VIPERdb: a relational database for structural virology

VIPERdb () is a database for icosahedral virus capsid structures. Our aim is to provide a comprehensive resource specific to the needs of the structural virology community, with an emphasis on the description and comparison of derived data from structural and energetic analyses of capsids. A relational database implementation based on a schema for macromolecular structure makes the data highly accessible to the user, allowing detailed queries at the atomic level. Together with curation practices that maintain data uniformity, this will facilitate structural bioinformatics studies of virus capsids. User friendly search, visualization and educational tools on the website allow both structural and derived data to be examined easily and extensively. Links to relevant literature, sequence and taxonomy databases are provided for each entry

Opal web services for biomedical applications

Biomedical applications have become increasingly complex, and they often require large-scale high-performance computing resources with a large number of processors and memory. The complexity of application deployment and the advances in cluster, grid and cloud computing require new modes of support for biomedical research. Scientific Software as a Service (sSaaS) enables scalable and transparent access to biomedical applications through simple standards-based Web interfaces. Towards this end, we built a production web server (http://ws.nbcr.net) in August 2007 to support the bioinformatics application called MEME. The server has grown since to include docking analysis with AutoDock and AutoDock Vina, electrostatic calculations using PDB2PQR and APBS, and off-target analysis using SMAP. All the applications on the servers are powered by Opal, a toolkit that allows users to wrap scientific applications easily as web services without any modification to the scientific codes, by writing simple XML configuration files. Opal allows both web forms-based access and programmatic access of all our applications. The Opal toolkit currently supports SOAP-based Web service access to a number of popular applications from the National Biomedical Computation Resource (NBCR) and affiliated collaborative and service projects. In addition, Opal’s programmatic access capability allows our applications to be accessed through many workflow tools, including Vision, Kepler, Nimrod/K and VisTrails. From mid-August 2007 to the end of 2009, we have successfully executed 239 814 jobs. The number of successfully executed jobs more than doubled from 205 to 411 per day between 2008 and 2009. The Opal-enabled service model is useful for a wide range of applications. It provides for interoperation with other applications with Web Service interfaces, and allows application developers to focus on the scientific tool and workflow development. Web server availability: http://ws.nbcr.net

Visual Viper: a portable visualization library for streamlined scientific communications.

