raw and temporally aggregated data to examine oscillation event propagation through the grid network. We validate PMUVis with expert reviews and a case study and discuss how visualization can be leveraged to enhance real-time spatiotemporal grid analysis by advancing operator capabilities.

Umbra: A Visual Analysis Approach for Defense Construction Against Inference Attacks on Sensitive Information.

Collecting and analyzing anonymous personal information is required as a part of data analysis processes, such as medical diagnosis and restaurant recommendation. Such data should ostensibly be stored so that specific individual information cannot be disclosed. Unfortunately, inference attacks-integrating background knowledge and intelligent models-hinder classic sanitization techniques like syntactic anonymity and differential privacy from exhaustively protecting sensitive information. As a solution, we introduce a three-stage approach empowered within a visual interface, which depicts underlying inference behaviors via a Bayesian Network and supports a customized defense against inference attacks from unknown adversaries. In particular, our approach visually explains the process details of the underlying privacy preserving models, allowing users to verify if the results sufficiently satisfy the requirements of privacy preservation. We demonstrate the effectiveness of our approach through two case studies and expert reviews.