11 resultados para visualize
em Aston University Research Archive
Resumo:
In data visualization, characterizing local geometric properties of non-linear projection manifolds provides the user with valuable additional information that can influence further steps in the data analysis. We take advantage of the smooth character of GTM projection manifold and analytically calculate its local directional curvatures. Curvature plots are useful for detecting regions where geometry is distorted, for changing the amount of regularization in non-linear projection manifolds, and for choosing regions of interest when constructing detailed lower-level visualization plots.
Resumo:
Multidimensional compound optimization is a new paradigm in the drug discovery process, yielding efficiencies during early stages and reducing attrition in the later stages of drug development. The success of this strategy relies heavily on understanding this multidimensional data and extracting useful information from it. This paper demonstrates how principled visualization algorithms can be used to understand and explore a large data set created in the early stages of drug discovery. The experiments presented are performed on a real-world data set comprising biological activity data and some whole-molecular physicochemical properties. Data visualization is a popular way of presenting complex data in a simpler form. We have applied powerful principled visualization methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), to help the domain experts (screening scientists, chemists, biologists, etc.) understand and draw meaningful decisions. We also benchmark these principled methods against relatively better known visualization approaches, principal component analysis (PCA), Sammon's mapping, and self-organizing maps (SOMs), to demonstrate their enhanced power to help the user visualize the large multidimensional data sets one has to deal with during the early stages of the drug discovery process. The results reported clearly show that the GTM and HGTM algorithms allow the user to cluster active compounds for different targets and understand them better than the benchmarks. An interactive software tool supporting these visualization algorithms was provided to the domain experts. The tool facilitates the domain experts by exploration of the projection obtained from the visualization algorithms providing facilities such as parallel coordinate plots, magnification factors, directional curvatures, and integration with industry standard software. © 2006 American Chemical Society.
Resumo:
The molecular chaperone, Hsc70, together with its cofactor, auxilin, facilitates the ATP-dependent removal of clathrin during clathrin-mediated endocytosis in cells. We have used cryo-electron microscopy to determine the 3D structure of a complex of clathrin, auxilin401-910 and Hsc70 at pH 6 in the presence of ATP, frozen within 20 seconds of adding Hsc70 in order to visualize events that follow the binding of Hsc70 to clathrin and auxilin before clathrin disassembly. In this map,we observe density beneath the vertex of the cage that we attribute to bound Hsc70. This density emerges asymmetrically from the clathrin vertex, suggesting preferential binding by Hsc70 for one of the three possible sites at the vertex. Statistical comparison with a map of whole auxilin and clathrin previously published by us reveals the location of statistically significant differences which implicate involvement of clathrin light chains in structural rearrangements which occur after Hsc70 is recruited. Clathrin disassembly assays using light scattering suggest that loss of clathrin light chains reduces the efficiency with which auxilin facilitates this reaction. These data support a regulatory role for clathrin light chains in clathrin disassembly in addition to their established role in regulating clathrin assembly. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Resumo:
The molecular dynamics (MD) simulations play a very important role in science today. They have been used successfully in binding free-energy calculations and rational design of drugs and vaccines. MD simulations can help visualize and understand structures and dynamics at an atomistic level when combined with molecular graphics programs. The molecular and atomistic properties can be displayed on a computer in a time-dependent way, which opens a road toward a better understanding of the relationship of structure, dynamics, and function. In this chapter, the basics of MD are explained, together with a step-by-step description of setup and running an MD simulation.
Resumo:
Recently, we have developed the hierarchical Generative Topographic Mapping (HGTM), an interactive method for visualization of large high-dimensional real-valued data sets. In this paper, we propose a more general visualization system by extending HGTM in three ways, which allows the user to visualize a wider range of data sets and better support the model development process. 1) We integrate HGTM with noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM). This enables us to visualize data of inherently discrete nature, e.g., collections of documents, in a hierarchical manner. 2) We give the user a choice of initializing the child plots of the current plot in either interactive, or automatic mode. In the interactive mode, the user selects "regions of interest," whereas in the automatic mode, an unsupervised minimum message length (MML)-inspired construction of a mixture of LTMs is employed. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. 3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualization plots, since they can highlight the boundaries between data clusters. We illustrate our approach on a toy example and evaluate it on three more complex real data sets. © 2005 IEEE.
