Serviços de referência virtual

Analisar os serviços de referência virtual, seus padrões e novas tecnologias que têm modificado a prática tradicional realizada no balcão de referência das bibliotecas. São descritas as principais iniciativas norte-americanas e as características de seu funcionamento.

El entorno virtual de la Universitat d'Andorra: la visión del profesorado

En los últimos años las Universidades tradicionalmente presenciales han apostado por la incorporación de la tecnología en los procesos académicos, muestra de ello es la proliferación de campus virtuales que acogen entornos virtuales de aprendizaje. La Universitat d"Andorra utiliza un entorno virtual de aprendizaje desde hace 8 años. El presente trabajo analiza los usos del entorno por parte del profesorado con el objetivo de proponer mejoras que reviertan en la mejora del aprendizaje por parte de los estudiantes. Se ha seguido una metodología descriptiva que combina técnicas cuantitativas y técnicas cualitativas, se ha distribuido un cuestionario dirigido al profesorado y se han realizado dos grupos de discusión dirigidos a profesores y estudiantes respectivamente. Los resultados de este estudio han permitido identificar las necesidades de formación del profesorado de la Universitat d"Andorra y realizar un plan de acciones para la mejora del uso del entorno virtual que en el momento de hacer el estudio prácticamente se reducía a una herramienta de compartición de documentación.

El espacio virtual de intercambio de información sobre recursos humanos en Ciencia y Tecnología de América Latina y el Caribe Del CV Lattes al CvLAC

Se describen los principales problemas de los diferentes sistemas de información para la gestión de la ciencia y tecnología de los países de América Latina y el Caribe y se identifican los retos y las alternativas para promover un mayor intercambio entre países del continente. La creación de un espacio de intercambio de información de los recursos humanos que participan en los sistemas de ciencia y tecnología, a través de una metodología común denominada el CvLAC, se constituye en una respuesta a los problemas identificados. El CvLAC parte de la experiencia brasileña del CvLattes. Se describen los objetivos y resultados esperados del Proyecto del CvLAC, ejecutado con la participación de Brasil, Colombia, Cuba, Chile, México y Venezuela. Se describen los desarrollos y alternativas a partir de los datos existentes en los países participantes en el proyecto, iniciando con Colombia y Chile, destacándose las infinitas posibilidades que las nuevas tecnologías de información digital y los nuevos desarrollos, logrados en este campo, hacen posible en términos de intercambio de datos.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Aspectos filosóficos do virtual e as obras simbólicas no ciberespaço

Discute os aspectos filosóficos do virtual e relaciona-os com o ciberespaço. Assim, o virtual é capaz de explicar um novo modelo de realização das formas simbólicas, ou seja, aquela tomada no pólo do virtual onde a conjunção e... e... constitui-se em aliança desenhando o conhecimento sob a forma de rede e explicando a desmaterialização das obras, contra a realidade tomada no pólo da "reificação", cujo paradigma é o da materialidade. A virtualização no ciberespaço potencializa a virtualidade da linguagem produzindo formas simbólicas que são, em essência, metamórficas. O virtual opera, ainda, a desterritorialização dos signos, portanto a desmaterialização das obras, produzindo uma complexidade na representação humana, em vez de uma substituição completa das obras.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

Plenária Virtual Permanente: em busca da interlocução entre os conselhos de saúde

A Plenária Virtual Permanente é um sistema de áudio e vídeo para redes digitais desenvolvido para conselhos de saúde do Brasil. O artigo aborda as discussões que subsidiaram a criação do dispositivo, a descrição do mesmo e os desafios para a inclusão digital no âmbito dessas instâncias de participação.

Virtual screening and computational optimization for the discovery of covalent prolyl oligopeptidase inhibitors with activity in human cells.

