The ovine cerebral venous system: comparative anatomy, visualization, and implications for translational research.

Cerebrovascular diseases are significant causes of death and disability in humans. Improvements in diagnostic and therapeutic approaches strongly rely on adequate gyrencephalic, large animal models being demanded for translational research. Ovine stroke models may represent a promising approach but are currently limited by insufficient knowledge regarding the venous system of the cerebral angioarchitecture. The present study was intended to provide a comprehensive anatomical analysis of the intracranial venous system in sheep as a reliable basis for the interpretation of experimental results in such ovine models. We used corrosion casts as well as contrast-enhanced magnetic resonance venography to scrutinize blood drainage from the brain. This combined approach yielded detailed and, to some extent, novel findings. In particular, we provide evidence for chordae Willisii and lateral venous lacunae, and report on connections between the dorsal and ventral sinuses in this species. For the first time, we also describe venous confluences in the deep cerebral venous system and an 'anterior condylar confluent' as seen in humans. This report provides a detailed reference for the interpretation of venous diagnostic imaging findings in sheep, including an assessment of structure detectability by in vivo (imaging) versus ex vivo (corrosion cast) visualization methods. Moreover, it features a comprehensive interspecies-comparison of the venous cerebral angioarchitecture in man, rodents, canines and sheep as a relevant large animal model species, and describes possible implications for translational cerebrovascular research.

Visualization of cell microtubules in their native state.

BACKGROUND INFORMATION Over the past decades, cryo-electron microscopy of vitrified specimens has yielded a detailed understanding of the tubulin and microtubule structures of samples reassembled in vitro from purified components. However, our knowledge of microtubule structure in vivo remains limited by the chemical treatments commonly used to observe cellular architecture using electron microscopy. RESULTS We used cryo-electron microscopy and cryo-electron tomography of vitreous sections to investigate the ultrastructure of microtubules in their cellular context. Vitreous sections were obtained from organotypic slices of rat hippocampus and from Chinese-hamster ovary cells in culture. Microtubules revealed their protofilament ultrastructure, polarity and, in the most favourable cases, molecular details comparable with those visualized in three-dimensional reconstructions of microtubules reassembled in vitro from purified tubulin. The resolution of the tomograms was estimated to be approx. 4 nm, which enabled the detection of luminal particles of approx. 6 nm in diameter inside microtubules. CONCLUSIONS The present study provides a first step towards a description of microtubules, in addition to other macromolecular assemblies, in an unperturbed cellular context at the molecular level. As the resolution appears to be similar to that obtainable with plunge-frozen samples, it should allow for the in vivo identification of larger macromolecular assemblies in vitreous sections of whole cells and tissues.

Renal Cryoablation: Investigation of Periprocedural Visualization Tools and Treatment Response Quantification

Cryoablation for small renal tumors has demonstrated sufficient clinical efficacy over the past decade as a non-surgical nephron-sparing approach for treating renal masses for patients who are not surgical candidates. Minimally invasive percutaneous cryoablations have been performed with image guidance from CT, ultrasound, and MRI. During the MRI-guided cryoablation procedure, the interventional radiologist visually compares the iceball size on monitoring images with respect to the original tumor on separate planning images. The comparisons made during the monitoring step are time consuming, inefficient and sometimes lack the precision needed for decision making, requiring the radiologist to make further changes later in the procedure. This study sought to mitigate uncertainty in these visual comparisons by quantifying tissue response to cryoablation and providing visualization of the response during the procedure. Based on retrospective analysis of MR-guided cryoablation patient data, registration and segmentation algorithms were investigated and implemented for periprocedural visualization to deliver iceball position/size with respect to planning images registered within 3.3mm with at least 70% overlap and a quantitative logit model was developed to relate perfusion deficit in renal parenchyma visualized in verification images as a result of iceball size visualized in monitoring images. Through retrospective study of 20 patient cases, the relationship between likelihood of perfusion loss in renal parenchyma and distance within iceball was quantified and iteratively fit to a logit curve. Using the parameters from the logit fit, the margin for 95% perfusion loss likelihood was found to be 4.28 mm within the iceball. The observed margin corresponds well with the clinically accepted margin of 3-5mm within the iceball. In order to display the iceball position and perfusion loss likelihood to the radiologist, algorithms were implemented to create a fast segmentation and registration module which executed in under 2 minutes, within the clinically-relevant 3 minute monitoring period. Using 16 patient cases, the average Hausdorff distance was reduced from 10.1mm to 3.21 mm with average DSC increased from 46.6% to 82.6% before and after registration.

