Presentación del Plan de Digitalización para las bibliotecas del CSIC

Sección: La RedA finales del mes de abril ha visto la luz el Plan de Digitalización para la Red de Bibliotecas del CSIC. Este plan tiene como objetivo crear una política unitaria que guíe a las bibliotecas en la aplicación de criterios comunes y estándares de uso para el adecuado desarrollo de los proyectos de digitalización. Se busca con ello sentar las bases para la creación y desarrollo de una colección digital propia del CSIC de proyección internacional y con visos de perdurabilidad. Aunque el plan está concebido pensando principalmente en los fondos patrimoniales y libres de derechos de autor, las pautas técnicas y los criterios de selección ahí expuestos son una buena guía para el desarrollo de cualquier proyecto de digitalización.N

Data-Driven Transformations In Small Area Estimation

Small area models typically depend on the validity of model as- sumptions. For example, a commonly used version of the Empirical Best Predictor relies on the Gaussian assumptions of the error terms of the linear mixed model, a feature rarely observed in applications with real data. The present paper proposes to tackle the potential lack of validity of the model assumptions by using data- driven scaled transformations as opposed to ad-hoc chosen transformations. Dif- ferent types of transformations are explored, the estimation of the transformation parameters is studied in detail under a linear mixed model and transformations are used in small area prediction of lin- ear and non-linear parameters. The use of scaled transformations is crucial as it allows for fitting the linear mixed model with standard software and hence it simplifies the work of the data analyst. Mean squared error estimation that accounts for the uncertainty due to the estimation of the transformation parameters is explored using para- metric and semi-parametric (wild) bootstrap. The proposed methods are illustrated using real survey and census data for estimating in- come deprivation parameters for municipalities in the Mexican state of Guerrero. Extensive simulation studies and the results from the application show that using carefully selected, data driven transfor- mations can improve small area estimation

CD38: A NAADP degrading enzyme

AbstractThe role of the multifunctional enzyme CD38 in formation of the Ca2+-mobilizing second messenger nicotinic acid adenine dinucleotide phosphate (NAADP) was investigated. Gene silencing of CD38 did neither inhibit NAADP synthesis in intact Jurkat T cells nor in thymus or spleen obtained from CD38 knock out mice. In vitro, both NAADP formation by base-exchange and degradation to 2-phospho adenosine diphosphoribose were efficiently decreased. Thus in vivo CD38 appears to be a NAADP degrading rather than a NAADP forming enzyme, perhaps avoiding desensitizing NAADP levels in intact cells

Small area estimation in R with application to Mexican income data

In the last decades policy decisions are often based on statistical measures. The more detailed this information is, the better is the basis for targeting policies and evaluating policy programs. For instance, the United Nations suggest more disaggregation of statistical indicators for monitoring their Sustainable Development Goals and also the number of National Statistical Institutes (NSIs) that notice the need of more disaggregated statistics is increasing. Dimensions for disaggregation can be characteristics of the individuals or households like sex, age or ethnicity, economic activity or spatial dimensions like metropolitan areas or districts. Primary data sources for variables that are used to estimate statistical indicators are national household surveys. However, sample sizes are usually small or even zero at disaggregated levels. Therefore, direct estimators based only on survey data can be unreliable or not available for small domains. While the option of more specific surveys is costly, model-based methodologies for dealing with small sample sizes can help to obtain reliable estimates for small domains. The so-called Small Area Estimation (SAE) methods [1,2] link survey data that is only available for a proportion of households with administrative or census data available for all households in the area of interest. Even though a wide range of SAE methods is proposed by academic researchers, these are, so far, applied only by a small number of NSIs or other practitioners like the World Bank. This gap between theoretical possibilities and practical application can have several reasons. One reason can be the lack of suitable statistical software. The free software environment R helps to counteract this issue since researchers can make their codes available to the public via packages. Thus, new methods can reach the practitioner faster than with non-free software. The next two sections summarize which packages are already available and what could be improved in the future

The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators

The R package emdi enables the estimation of regionally disaggregated indicators using small area estimation methods and includes tools for processing, assessing, and presenting the results. The mean of the target variable, the quantiles of its distribution, the headcount ratio, the poverty gap, the Gini coefficient, the quintile share ratio, and customized indicators are estimated using direct and model-based estimation with the empirical best predictor (Molina and Rao 2010). The user is assisted by automatic estimation of datadriven transformation parameters. Parametric and semi-parametric, wild bootstrap for mean squared error estimation are implemented with the latter offering protection against possible misspecification of the error distribution. Tools for (a) customized parallel computing, (b) model diagnostic analyses, (c) creating high quality maps and (d) exporting the results to Excel and OpenDocument Spreadsheets are included. The functionality of the package is illustrated with example data sets for estimating the Gini coefficient and median income for districts in Austria