Our docking program, Fitted, implemented in our computational platform, Forecaster, has been modified to carry out automated virtual screening of covalent inhibitors. With this modified version of the program, virtual screening and further docking-based optimization of a selected hit led to the identification of potential covalent reversible inhibitors of prolyl oligopeptidase activity. After visual inspection, a virtual hit molecule together with four analogues were selected for synthesis and made in one-five chemical steps. Biological evaluations on recombinant POP and FAPα enzymes, cell extracts, and living cells demonstrated high potency and selectivity for POP over FAPα and DPPIV. Three compounds even exhibited high nanomolar inhibitory activities in intact living human cells and acceptable metabolic stability. This small set of molecules also demonstrated that covalent binding and/or geometrical constraints to the ligand/protein complex may lead to an increase in bioactivity.

Matrizes da linguagem e a organização virtual do conhecimento

Os signos e as linguagens foram investigados para a organização virtual do conhecimento por meio dos mecanismos de busca. Fundamentou-se na teoria das matrizes da linguagem-pensamento, postuladas por Santaella (2005), na qual se perscrutou as linguagens sonora, visual e verbal. O objetivo da investigação foi estabelecer uma categorização dos mecanismos de busca a partir da correspondência dessas matrizes da linguagem com a indexação virtual e o modus análogo de busca. Os resultados da investigação indicam ser adequada a categorização dos mecanismos de busca sob o critério dos paradigmas semiótico da linguagem em três matrizes, dado o seu modo de ser e sua operacionalidade, sendo eles baseados em conteúdos sonoros, visuais e verbais. Sinteticamente, a sintaxe é a representação do sonoro, a forma é a representação do visível e o discurso é a representação do conhecimento verbal.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

Why microalgal biofuels won't save the internal combustion machine

Proponents of microalgae biofuel technologies often claim that the world demand of liquid fuels, about 5 trillion liters per year, could be supplied by microalgae cultivated on only a few tens of millions of hectares. This perspective reviews this subject and points out that such projections are greatly exaggerated, because (1) the pro- ductivities achieved in large-scale commercial microalgae production systems, operated year-round, do not surpass those of irrigated tropical crops; (2) cultivating, harvesting and processing microalgae solely for the production of biofuels is simply too expensive using current or prospective technology; and (3) currently available (limited) data suggest that the energy balance of algal biofuels is very poor. Thus, microalgal biofuels are no panacea for depleting oil or global warming, and are unlikely to save the internal combustion machine.

IMRT credentialing for prospective trials using institutional virtual phantoms: results of a joint European Organization for the Research and Treatment of Cancer and Radiological Physics Center project.

BACKGROUND AND PURPOSE: Intensity-modulated radiotherapy (IMRT) credentialing for a EORTC study was performed using an anthropomorphic head phantom from the Radiological Physics Center (RPC; RPCPH). Institutions were retrospectively requested to irradiate their institutional phantom (INSTPH) using the same treatment plan in the framework of a Virtual Phantom Project (VPP) for IMRT credentialing. MATERIALS AND METHODS: CT data set of the institutional phantom and measured 2D dose matrices were requested from centers and sent to a dedicated secure EORTC uploader. Data from the RPCPH and INSTPH were thereafter centrally analyzed and inter-compared by the QA team using commercially available software (RIT; ver.5.2; Colorado Springs, USA). RESULTS: Eighteen institutions participated to the VPP. The measurements of 6 (33%) institutions could not be analyzed centrally. All other centers passed both the VPP and the RPC ±7%/4 mm credentialing criteria. At the 5%/5 mm gamma criteria (90% of pixels passing), 11(92%) as compared to 12 (100%) centers pass the credentialing process with RPCPH and INSTPH (p = 0.29), respectively. The corresponding pass rate for the 3%/3 mm gamma criteria (90% of pixels passing) was 2 (17%) and 9 (75%; p = 0.01), respectively. CONCLUSIONS: IMRT dosimetry gamma evaluations in a single plane for a H&N prospective trial using the INSTPH measurements showed agreement at the gamma index criteria of ±5%/5 mm (90% of pixels passing) for a small number of VPP measurements. Using more stringent, criteria, the RPCPH and INSTPH comparison showed disagreement. More data is warranted and urgently required within the framework of prospective studies.