Tools for constraint visualization: The VIFID/TRIFID tool

Visualization of program executions has been used in applications which include education and debugging. However, traditional visualization techniques often fall short of expectations or are altogether inadequate for new programming paradigms, such as Constraint Logic Programming (CLP), whose declarative and operational semantics differ in some crucial ways from those of other paradigms. In particular, traditional ideas regarding the behavior of data often cannot be lifted in a straightforward way to (C)LP from other families of programming languages. In this chapter we discuss techniques for visualizing data evolution in CLP. We briefly review some previously proposed visualization paradigms, and also propose a number of (to our knowledge) novel ones. The graphical representations have been chosen based on the perceived needs of a programmer trying to analyze the behavior and characteristics of an execution. In particular, we concéntrate on the representation of the run-time valúes of the variables, and the constraints among them. Given our interest in visualizing large executions, we also pay attention to abstraction techniques, i.e., techniques which are intended to help in reducing the complexity of the visual information.

Some design issues in the visualization of constraint logic program execution

Visualization of program executions has been found useful in applications which include education and debugging. However, traditional visualization techniques often fall short of expectations or are altogether inadequate for new programming paradigms, such as Constraint Logic Programming (CLP), whose declarative and operational semantics differ in some crucial ways from those of other paradigms. In particular, traditional ideas regarding flow control and the behavior of data often cannot be lifted in a straightforward way to (C)LP from other families of programming languages. In this paper we discuss techniques for visualizing program execution and data evolution in CLP. We briefly review some previously proposed visualization paradigms, and also propose a number of (to our knowledge) novel ones. The graphical representations have been chosen based on the perceived needs of a programmer trying to analyze the behavior and characteristics of an execution. In particular, we concéntrate on the representation of the program execution behavior (control), the runtime valúes of the variables, and the runtime constraints. Given our interest in visualizing large executions, we also pay attention to abstraction techniques, Le., techniques which are intended to help in reducing the complexity of the visual information.

Tutorial 2.0 on Technical Drawing 3D and Visualization

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].

A visual framework to accelerate knowledge discovery based on dimensionality reduction minimizing degradation of quality

