Elevation of cardiac glycolysis reduces pyruvate dehydrogenase but increases glucose oxidation.

Heart failure is the most frequent cause of mortality in western countries. Currently, there is no cure treatment for heart failure and the long term survival rate following heart failure is poor, with one third of patients dying within a year of diagnosis. Thus, new therapeutic targets have to be developed. Enhanced glycolysis is a very common phenomenon in the development of heart failure and maybe a target for drug development. However it is not know whether the increased glycolysis is a cause or an effect of heart failure. Also, metabolic modulators to increase glucose use by the heart have been used acutely in treatment in heart failure but the long term impact of increased glycolysis is not known. To understand whether chronically increased glycolysis specifically in the heart is beneficial or detrimental, glycolysis was chronically elevated by cardiac-specific overexpression of a modified, phosphatase-deficient 6-phosphofructo-2-kinase/fructose-2, 6-bisphosphatase (PFK-2) in transgenic mice. PFK-2 controls the level offructose-2, 6-bisphosphate (Fru-2, 6-P2), an important regulator of phosphofructokinase and glycolysis. These transgenic mice were used to test two hypotheses: (1) Long term elevation of cardiac Fru-2, 6-P2 will increase glycolysis and alter glucose oxidation. (2) Chronically increased cardiac glycolysis will be detrimental to the heart. To test these hypotheses we carried out three specific aims: Aim I was to produce transgenic mice with overexpression of phosphatase-deficient 6-phosphofructo-2-kinase/fructose-2, 6-bisphosphatase (PFK-2). Aim 2 was to compare metabolites and glucose metabolism in transgenic and control samples using whole hearts, Langendorff perfused hearts and cultured adult cardiomyocytes. Aim 3 was to assess whether chronically increased glycolysis promotes cardiac fibrosis, hypertrophy or impaired function. The results demonstrated a new line of transgenic mice called Mk, with cardiac expression of modified PFK2 and increased levels of Fru-2, 6-P2. Mk hearts had elevated glycolysis that was less sensitive to inhibition by palmitate. Mk cardiomyocytes had increased glucose oxidation despite reduced pyruvate dehydrogenase complex (PDC) activity. PDC activity was decreased because of reduced protein levels of PDC subunit Ela and because of increased PDC Ela phosphorylation. Mk hearts had increased mitochondrial level of MCT -2 transporter protein and malate content. The increased malate content and elevated MCT2 expression suggested that anaplerosis pathways in transgenic hearts might explain the paradoxical finding of reduced PDC activity and elevated glucose oxidation. Functional studies revealed that the elevation in glycolysis made transgenic cardiomyocytes highly resistant to contractile inhibition by hypoxia, in vitro. However, in vivo the transgene had no protective effects on ischemia-reperfusion injury. Furthermore, the transgenic hearts exhibited pathologic changes that included a 17% increase of the heart weight-to-body weight ratio, greater cardiomyocyte length and increased cardiac fibrosis. Therefore, chronic elevation of glycolysis produced more pathological effects than protective effects on the heart

A Lightweight Modular Continuum Manipulator with IMU-based Force Estimation

Most aerial manipulators use serial rigid-link designs, which results in large forces when initiating contacts during manipulation and could cause flight stability difficulty. This limitation could potentially be improved by the compliance of continuum manipulators. To achieve this goal, we present the novel design of a compact, lightweight, and modular cable-driven continuum manipulator for aerial drones. We then derive a complete modeling framework for its kinematics, statics, and stiffness (compliance). The modeling framework can guide the control and design problems to integrate the manipulator to aerial drones. In addition, thanks to the derived stiffness (compliance) matrix, and using a low-cost IMU sensor to capture deformation angles, we present a simple method to estimate manipulation force at the tip of the manipulator. We report preliminary experimental validations of the hardware prototype, providing insights on its manipulation feasibility. We also report preliminary results of the IMU-based force estimation method.Comment: 12 pages, submitted to ASME Journal of Mechanisms and Robotics 2022, under review. arXiv admin note: substantial text overlap with arXiv:2206.0624

A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization

Inspired by the great success of machine learning (ML), researchers have applied ML techniques to visualizations to achieve a better design, development, and evaluation of visualizations. This branch of studies, known as ML4VIS, is gaining increasing research attention in recent years. To successfully adapt ML techniques for visualizations, a structured understanding of the integration of ML4VISis needed. In this paper, we systematically survey 88 ML4VIS studies, aiming to answer two motivating questions: "what visualization processes can be assisted by ML?" and "how ML techniques can be used to solve visualization problems?" This survey reveals seven main processes where the employment of ML techniques can benefit visualizations:Data Processing4VIS, Data-VIS Mapping, InsightCommunication, Style Imitation, VIS Interaction, VIS Reading, and User Profiling. The seven processes are related to existing visualization theoretical models in an ML4VIS pipeline, aiming to illuminate the role of ML-assisted visualization in general visualizations.Meanwhile, the seven processes are mapped into main learning tasks in ML to align the capabilities of ML with the needs in visualization. Current practices and future opportunities of ML4VIS are discussed in the context of the ML4VIS pipeline and the ML-VIS mapping. While more studies are still needed in the area of ML4VIS, we hope this paper can provide a stepping-stone for future exploration. A web-based interactive browser of this survey is available at https://ml4vis.github.ioComment: 19 pages, 12 figures, 4 table

