986 resultados para Process Visualization


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Analyzing and modeling relationships between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects in chemical datasets is a challenging task for scientific researchers in the field of cheminformatics. Therefore, (Q)SAR model validation is essential to ensure future model predictivity on unseen compounds. Proper validation is also one of the requirements of regulatory authorities in order to approve its use in real-world scenarios as an alternative testing method. However, at the same time, the question of how to validate a (Q)SAR model is still under discussion. In this work, we empirically compare a k-fold cross-validation with external test set validation. The introduced workflow allows to apply the built and validated models to large amounts of unseen data, and to compare the performance of the different validation approaches. Our experimental results indicate that cross-validation produces (Q)SAR models with higher predictivity than external test set validation and reduces the variance of the results. Statistical validation is important to evaluate the performance of (Q)SAR models, but does not support the user in better understanding the properties of the model or the underlying correlations. We present the 3D molecular viewer CheS-Mapper (Chemical Space Mapper) that arranges compounds in 3D space, such that their spatial proximity reflects their similarity. The user can indirectly determine similarity, by selecting which features to employ in the process. The tool can use and calculate different kinds of features, like structural fragments as well as quantitative chemical descriptors. Comprehensive functionalities including clustering, alignment of compounds according to their 3D structure, and feature highlighting aid the chemist to better understand patterns and regularities and relate the observations to established scientific knowledge. Even though visualization tools for analyzing (Q)SAR information in small molecule datasets exist, integrated visualization methods that allows for the investigation of model validation results are still lacking. We propose visual validation, as an approach for the graphical inspection of (Q)SAR model validation results. New functionalities in CheS-Mapper 2.0 facilitate the analysis of (Q)SAR information and allow the visual validation of (Q)SAR models. The tool enables the comparison of model predictions to the actual activity in feature space. Our approach reveals if the endpoint is modeled too specific or too generic and highlights common properties of misclassified compounds. Moreover, the researcher can use CheS-Mapper to inspect how the (Q)SAR model predicts activity cliffs. The CheS-Mapper software is freely available at http://ches-mapper.org.

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Data visualization is the process of representing data as pictures to support reasoning about the underlying data. For the interpretation to be as easy as possible, we need to be as close as possible to the original data. As most visualization tools have an internal meta-model, which is different from the one for the presented data, they usually need to duplicate the original data to conform to their meta-model. This leads to an increase in the resources needed, increase which is not always justified. In this work we argue for the need of having an engine that is as close as possible to the data and we present our solution of moving the visualization tool to the data, instead of moving the data to the visualization tool. Our solution also emphasizes the necessity of reusing basic blocks to express complex visualizations and allowing the programmer to script the visualization using his preferred tools, rather than a third party format. As a validation of the expressiveness of our framework, we show how we express several already published visualizations and describe the pros and cons of the approach.

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OBJECTIVE: To describe outcome after an alternative unilateral approach to the thoracolumbar spine for dorsal laminectomy. STUDY DESIGN: Retrospective clinical study. ANIMALS: Dogs (n=14) with thoracolumbar spinal cord compression. METHODS: Thoracolumbar spinal cord compression was lateral (6 dogs), dorsal (4), and dorsolateral (4) caused by subarachnoid (7) and synovial cysts (2) and intradural-extramedullary neoplasia (5). All dogs were treated by dorsal laminectomy with osteotomy of the spinous process using a unilateral paramedian approach. The contralateral paraspinal muscles were not stripped from the spinous process and the osteoligamentous complexes were preserved. Retraction of the spinous process and muscles to the contralateral side resulted in complete visualization of the dorsal vertebral arch thereby allowing dorsal laminectomy to be performed. RESULTS: No technique complications occurred. Approximately 75% exposure of the spinal cord (dorsal and lateral compartments) was achieved providing adequate visualization and treatment of the lesions. Transient deterioration of neurologic state occurred in 5 dogs because of extensive spinal cord manipulation. At long-term follow-up, 6 dogs were normal, 6 had clinical improvement, and 2 were unchanged. CONCLUSION: Dorsal laminectomy after osteotomy and retraction of the spinous process may be considered in canine patients with dorsal, dorsolateral, or lateral compression to facilitate adequate decompression of the spinal cord. CLINICAL SIGNIFICANCE: This surgical technique offers an alternative approach to the thoracolumbar spine and spinal cord by a modified dorsal laminectomy that preserves the paraspinal muscle integrity on the contralateral side.