Resumo:
Flow cytometry and confocal microscopy were used to quantify and visualize FITC-lectin binding to cell-surface carbohydrate ligands of log and stationary phase acapsular and capsular Cryptococcus neoformans strains. Cell populations demonstrated marked avidity for terminal a-linked mannose and glucose specific FITC-Con A, mannose specific FITC-GNL, as well as N-acetylglucosamine specific FITC-WGA. Exposure to other FITC-lectins specific for mannose, fucose and N-acetylgalactosamine resulted in little cell-surface fluorescence. The nature of cell-surface carbohydrates was investigated further by measurement of the fluorescence from surfaces of log and stationary phase cell populations after exposing them to increasing concentrations of FITC-Con A and FITC-WGA. Cell fluorescence increased significantly with small increases in FITC-Con A and FITC-WGA concentrations attaining reproducible maxima. Measurements of this nature supported calculation of the lectin binding determinants EC 50, Hn, Fmax and relative Bmax values. EC50 values indicated that the yeast-cell surfaces had greatest affinity for FITC-WGA, however, relative Bmax values indicated that greater numbers of Con A binding sites were present on these same cell surfaces. Hn values suggested a co-operative lectin-carbohydrate ligand interaction. Imaging of FITC-Con A and FITC-WGA cell-surface fluorescence by confocal microscopy demonstrated marked localization of both lectins to cell surfaces associated with cell division and maturation, indicative of dynamic carbohydrate ligand exposure and masking. Some fluorescence was associated with entrapment of FITC-Con A by capsular components, but FITC-Con A and FITC-WGA readily penetrated the capsule matrix to bind to the same cell surfaces labelled in acapsular cells.
Resumo:
This article reports on an investigationwith first year undergraduate ProductDesign and Management students within a School of Engineering and Applied Science. The students at the time of this investigation had studied fundamental engineering science and mathematics for one semester. The students were given an open ended, ill-formed problem which involved designing a simple bridge to cross a river.They were given a talk on problemsolving and given a rubric to follow, if they chose to do so.They were not given any formulae or procedures needed in order to resolve the problem. In theory, they possessed the knowledge to ask the right questions in order tomake assumptions but, in practice, it turned out they were unable to link their a priori knowledge to resolve this problem. They were able to solve simple beam problems when given closed questions. The results show they were unable to visualize a simple bridge as an augmented beam problem and ask pertinent questions and hence formulate appropriate assumptions in order to offer resolutions.
Resumo:
The cardiovascular health of the human population is a major concern for medical clinicians, with cardiovascular diseases responsible for 48% of all deaths worldwide, according to the World Health Organization. The development of new diagnostic tools that are practicable and economical to scrutinize the cardiovascular health of humans is a major driver for clinicians. We offer a new technique to obtain seismocardiographic signals up to 54 Hz covering both ballistocardiography (below 20 Hz) and audible heart sounds (20 Hz upward), using a system based on curvature sensors formed from fiber optic long period gratings. This system can visualize the real-time three-dimensional (3-D) mechanical motion of the heart by using the data from the sensing array in conjunction with a bespoke 3-D shape reconstruction algorithm. Visualization is demonstrated by adhering three to four sensors on the outside of the thorax and in close proximity to the apex of the heart; the sensing scheme revealed a complex motion of the heart wall next to the apex region of the heart. The detection scheme is low-cost, portable, easily operated and has the potential for ambulatory applications.
Resumo:
Purpose: In golf, the impact of eye-hand dominance on putting performance has long been debated. Eye-hand dominance is thought to impact how golfers judge the alignment of the ball with the target and the club with the ball, as well as how golfers visualize the line of the putt when making decisions about the force needed to hit the ball. Previous studies have all measured ocular dominance in primary gaze only, despite golfers spending a significant amount of their time in a putting stance (bent at the hips, head tilted down). Thus, the purpose of this study was to assess ocular dominance in both primary gaze (aligning the ball with the target) and putting gaze (addressing the ball and aligning the club). Methods: This study investigatedmeasuring pointing oculardominance in both primary and putting gaze positions on 31 golfers (14 amateur, 7 club professionals, and 10 top professionals). All playerswere right-handed golfers, although one reported having no hand dominance and one reported being strongly left hand dominant. Results: The results showed that (1) primary and putting gaze ocular dominances are not equal, nor are they predictive of each other; (2) themagnitude of putting ocular dominance is significantly less than themagnitude of primary gaze ocular dominance; (3) ocular dominance is not correlated with handedness in either primary or putting gaze; and (4) eye-hand dominance is not associated with increased putting skill, although ocular dominance may be associated with increased putting success. Conclusions: It is important that coaches assess golfers' ocular dominance in both primary and putting gaze positions to ensure they have the most accurate information upon which to base their vision strategy decisions.
Resumo:
The aim of this paper is to propose a conceptual framework for studying the knowledge transfer problem within the supply chain. The social network analysis (SNA) is presented as a useful tool to study knowledge networks within supply chain, to visualize knowledge flows and to identify the accumulating knowledge nodes of the networks. © 2011 IEEE.
Resumo:
Event extraction from texts aims to detect structured information such as what has happened, to whom, where and when. Event extraction and visualization are typically considered as two different tasks. In this paper, we propose a novel approach based on probabilistic modelling to jointly extract and visualize events from tweets where both tasks benefit from each other. We model each event as a joint distribution over named entities, a date, a location and event-related keywords. Moreover, both tweets and event instances are associated with coordinates in the visualization space. The manifold assumption that the intrinsic geometry of tweets is a low-rank, non-linear manifold within the high-dimensional space is incorporated into the learning framework using a regularization. Experimental results show that the proposed approach can effectively deal with both event extraction and visualization and performs remarkably better than both the state-of-the-art event extraction method and a pipeline approach for event extraction and visualization.