Towards automated infographic design: Deep learning-based auto-extraction of extensible timeline

Designers need to consider not only perceptual effectiveness but also visual styles when creating an infographic. This process can be difficult and time consuming for professional designers, not to mention non-expert users, leading to the demand for automated infographics design. As a first step, we focus on timeline infographics, which have been widely used for centuries. We contribute an end-to-end approach that automatically extracts an extensible timeline template from a bitmap image. Our approach adopts a deconstruction and reconstruction paradigm. At the deconstruction stage, we propose a multi-task deep neural network that simultaneously parses two kinds of information from a bitmap timeline: 1) the global information, i.e., the representation, scale, layout, and orientation of the timeline, and 2) the local information, i.e., the location, category, and pixels of each visual element on the timeline. At the reconstruction stage, we propose a pipeline with three techniques, i.e., Non-Maximum Merging, Redundancy Recover, and DL GrabCut, to extract an extensible template from the infographic, by utilizing the deconstruction results. To evaluate the effectiveness of our approach, we synthesize a timeline dataset (4296 images) and collect a real-world timeline dataset (393 images) from the Internet. We first report quantitative evaluation results of our approach over the two datasets. Then, we present examples of automatically extracted templates and timelines automatically generated based on these templates to qualitatively demonstrate the performance. The results confirm that our approach can effectively extract extensible templates from real-world timeline infographics.Comment: 10 pages, Automated Infographic Design, Deep Learning-based Approach, Timeline Infographics, Multi-task Mode

ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning

To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge model search space, it is impossible to try all models. Users tend to distrust automatic results and increase the search budget as much as they can, thereby undermining the efficiency of AutoML. To address these issues, we design and implement ATMSeer, an interactive visualization tool that supports users in refining the search space of AutoML and analyzing the results. To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts. A multi-granularity visualization is proposed to enable users to monitor the AutoML process, analyze the searched models, and refine the search space in real time. We demonstrate the utility and usability of ATMSeer through two case studies, expert interviews, and a user study with 13 end users.Comment: Published in the ACM Conference on Human Factors in Computing Systems (CHI), 2019, Glasgow, Scotland U

Meaning of 'Science' and 'Religion' Related to Indigenous Knowledge of Human Origin and Life Course Among Indonesian and Chinese Students

By means of the open-ended questions inquiry this study try explore how the meaning of 'science' and 'religion' constructed culturally by Indonesian (n=204) and Chinese (n=125) university student in term of to deepen cross-cultural understanding. All of respondents are students in major of psychology and behavioral science. This study also demonstrate how this indigenous knowledge contributes implicitly to their preconception on human following three categories: (a) Origin of human; (b) Events in humans life span (i.e. Birth, mental activity, and death); and (c) The meaning of human existence. In this research science learning viewed as cultural 'crossing-border' (Aikenhead&Jegede, 1999). This research proves that students indigenous knowledge on religion and science has an influence on science learning, since they are systems of meaning that offer different answers to the same problem. Both group of respondents mostly see religion as a belief; but Indonesian students tend to interpret science as 'information and knowledge,' while the Chinese students tend to interpret it as 'the truth'. Related to the explanation of human originsand lifecourse, Indonesian students tendto involve theological explanation than Chinese students that rely more on science or other sources as the answer Beside this, Indonesian students are more prone to 'compartemerztized answer' (or 'parallel collatera llearning' according to Aikenhead & Jegede) rather than Chinese students in the topic of human origi

Visual analysis of discrimination in machine learning

The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning. How can we decide whether different treatments are reasonable or discriminatory? In this paper, we investigate discrimination in machine learning from a visual analytics perspective and propose an interactive visualization tool, DiscriLens, to support a more comprehensive analysis. To reveal detailed information on algorithmic discrimination, DiscriLens identifies a collection of potentially discriminatory itemsets based on causal modeling and classification rules mining. By combining an extended Euler diagram with a matrix-based visualization, we develop a novel set visualization to facilitate the exploration and interpretation of discriminatory itemsets. A user study shows that users can interpret the visually encoded information in DiscriLens quickly and accurately. Use cases demonstrate that DiscriLens provides informative guidance in understanding and reducing algorithmic discrimination

DeepDrawing: A deep learning approach to graph drawing

Node-link diagrams are widely used to facilitate network explorations. However, when using a graph drawing technique to visualize networks, users often need to tune different algorithm-specific parameters iteratively by comparing the corresponding drawing results in order to achieve a desired visual effect. This trial and error process is often tedious and time-consuming, especially for non-expert users. Inspired by the powerful data modelling and prediction capabilities of deep learning techniques, we explore the possibility of applying deep learning techniques to graph drawing. Specifically, we propose using a graph-LSTM-based approach to directly map network structures to graph drawings. Given a set of layout examples as the training dataset, we train the proposed graph-LSTM-based model to capture their layout characteristics. Then, the trained model is used to generate graph drawings in a similar style for new networks. We evaluated the proposed approach on two special types of layouts (i.e., grid layouts and star layouts) and two general types of layouts (i.e., ForceAtlas2 and PivotMDS) in both qualitative and quantitative ways. The results provide support for the effectiveness of our approach. We also conducted a time cost assessment on the drawings of small graphs with 20 to 50 nodes. We further report the lessons we learned and discuss the limitations and future work.Comment: 11 page