Tradicionalmente, el uso de técnicas de análisis de datos ha sido una de las principales vías para el descubrimiento de conocimiento oculto en grandes cantidades de datos, recopilados por expertos en diferentes dominios. Por otra parte, las técnicas de visualización también se han usado para mejorar y facilitar este proceso. Sin embargo, existen limitaciones serias en la obtención de conocimiento, ya que suele ser un proceso lento, tedioso y en muchas ocasiones infructífero, debido a la dificultad de las personas para comprender conjuntos de datos de grandes dimensiones. Otro gran inconveniente, pocas veces tenido en cuenta por los expertos que analizan grandes conjuntos de datos, es la degradación involuntaria a la que someten a los datos durante las tareas de análisis, previas a la obtención final de conclusiones. Por degradación quiere decirse que los datos pueden perder sus propiedades originales, y suele producirse por una reducción inapropiada de los datos, alterando así su naturaleza original y llevando en muchos casos a interpretaciones y conclusiones erróneas que podrían tener serias implicaciones. Además, este hecho adquiere una importancia trascendental cuando los datos pertenecen al dominio médico o biológico, y la vida de diferentes personas depende de esta toma final de decisiones, en algunas ocasiones llevada a cabo de forma inapropiada. Ésta es la motivación de la presente tesis, la cual propone un nuevo framework visual, llamado MedVir, que combina la potencia de técnicas avanzadas de visualización y minería de datos para tratar de dar solución a estos grandes inconvenientes existentes en el proceso de descubrimiento de información válida. El objetivo principal es hacer más fácil, comprensible, intuitivo y rápido el proceso de adquisición de conocimiento al que se enfrentan los expertos cuando trabajan con grandes conjuntos de datos en diferentes dominios. Para ello, en primer lugar, se lleva a cabo una fuerte disminución en el tamaño de los datos con el objetivo de facilitar al experto su manejo, y a la vez preservando intactas, en la medida de lo posible, sus propiedades originales. Después, se hace uso de efectivas técnicas de visualización para representar los datos obtenidos, permitiendo al experto interactuar de forma sencilla e intuitiva con los datos, llevar a cabo diferentes tareas de análisis de datos y así estimular visualmente su capacidad de comprensión. De este modo, el objetivo subyacente se basa en abstraer al experto, en la medida de lo posible, de la complejidad de sus datos originales para presentarle una versión más comprensible, que facilite y acelere la tarea final de descubrimiento de conocimiento. MedVir se ha aplicado satisfactoriamente, entre otros, al campo de la magnetoencefalografía (MEG), que consiste en la predicción en la rehabilitación de lesiones cerebrales traumáticas (Traumatic Brain Injury (TBI) rehabilitation prediction). Los resultados obtenidos demuestran la efectividad del framework a la hora de acelerar y facilitar el proceso de descubrimiento de conocimiento sobre conjuntos de datos reales. ABSTRACT Traditionally, the use of data analysis techniques has been one of the main ways of discovering knowledge hidden in large amounts of data, collected by experts in different domains. Moreover, visualization techniques have also been used to enhance and facilitate this process. However, there are serious limitations in the process of knowledge acquisition, as it is often a slow, tedious and many times fruitless process, due to the difficulty for human beings to understand large datasets. Another major drawback, rarely considered by experts that analyze large datasets, is the involuntary degradation to which they subject the data during analysis tasks, prior to obtaining the final conclusions. Degradation means that data can lose part of their original properties, and it is usually caused by improper data reduction, thereby altering their original nature and often leading to erroneous interpretations and conclusions that could have serious implications. Furthermore, this fact gains a trascendental importance when the data belong to medical or biological domain, and the lives of people depends on the final decision-making, which is sometimes conducted improperly. This is the motivation of this thesis, which proposes a new visual framework, called MedVir, which combines the power of advanced visualization techniques and data mining to try to solve these major problems existing in the process of discovery of valid information. Thus, the main objective is to facilitate and to make more understandable, intuitive and fast the process of knowledge acquisition that experts face when working with large datasets in different domains. To achieve this, first, a strong reduction in the size of the data is carried out in order to make the management of the data easier to the expert, while preserving intact, as far as possible, the original properties of the data. Then, effective visualization techniques are used to represent the obtained data, allowing the expert to interact easily and intuitively with the data, to carry out different data analysis tasks, and so visually stimulating their comprehension capacity. Therefore, the underlying objective is based on abstracting the expert, as far as possible, from the complexity of the original data to present him a more understandable version, thus facilitating and accelerating the task of knowledge discovery. MedVir has been succesfully applied to, among others, the field of magnetoencephalography (MEG), which consists in predicting the rehabilitation of Traumatic Brain Injury (TBI). The results obtained successfully demonstrate the effectiveness of the framework to accelerate and facilitate the process of knowledge discovery on real world datasets.

Tutorial 2.0 on Technical Drawing 3D and Visualization

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).