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This paper introduces a mobile application (app) as the first part of an interactive framework. The framework enhances the inter-action between cities and their citizens, introducing the Fuzzy Analytical Hierarchy Process (FAHP) as a potential information acquisition method to improve existing citizen management en-deavors for cognitive cities. Citizen management is enhanced by advanced visualization using Fuzzy Cognitive Maps (FCM). The presented app takes fuzziness into account in the constant inter-action and continuous development of communication between cities or between certain of their entities (e.g., the tax authority) and their citizens. A transportation use case is implemented for didactical reasons.

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The fuzzy analytical network process (FANP) is introduced as a potential multi-criteria-decision-making (MCDM) method to improve digital marketing management endeavors. Today’s information overload makes digital marketing optimization, which is needed to continuously improve one’s business, increasingly difficult. The proposed FANP framework is a method for enhancing the interaction between customers and marketers (i.e., involved stakeholders) and thus for reducing the challenges of big data. The presented implementation takes realities’ fuzziness into account to manage the constant interaction and continuous development of communication between marketers and customers on the Web. Using this FANP framework, the marketers are able to increasingly meet the varying requirements of their customers. To improve the understanding of the implementation, advanced visualization methods (e.g., wireframes) are used.

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The reporting of outputs from health surveillance systems should be done in a near real-time and interactive manner in order to provide decision makers with powerful means to identify, assess, and manage health hazards as early and efficiently as possible. While this is currently rarely the case in veterinary public health surveillance, reporting tools do exist for the visual exploration and interactive interrogation of health data. In this work, we used tools freely available from the Google Maps and Charts library to develop a web application reporting health-related data derived from slaughterhouse surveillance and from a newly established web-based equine surveillance system in Switzerland. Both sets of tools allowed entry-level usage without or with minimal programing skills while being flexible enough to cater for more complex scenarios for users with greater programing skills. In particular, interfaces linking statistical softwares and Google tools provide additional analytical functionality (such as algorithms for the detection of unusually high case occurrences) for inclusion in the reporting process. We show that such powerful approaches could improve timely dissemination and communication of technical information to decision makers and other stakeholders and could foster the early-warning capacity of animal health surveillance systems.

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The control part of the execution of a constraint logic program can be conceptually shown as a search-tree, where nodes correspond to calis, and whose branches represent conjunctions and disjunctions. This tree represents the search space traversed by the program, and has also a direct relationship with the amount of work performed by the program. The nodes of the tree can be used to display information regarding the state and origin of instantiation of the variables involved in each cali. This depiction can also be used for the enumeration process. These are the features implemented in APT, a tool which runs constraint logic programs while depicting a (modified) search-tree, keeping at the same time information about the state of the variables at every moment in the execution. This information can be used to replay the execution at will, both forwards and backwards in time. These views can be abstracted when the size of the execution requires it. The search-tree view is used as a framework onto which constraint-level visualizations (such as those presented in the following chapter) can be attached.

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The arrangement of atoms at the surface of a solid accounts for many of its properties: Hardness, chemical activity, corrosion, etc. are dictated by the precise surface structure. Hence, finding it, has a broad range of technical and industrial applications. The ability to solve this problem opens the possibility of designing by computer materials with properties tailored to specific applications. Since the search space grows exponentially with the number of atoms, its solution cannot be achieved for arbitrarily large structures. Presently, a trial and error procedure is used: an expert proposes an structure as a candidate solution and tries a local optimization procedure on it. The solution relaxes to the local minimum in the attractor basin corresponding to the initial point, that might be the one corresponding to the global minimum or not. This procedure is very time consuming and, for reasonably sized surfaces, can take many iterations and much effort from the expert. Here we report on a visualization environment designed to steer this process in an attempt to solve bigger structures and reduce the time needed. The idea is to use an immersive environment to interact with the computation. It has immediate feedback to assess the quality of the proposed structure in order to let the expert explore the space of candidate solutions. The visualization environment is also able to communicate with the de facto local solver used for this problem. The user is then able to send trial structures to the local minimizer and track its progress as they approach the minimum. This allows for simultaneous testing of candidate structures. The system has also proved very useful as an educational tool for the field.

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In recent years, the continuous incorporation of new technologies in the learning process has been an important factor in the educational process [1]. The Technical University of Madrid (UPM) promotes educational innovation processes and develops projects related to the improvement of the education quality. The experience that we present fits into the Educational Innovation Project (EIP) of the E.U. of Agricultural Engineering of Madrid. One of the main objectives of the EIP is to "Take advantage of the new opportunities offered by the Learning and Knowledge Technologies in order to enrich the educational processes and teaching management" [2].