The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators

The R package emdi offers a methodological and computational framework for the estimation of regionally disaggregated indicators using small area estimation methods and provides tools for assessing, processing and presenting the results. A range of indicators that includes the mean of the target variable, the quantiles of its distribution and complex, non-linear indicators or customized indicators can be estimated simultaneously using direct estimation and the empirical best predictor (EBP) approach (Molina and Rao 2010). In the application presented in this paper package emdi is used for estimating inequality indicators and the median of the income distributions for small areas in Austria. Because the EBP approach relies on the normality of the mixed model error terms, the user is further assisted by an automatic selection of data-driven transformation parameters. Estimating the uncertainty of small area estimates (using a mean squared error - MSE measure) is achieved by using both parametric bootstrap and semi-parametric wild bootstrap. The additional uncertainty due to the estimation of the transformation parameter is also captured in MSE estimation. The semi-parametric wild bootstrap further protects the user against departures from the assumptions of the mixed model in particular, those of the unit-level error term. The bootstrap schemes are facilitated by computationally effcient code that uses parallel computing. The package supports the users beyond the production of small area estimates. Firstly, tools are provided for exploring the structure of the data and for diagnostic analysis of the model assumptions. Secondly, tools that allow the spatial mapping of the estimates enable the user to create high quality visualizations. Thirdly, results and model summaries can be exported to Excel™ spreadsheets for further reporting purposes

CANEUS2006-11042 HIGH TEMPERATURE (800°C) MEMS PRESSURE SENSOR DEVELOPMENT INCLUDING REUSABLE PACKAGING FOR ROCKET ENGINE APPLICATIONS

ABSTRACT For aircraft and rocket engines there is a strong need to measure the pressure in the propulsion system at high temperature (HT) with a high local resolution. Miniaturized sensor elements commercially available show decisive disadvantages. With piezoelectric-based sensors working clearly above 500°C static pressures can not be measured. Optical sensors are very expensive and require complex electronics. SiC sensor prototypes are operated up to 650°C, but require high technological efforts. The present approach is based on resistors placed on top of a 2 mm diameter sapphire membrane (8 mm chip diameter). The strain gauges are made either of antimony doped tin oxide (SnO2:Sb) or platinum (Pt). This material combination allows for matching the thermal coefficients of expansion (TCE) of the materials involved. The morphology of the SnO 2 :Sb layer can be optimized to reduce surface roughness on the nanometer scale and hence, gas sensitivity. Antimony doping increases conductivity, but decreases the gauge factor. With this nanotechnological knowledge it is possible to adjust the material properties to the needs of our aerospace applications. Tin oxide was shown to be very stable at HT. We also measured a 2.5% change in electrica

A Comprehensive Molecular Characterization of the Pancreatic Neuroendocrine Tumor Cell Lines BON-1 and QGP-1

Experimental models of neuroendocrine tumor disease are scarce, with only a few existing neuroendocrine tumor cell lines of pancreatic origin (panNET). Their molecular characterization has so far focused on the neuroendocrine phenotype and cancer-related mutations, while a transcription-based assessment of their developmental origin and malignant potential is lacking. In this study, we performed immunoblotting and qPCR analysis of neuroendocrine, epithelial, developmental endocrine-related genes as well as next-generation sequencing (NGS) analysis of microRNAs (miRs) on three panNET cell lines, BON-1, QGP-1, and NT-3. All three lines displayed a neuroendocrine and epithelial phenotype; however, while insulinoma-derived NT-3 cells preferentially expressed markers of mature functional pancreatic β-cells (i.e., INS, MAFA), both BON-1 and QGP-1 displayed high expression of genes associated with immature or non-functional β/δ-cells genes (i.e., NEUROG3), or pancreatic endocrine progenitors (i.e., FOXA2). NGS-based identification of miRs in BON-1 and QGP-1 cells revealed the presence of all six members of the miR-17-92 cluster, which have been implicated in b-cell function and differentiation, but also have roles in cancer being both oncogenic or tumor suppressive. Notably, both BON-1 and QGP-1 cells expressed several miRs known to be negatively associated with epithelial-mesenchymal transition, invasion or metastasis. Moreover, both cell lines failed to exhibit migratory activity in vitro. Taken together, NT-3 cells resemble mature functional β-cells, while both BON-1 and QGP-1 are more similar to immature/non-functional pancreatic β/δ-cells or pancreatic endocrine progenitors. Based on the recent identification of three transcriptional subtypes in panNETs, NT-3 cells resemble the "islet/insulinoma tumors" (IT) subtype, while BON-1 and QGP-1 cells were tentatively classified as "metastasis-like/primary" (MLP). Our results provide a comprehensive characterization of three panNET cell lines and demonstrate their relevance as neuroendocrine tumor models