Nuevos Esquemas de Visualizaciones para Mejorar la Comprensibilidad de Modelos de Data Mining

La gran cantidad de datos que se registran diariamente en los sistemas de base de datos de las organizaciones ha generado la necesidad de analizarla. Sin embargo, se enfrentan a la complejidad de procesar enormes volúmenes de datos a través de métodos tradicionales de análisis. Además, dentro de un contexto globalizado y competitivo las organizaciones se mantienen en la búsqueda constante de mejorar sus procesos, para lo cual requieren herramientas que les permitan tomar mejores decisiones. Esto implica estar mejor informado y conocer su historia digital para describir sus procesos y poder anticipar (predecir) eventos no previstos. Estos nuevos requerimientos de análisis de datos ha motivado el desarrollo creciente de proyectos de minería de datos. El proceso de minería de datos busca obtener desde un conjunto masivo de datos, modelos que permitan describir los datos o predecir nuevas instancias en el conjunto. Implica etapas de: preparación de los datos, procesamiento parcial o totalmente automatizado para identificar modelos en los datos, para luego obtener como salida patrones, relaciones o reglas. Esta salida debe significar un nuevo conocimiento para la organización, útil y comprensible para los usuarios finales, y que pueda ser integrado a los procesos para apoyar la toma de decisiones. Sin embargo, la mayor dificultad es justamente lograr que el analista de datos, que interviene en todo este proceso, pueda identificar modelos lo cual es una tarea compleja y muchas veces requiere de la experiencia, no sólo del analista de datos, sino que también del experto en el dominio del problema. Una forma de apoyar el análisis de datos, modelos y patrones es a través de su representación visual, utilizando las capacidades de percepción visual del ser humano, la cual puede detectar patrones con mayor facilidad. Bajo este enfoque, la visualización ha sido utilizada en minería datos, mayormente en el análisis descriptivo de los datos (entrada) y en la presentación de los patrones (salida), dejando limitado este paradigma para el análisis de modelos. El presente documento describe el desarrollo de la Tesis Doctoral denominada “Nuevos Esquemas de Visualizaciones para Mejorar la Comprensibilidad de Modelos de Data Mining”. Esta investigación busca aportar con un enfoque de visualización para apoyar la comprensión de modelos minería de datos, para esto propone la metáfora de modelos visualmente aumentados. ABSTRACT The large amount of data to be recorded daily in the systems database of organizations has generated the need to analyze it. However, faced with the complexity of processing huge volumes of data over traditional methods of analysis. Moreover, in a globalized and competitive environment organizations are kept constantly looking to improve their processes, which require tools that allow them to make better decisions. This involves being bettered informed and knows your digital story to describe its processes and to anticipate (predict) unanticipated events. These new requirements of data analysis, has led to the increasing development of data-mining projects. The data-mining process seeks to obtain from a massive data set, models to describe the data or predict new instances in the set. It involves steps of data preparation, partially or fully automated processing to identify patterns in the data, and then get output patterns, relationships or rules. This output must mean new knowledge for the organization, useful and understandable for end users, and can be integrated into the process to support decision-making. However, the biggest challenge is just getting the data analyst involved in this process, which can identify models is complex and often requires experience not only of the data analyst, but also the expert in the problem domain. One way to support the analysis of the data, models and patterns, is through its visual representation, i.e., using the capabilities of human visual perception, which can detect patterns easily in any context. Under this approach, the visualization has been used in data mining, mostly in exploratory data analysis (input) and the presentation of the patterns (output), leaving limited this paradigm for analyzing models. This document describes the development of the doctoral thesis entitled "New Visualizations Schemes to Improve Understandability of Data-Mining Models". This research aims to provide a visualization approach to support understanding of data mining models for this proposed metaphor visually enhanced models.