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In recent years, the continuous incorporation of new technologies in the learning process has been an important factor in the educational process (1). The Technical University of Madrid (UPM) promotes educational innovation processes and develops projects related to the improvement of the education quality. The experience that we present fits into the Educational Innovation Project (EIP) of the E.U. of Agricultural Engineering of Madrid. One of the main objectives of the EIP is to Take advantage of the new opportunities offered by the Learning and Knowledge Technologies in order to enrich the educational processes and teaching management (2).

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The human estrogen receptor α (ER α) has been tagged at its amino terminus with the S65T variant of the green fluorescent protein (GFP), allowing subcellular trafficking and localization to be observed in living cells by fluorescence microscopy. The tagged receptor, GFP-ER, is functional as a ligand-dependent transcription factor, responds to both agonist and antagonist ligands, and can associate with the nuclear matrix. Its cellular localization was analyzed in four human breast cancer epithelial cell lines, two ER+ (MCF7 and T47D) and two ER− (MDA-MB-231 and MDA-MB-435A), under a variety of ligand conditions. In all cell lines, GFP-ER is observed only in the nucleus in the absence of ligand. Upon the addition of agonist or antagonist ligand, a dramatic redistribution of GFP-ER from a reticular to punctate pattern occurs within the nucleus. In addition, the full antagonist ICI 182780 alters the nucleocytoplasmic compartmentalization of the receptor and causes partial accumulation in the cytoplasm in a process requiring continued protein synthesis. GFP-ER localization varies between cells, despite being cultured and treated in a similar manner. Analysis of the nuclear fluorescence intensity for variation in its frequency distribution helped establish localization patterns characteristic of cell line and ligand. During the course of this study, localization of GFP-ER to the nucleolar region is observed for ER− but not ER+ human breast cancer epithelial cell lines. Finally, our work provides a visual description of the “unoccupied” and ligand-bound receptor and is discussed in the context of the role of ligand in modulating receptor activity.

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During protein synthesis, elongation factor G (EF-G) binds to the ribosome and promotes the step of translocation, a process in which tRNA moves from the A to the P site of the ribosome and the mRNA is advanced by one codon. By using three-dimensional cryo-electron microscopy, we have visualized EF-G in a ribosome–EF-G–GDP–fusidic acid complex. Fitting the crystal structure of EF-G–GDP into the cryo density map reveals a large conformational change mainly associated with domain IV, the domain that mimics the shape of the anticodon arm of the tRNA in the structurally homologous ternary complex of Phe-tRNAPhe, EF-Tu, and a GTP analog. The tip portion of this domain is found in a position that overlaps the anticodon arm of the A-site tRNA, whose position in the ribosome is known from a study of the pretranslocational complex, implying that EF-G displaces the A-site tRNA to the P site by physical interaction with the anticodon arm.

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A highly fluorescent mutant form of the green fluorescent protein (GFP) has been fused to the rat glucocorticoid receptor (GR). When GFP-GR is expressed in living mouse cells, it is competent for normal transactivation of the GR-responsive mouse mammary tumor virus promoter. The unliganded GFP-GR resides in the cytoplasm and translocates to the nucleus in a hormone-dependent manner with ligand specificity similar to that of the native GR receptor. Due to the resistance of the mutant GFP to photobleaching, the translocation process can be studied by time-lapse video microscopy. Confocal laser scanning microscopy showed nuclear accumulation in a discrete series of foci, excluding nucleoli. Complete receptor translocation is induced with RU486 (a ligand with little agonist activity), although concentration into nuclear foci is not observed. This reproducible pattern of transactivation-competent GR reveals a previously undescribed intranuclear architecture of GR target sites.

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Terrain can be approximated by a triangular mesh consisting millions of 3D points. Multiresolution triangular mesh (MTM) structures are designed to support applications that use terrain data at variable levels of detail (LOD). Typically, an MTM adopts a tree structure where a parent node represents a lower-resolution approximation of its descendants. Given a region of interest (ROI) and a LOD, the process of retrieving the required terrain data from the database is to traverse the MTM tree from the root to reach all the nodes satisfying the ROI and LOD conditions. This process, while being commonly used for multiresolution terrain visualization, is inefficient as either a large number of sequential I/O operations or fetching a large amount of extraneous data is incurred. Various spatial indexes have been proposed in the past to address this problem, however level-by-level tree traversal remains a common practice in order to obtain topological information among the retrieved terrain data. A new MTM data structure called direct mesh is proposed. We demonstrate that with direct mesh the amount of data retrieval can be substantially reduced. Comparing with existing MTM indexing methods, a significant performance improvement has been observed for real-life terrain data.

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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.