Visualizing latent domain knowledge

Knowledge discovery and data mining commonly rely on finding salient patterns of association from a vast amount of data. Traditional citation analysis of scientific literature draws insights from strong citation patterns. Latent domain knowledge, in contrast to the mainstream domain knowledge, often consists of highly relevant but relatively infrequently cited scientific works. Visualizing latent domain knowledge presents a significant challenge to knowledge discovery and quantitative studies of science. We build upon a citation-based knowledge visualization procedure and develop an approach that not only captures knowledge structures from prominent and highly cited works, but also traces latent domain knowledge through low-frequency citation chains. We apply this approach to two cases: (1) identifying cross-domain applications of Pathfinder networks (PFNETs) and (2) clarifying the current status of scientific inquiry of a possible link between Bovine spongiform encephalopathy (BSE), also known as mad cow disease, and a new variant Creutzfeldt-Jakob disease (vCJD), a type of brain disease in human

A document-like software visualization method for effective cognition of c-based software systems

It is clear that maintenance is a crucial and very costly process in a software life cycle. Nowadays there are a lot of software systems particularly legacy systems that are always maintained from time to time as new requirements arise. One important source to understand a software system before it is being maintained is through the documentation, particularly system documentation. Unfortunately, not all software systems developed or maintained are accompanied with their reliable and updated documents. In this case, source codes will be the only reliable source for programmers. A number of studies have been carried out in order to assist cognition based on source codes. One way is through tool automation via reverse engineering technique in which source codes will be parsed and the information extracted will be visualized using certain visualization methods. Most software visualization methods use graph as the main element to represent extracted software artifacts. Nevertheless, current methods tend to produce more complicated graphs and do not grant an explicit, document-like re-documentation environment. Hence, this thesis proposes a document-like software visualization method called DocLike Modularized Graph (DMG). The method is realized in a prototype tool named DocLike Viewer that targets on C-based software systems. The main contribution of the DMG method is to provide an explicit structural re-document mechanism in the software visualization tool. Besides, the DMG method provides more level of information abstractions via less complex graph that include inter-module dependencies, inter-program dependencies, procedural abstraction and also parameter passing. The DMG method was empirically evaluated based on the Goal/Question/Metric (GQM) paradigm and the findings depict that the method can improve productivity and quality in the aspect of cognition or program comprehension. A usability study was also conducted and DocLike Viewer had the most positive responses from the software practitioners

Portinari: A Data Exploration Tool to Personalize Cervical Cancer Screening

Socio-technical systems play an important role in public health screening programs to prevent cancer. Cervical cancer incidence has significantly decreased in countries that developed systems for organized screening engaging medical practitioners, laboratories and patients. The system automatically identifies individuals at risk of developing the disease and invites them for a screening exam or a follow-up exam conducted by medical professionals. A triage algorithm in the system aims to reduce unnecessary screening exams for individuals at low-risk while detecting and treating individuals at high-risk. Despite the general success of screening, the triage algorithm is a one-size-fits all approach that is not personalized to a patient. This can easily be observed in historical data from screening exams. Often patients rely on personal factors to determine that they are either at high risk or not at risk at all and take action at their own discretion. Can exploring patient trajectories help hypothesize personal factors leading to their decisions? We present Portinari, a data exploration tool to query and visualize future trajectories of patients who have undergone a specific sequence of screening exams. The web-based tool contains (a) a visual query interface (b) a backend graph database of events in patients' lives (c) trajectory visualization using sankey diagrams. We use Portinari to explore diverse trajectories of patients following the Norwegian triage algorithm. The trajectories demonstrated variable degrees of adherence to the triage algorithm and allowed epidemiologists to hypothesize about the possible causes.Comment: Conference paper published at ICSE 2017 Buenos Aires, at the Software Engineering in Society Track. 10 pages, 5 figure

Effect of age and cytoskeletal elements on the indentation-dependent mechanical properties of chondrocytes.

Articular cartilage chondrocytes are responsible for the synthesis, maintenance, and turnover of the extracellular matrix, metabolic processes that contribute to the mechanical properties of these cells. Here, we systematically evaluated the effect of age and cytoskeletal disruptors on the mechanical properties of chondrocytes as a function of deformation. We quantified the indentation-dependent mechanical properties of chondrocytes isolated from neonatal (1-day), adult (5-year) and geriatric (12-year) bovine knees using atomic force microscopy (AFM). We also measured the contribution of the actin and intermediate filaments to the indentation-dependent mechanical properties of chondrocytes. By integrating AFM with confocal fluorescent microscopy, we monitored cytoskeletal and biomechanical deformation in transgenic cells (GFP-vimentin and mCherry-actin) under compression. We found that the elastic modulus of chondrocytes in all age groups decreased with increased indentation (15-2000 nm). The elastic modulus of adult chondrocytes was significantly greater than neonatal cells at indentations greater than 500 nm. Viscoelastic moduli (instantaneous and equilibrium) were comparable in all age groups examined; however, the intrinsic viscosity was lower in geriatric chondrocytes than neonatal. Disrupting the actin or the intermediate filament structures altered the mechanical properties of chondrocytes by decreasing the elastic modulus and viscoelastic properties, resulting in a dramatic loss of indentation-dependent response with treatment. Actin and vimentin cytoskeletal structures were monitored using confocal fluorescent microscopy in transgenic cells treated with disruptors, and both treatments had a profound disruptive effect on the actin filaments. Here we show that disrupting the structure of intermediate filaments indirectly altered the configuration of the actin cytoskeleton. These findings underscore the importance of the cytoskeletal elements in the overall mechanical response of chondrocytes, indicating that intermediate filament integrity is key to the non-linear elastic properties of chondrocytes. This study improves our understanding of the mechanical properties of articular cartilage at the single cell level

Multiple Uncertainties in Time-Variant Cosmological Particle Data

Though the mediums for visualization are limited, the potential dimensions of a dataset are not. In many areas of scientific study, understanding the correlations between those dimensions and their uncertainties is pivotal to mining useful information from a dataset. Obtaining this insight can necessitate visualizing the many relationships among temporal, spatial, and other dimensionalities of data and its uncertainties. We utilize multiple views for interactive dataset exploration and selection of important features, and we apply those techniques to the unique challenges of cosmological particle datasets. We show how interactivity and incorporation of multiple visualization techniques help overcome the problem of limited visualization dimensions and allow many types of uncertainty to be seen in correlation with other variables