The Arabidopsis Information Resource (TAIR): a comprehensive database and web-based information retrieval, analysis, and visualization system for a model plant

Arabidopsis thaliana, a small annual plant belonging to the mustard family, is the subject of study by an estimated 7000 researchers around the world. In addition to the large body of genetic, physiological and biochemical data gathered for this plant, it will be the first higher plant genome to be completely sequenced, with completion expected at the end of the year 2000. The sequencing effort has been coordinated by an international collaboration, the Arabidopsis Genome Initiative (AGI). The rationale for intensive investigation of Arabidopsis is that it is an excellent model for higher plants. In order to maximize use of the knowledge gained about this plant, there is a need for a comprehensive database and information retrieval and analysis system that will provide user-friendly access to Arabidopsis information. This paper describes the initial steps we have taken toward realizing these goals in a project called The Arabidopsis Information Resource (TAIR) (www.arabidopsis.org).

Automated mapping of environmental variables from a SEIS or SISE perspective

The INTAMAP FP6 project has developed an interoperable framework for real-time automatic mapping of critical environmental variables by extending spatial statistical methods and employing open, web-based, data exchange protocols and visualisation tools. This paper will give an overview of the underlying problem, of the project, and discuss which problems it has solved and which open problems seem to be most relevant to deal with next. The interpolation problem that INTAMAP solves is the generic problem of spatial interpolation of environmental variables without user interaction, based on measurements of e.g. PM10, rainfall or gamma dose rate, at arbitrary locations or over a regular grid covering the area of interest. It deals with problems of varying spatial resolution of measurements, the interpolation of averages over larger areas, and with providing information on the interpolation error to the end-user. In addition, monitoring network optimisation is addressed in a non-automatic context.

KEMNAD:a knowledge engineering methodology for negotiating agent development

Automated negotiation is widely applied in various domains. However, the development of such systems is a complex knowledge and software engineering task. So, a methodology there will be helpful. Unfortunately, none of existing methodologies can offer sufficient, detailed support for such system development. To remove this limitation, this paper develops a new methodology made up of: (1) a generic framework (architectural pattern) for the main task, and (2) a library of modular and reusable design pattern (templates) of subtasks. Thus, it is much easier to build a negotiating agent by assembling these standardised components rather than reinventing the wheel each time. Moreover, since these patterns are identified from a wide variety of existing negotiating agents (especially high impact ones), they can also improve the quality of the final systems developed. In addition, our methodology reveals what types of domain knowledge need to be input into the negotiating agents. This in turn provides a basis for developing techniques to acquire the domain knowledge from human users. This is important because negotiation agents act faithfully on the behalf of their human users and thus the relevant domain knowledge must be acquired from the human users. Finally, our methodology is validated with one high impact system.

Visual data mining using principled projection algorithms and information visualization techniques

We introduce a flexible visual data mining framework which combines advanced projection algorithms from the machine learning domain and visual techniques developed in the information visualization domain. The advantage of such an interface is that the user is directly involved in the data mining process. We integrate principled projection algorithms, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates and billboarding, to provide a visual data mining framework. Results on a real-life chemoinformatics dataset using GTM are promising and have been analytically compared with the results from the traditional projection methods. It is also shown that the HGTM algorithm provides additional value for large datasets. The computational complexity of these algorithms is discussed to demonstrate their suitability for the visual data mining framework. Copyright 2006 ACM.

Knowledge management for more sustainable water systems

The management and sharing of complex data, information and knowledge is a fundamental and growing concern in the Water and other Industries for a variety of reasons. For example, risks and uncertainties associated with climate, and other changes require knowledge to prepare for a range of future scenarios and potential extreme events. Formal ways in which knowledge can be established and managed can help deliver efficiencies on acquisition, structuring and filtering to provide only the essential aspects of the knowledge really needed. Ontologies are a key technology for this knowledge management. The construction of ontologies is a considerable overhead on any knowledge management programme. Hence current computer science research is investigating generating ontologies automatically from documents using text mining and natural language techniques. As an example of this, results from application of the Text2Onto tool to stakeholder documents for a project on sustainable water cycle management in new developments are presented. It is concluded that by adopting ontological representations sooner, rather than later in an analytical process, decision makers will be able to make better use of highly knowledgeable systems containing automated services to ensure that sustainability considerations are included.

Semisupervised learning of hierarchical latent trait models for data visualization

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.