À medida que o sector da saúde passa por uma transformação digital, a afluência de dados de saúde para profissionais de saúde e investigadores tem disparado. A crescente necessidade de criar visualizações de dados para compreender esta informação levou ao desenvolvimento do Visual Viper, uma biblioteca Python destinada a automatizar a visualização de dados, para agilizar o processo frequentemente trabalhoso de gerar visualizações. O Visual Viper usa Vega-Lite, uma gramática de alto nível de gráficos interativos, para criar visualizações a partir de várias fontes de dados de investigação através de uma interface de programação de aplicações ('application programming interface' - API) conveniente. Esta automação poupa tempo e facilita a consistência da comunicação científica. A funcionalidade da biblioteca compreende componentes interligados: começa por obter dados de uma fonte selecionada, segue-se a transformação destes dados para se adequarem aos requisitos de visualização. subsequentemente o Visual Viper renderiza os gráficos usando Vega-Lite e exporta-os para uso na comunicação científica. Este pipeline é implementado utilizando uma arquitetura de 'plugins' modular e extensível, que permite acomodar diferentes fontes de dados e tipos de visualização. Cada etapa permite pode ser modificada de forma independente, possibilitando uma personalização extensa com base em casos de uso específicos e sem afetar a funcionalidade geral da biblioteca. Alguns paradigmas importantes usados no desenvolvimento do Visual Viper incluem a programação orientada a objetos ('object oriented programming' - OOP) e desenvolvimento orientado a testes ('test-driven development' - TDD), ambos proporcionando estrutura, eficiência e funcionalidade. Ao usar OOP, a biblioteca adota uma estrutura clara para o código, tornando-o mais fácil de gerir e manter. Os princípios de encapsulamento, herança e polimorfismo proporcionam eficiência e flexibilidade, enquanto o uso de classes como 'DatasetBuilder', 'ChartNotationBuilder' e 'ChartDeployer' facilitam a reutilização de código. A classe 'DatasetBuilder' é projetada para obter e pré-processar dados de várias fontes, a classe 'ChartNotationBuilder' é responsável por criar o layout do gráfico e estética visual baseada nos dados pré-processados, e a classe 'ChartDeployer' lida com a implantação das visualizações finalizadas. Cada uma dessas classes encapsula funções e dados relacionados, reduzindo a complexidade e tornando o código mais fácil de manter e estender. TDD também apresentou um papel crucial no desenvolvimento do Visual Viper. Esta abordagem, que envolve escrever testes antes do código propriamente dito, garante que todas as funções estão a funcionar como pretendido, levando assim a uma melhoria da qualidade do código, simplificação da depuração e um ciclo de desenvolvimento mais rápido. A implementação do Visual Viper garante que este pode funcionar em vários ambientes sem alterações significativas (é agnóstico ao ambiente), e pode operar independentemente em máquinas locais, AWS Lambda, ou como uma API Web. Em conclusão, o Visual Viper fornece uma ferramenta robusta e flexível para a visualização de dados, reforçando a eficiência da comunicação científica no sector da saúde.As the healthcare sector undergoes digital transformation, the influx of data for health professionals and researchers has surged. The increased need for data visualizations to comprehend this information led to the development of Visual Viper, a Python library aimed at automating data visualization, to streamline the often labor-intensive process of generating visualizations. Visual Viper uses Vega-Lite, a high-level grammar of interactive graphics, to create visualizations from various research data sources via a convenient application programming interface (API). This automation saves time and facilitates the consistency of science communication. The library's functionality comprises interconnected components. It begins by retrieving data from a selected source, followed by transforming this data to suit visualization requirements. Subsequently, Visual Viper renders the charts using Vega-Lite and deploys them for use in scientific communication. Implemented within a modular and extensible plugin architecture, it accommodates different data sources and visualization types. Each stage allows independent modification, enabling extensive customization based on specific use-cases without affecting the library's overall functionality. Some important paradigms used in Visual Viper's development include the application of object-oriented programming (OOP) and test-driven development (TDD). By using OOP, the library adopts a structured codebase that is easier to manage and maintain. The principles of encapsulation, inheritance, and polymorphism ensure efficiency and flexibility, while the use of classes facilitates code reuse. The 'DatasetBuilder' class is designed to fetch and preprocess data from various sources, 'ChartNotationBuilder' class is responsible for creating the chart layout and visual aesthetics based on the preprocessed data, and the 'ChartDeployer' class handles the deployment of the finished visualizations. These classes encapsulate related functions and data, reducing complexity and aiding code maintenance and extension. The TDD approach, which involves writing tests before the actual code, ensures all functions are operating as intended, thus leading to improved code quality, simplified debugging, and a faster development cycle. Its implementation ensures the library can run in various environments without significant changes (Environment Agnostic), and it can operate independently on local machines, Lambda, or as a Web API (Serverless Deployment). Future steps for Visual Viper include development of plugins including Google Sheets Dataset Builder and Figma Chart Deployer and creation of additional Vega Lite Chart Notation Builders such as Bar Chart, Survival Chart, and Forest Plot. Once these steps are complete, Visual Viper will be packaged as an importable Python package, with an efficiency evaluation to follow. In conclusion, Visual Viper provides a robust and flexible tool for data visualization, bolstering the efficiency of scientific communication in the healthcare sector

Un système interactif pour le prototypage virtuel coopératif

We present in this thesis the study and implementation of an interactive system for cooperative prototyping of virtual models. These works make use of several technologies from different scientific backgrounds; Virtual Reality is at the crossroads of many disciplines. Our goal is not to replace right now a CAD system with a system such as that we propose in this thesis. Indeed, the power of the machines does not allow yet the management of virtual objects with an accuracy comparable to that of CAD tools. While our system is intuitive and interactive but does not have enough machine power to compete with such precision tools; This precision is however necessary for the industry. This development will be achieved, for sure, but it is more reasonable for the moment to see virtual reality as a complement to CAD.Nous présentons dans ce mémoire l’étude et la réalisation d’un système interactif pour le prototypage coopératif de maquettes virtuelles. Ces travaux font usage de plusieurs technologies issues de milieux scientifiques variés ; la réalité virtuelle n’est elle pas à la croisée des chemins de nombreuses disciplines ? Notre objectif n’est pas de remplacer dès à présent un système de CAO par un système tel que celui que nous proposons dans ce mémoire. En effet, la puissance des machines ne permet pas encore la gestion d’objets virtuels avec une précision comparable à celle des outils de CAO. Certes notre système est intuitif et interactif mais il ne dispose pas d’assez de puissance machine pour rivaliser en précision avec de tels outils ; cette précision est pourtant nécessaire pour l’industrie. Cette évolution se fera, c’est sûr, mais il est pour l’instant plus raisonnable de voir la réalité virtuelle comme un complément de la CAO

Computational Steering in the Problem Solving Environment WBCSim

Computational steering allows scientists to interactively control a numerical experiment and adjust parameters of the computation on-the-fly and explore “what if ” analysis. Computational steering effectively reduces computational time, makes research more efficient, and opens up new product design opportunities. There are several problem solving environments (PSEs) featuring computational steering. However, there is hardly any work explaining how to enable computational steering for PSEs embedded with legacy simulation codes. This paper describes a practical approach to implement computational steering for such PSEs by using WBCSim as an example. WBCSim is a Web based simulation system designed to increase the productivity of wood scientists conducting research on wood-based composites manufacturing processes. WBCSim serves as a prototypical example for the design, construction, and evaluation of small-scale PSEs. Various changes have been made to support computational steering across the three layers—client, server, developer—comprising the WBCSim system. A detailed description of the WBCSim system architecture is presented, along with a typical scenario of computational steering usage

The Python interpreter as a framework for integrating scientific computing software-components

The focus of the Molecular Simulation Laboratory is to model molecularinteractions. In particular, we are working on automated docking and molecular visualization. Building and simulating complex molecular systems requires the tight interoperation of a variety of software tools originating from various scientific disciplines and usually developed independently of each other. Over the last ten years we have evolved a strategy for addressing the formidable software engineering problem ofintegrating such heterogeneous software tools. The basic idea is that the Python interpreter serves as the integration framework and provides a powerful and flexible glue for rapidly prototyping applications from reusable software components (i.e. Python packages). We no longer think in terms of programs, but rather in terms of packages which can be loaded dynamically into the interpreter when needed, and instantly extend our framework (i.e. the Python interpreter) with new functionality. We have written more than 30 packages (>2500 classes) providing support for applications ranging from scientific visualization and visual programming to molecular simulations and virtual reality. Moreover, some of our components have been reused successfully by otherlaboratories for their own research. Applications created from our software components have been distributed to over 15000 users around the world. In this paper we describe our approach and its various applications, discuss the reasons that make this approach so successful, and present lessons learns and pitfalls to avoid in order to maximize the reusability and interoperability of software components

NASA Tech Briefs, September 2011

Topics covered include: Fused Reality for Enhanced Flight Test Capabilities; Thermography to Inspect Insulation of Large Cryogenic Tanks; Crush Test Abuse Stand; Test Generator for MATLAB Simulations; Dynamic Monitoring of Cleanroom Fallout Using an Air Particle Counter; Enhancement to Non-Contacting Stress Measurement of Blade Vibration Frequency; Positively Verifying Mating of Previously Unverifiable Flight Connectors; Radiation-Tolerant Intelligent Memory Stack - RTIMS; Ultra-Low-Dropout Linear Regulator; Excitation of a Parallel Plate Waveguide by an Array of Rectangular Waveguides; FPGA for Power Control of MSL Avionics; UAVSAR Active Electronically Scanned Array; Lockout/Tagout (LOTO) Simulator; Silicon Carbide Mounts for Fabry-Perot Interferometers; Measuring the In-Process Figure, Final Prescription, and System Alignment of Large; Optics and Segmented Mirrors Using Lidar Metrology; Fiber-Reinforced Reactive Nano-Epoxy Composites; Polymerization Initiated at the Sidewalls of Carbon Nanotubes; Metal-Matrix/Hollow-Ceramic-Sphere Composites; Piezoelectrically Enhanced Photocathodes; Iridium-Doped Ruthenium Oxide Catalyst for Oxygen Evolution; Improved Mo-Re VPS Alloys for High-Temperature Uses; Data Service Provider Cost Estimation Tool; Hybrid Power Management-Based Vehicle Architecture; Force Limit System; Levitated Duct Fan (LDF) Aircraft Auxiliary Generator; Compact, Two-Sided Structural Cold Plate Configuration; AN Fitting Reconditioning Tool; Active Response Gravity Offload System; Method and Apparatus for Forming Nanodroplets; Rapid Detection of the Varicella Zoster Virus in Saliva; Improved Devices for Collecting Sweat for Chemical Analysis; Phase-Controlled Magnetic Mirror for Wavefront Correction; and Frame-Transfer Gating Raman Spectroscopy for Time-Resolved Multiscalar Combustion Diagnostics

A Systematic Survey of ML Datasets for Prime CV Research Areas-Media and Metadata

The ever-growing capabilities of computers have enabled pursuing Computer Vision through Machine Learning (i.e., MLCV). ML tools require large amounts of information to learn from (ML datasets). These are costly to produce but have received reduced attention regarding standardization. This prevents the cooperative production and exploitation of these resources, impedes countless synergies, and hinders ML research. No global view exists of the MLCV dataset tissue. Acquiring it is fundamental to enable standardization. We provide an extensive survey of the evolution and current state of MLCV datasets (1994 to 2019) for a set of specific CV areas as well as a quantitative and qualitative analysis of the results. Data were gathered from online scientific databases (e.g., Google Scholar, CiteSeerX). We reveal the heterogeneous plethora that comprises the MLCV dataset tissue; their continuous growth in volume and complexity; the specificities of the evolution of their media and metadata components regarding a range of aspects; and that MLCV progress requires the construction of a global standardized (structuring, manipulating, and sharing) MLCV "library". Accordingly, we formulate a novel interpretation of this dataset collective as a global tissue of synthetic cognitive visual memories and define the immediately necessary steps to advance its standardization and integration