Molecular recognition with nanostructures fabricated by photopolymerization within metallic subwavelength apertures

The first demonstration of fabrication of submicron lateral resolution molecularly imprinted polymer (MIP) patterns by photoinduced local polymerization within metal subwavelength apertures is reported. The size of the photopolymerized MIP features is finely tuned by the dose of 532 nm radiation. Rhodamine 123 (R123) has been selected as a fluorescent model template to prove the recognition capability of the MIP nanostructures, which has been evaluated by fluorescence lifetime imaging microscopy (FLIM) with single photon timing measurements. The binding selectivity provided by the imprinting effect has been confirmed in the presence of compounds structurally related to R123. These results pave the way to the development of nanomaterial architectures with biomimetic artificial recognition properties for environmental, clinical and food testing

Potencial Analítico de los Polímeros de Impronta Molecular (MIPs) como Elementos de Reconocimiento Biomimético

Los polímeros de impronta molecular (MIPs) son materiales sintéticos que presentan propiedades de reconocimiento molecular específico hacia determinados compuestos. Estos materiales con “memoria selectiva” presentan un elevado potencial analítico como sustitutos de elementos de reconocimiento de origen biológico para el desarrollo de sensores, como sorbentes en procesos de extracción en fase sólida (SPE) y como fases estacionarias para HPLC y CE. La síntesis de estos materiales se basa en la formación de una estructura polimérica, altamente entrecruzada, alrededor de una molécula que actúa como plantilla que se extrae después de la polimerización. De esta forma, el MIP contendrá sitios de unión que son complementarios a la molécula plantilla en forma, tamaño y distribución de grupos funcionales que permiten su reconocimiento posterior, de forma selectiva Los MIPs suelen presentar ventajas interesantes en comparación con los receptore

Predicting Global Irradiance Combining Forecasting Models Through Machine Learning

This paper has been presented at : 13th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2018)Predicting solar irradiance is an active research problem, with many physical models having being designed to accurately predict Global Horizontal Irradiance. However, some of the models are better at short time horizons, while others are more accurate for medium and long horizons. The aim of this research is to automatically combine the predictions of four different models (Smart Persistence, Satellite, Cloud Index Advection and Diffusion, and Solar Weather Research and Forecasting) by means of a state-of-the-art machine learning method (Extreme Gradient Boosting). With this purpose, the four models are used as inputs to the machine learning model, so that the output is an improved Global Irradiance forecast. A 2-year dataset of predictions and measures at one radiometric station in Seville has been gathered to validate the method proposed. Three approaches are studied: a general model, a model for each horizon, and models for groups of horizons. Experimental results show that the machine learning combination of predictors is, on average, more accurate than the predictors themselves.The authors are supported by the Spanish Ministry of Economy and Competitiveness, projects ENE2014-56126-C2-1-R and ENE2014-56126-C2-2-R and FEDER funds. Some of the authors are also funded by the Junta de Andalucía (research group TEP-220)

Determination of zearalenone and its metabolites in endometrial cancer by coupled separation techniques

This study presents a selective method of isolation of zearalenone (ZON) and its metabolite, α-zearalenol (α-ZOL), in neoplastically changed human tissue by accelerated solvent and ultrasonic extractions using a mixture of acetonitrile/water (84/16% v/v) as the extraction solvent. Extraction effectiveness was determined through the selection of parameters (composition of the solvent mixture, temperature, pressure, number of cycles) with tissue contamination at the level of nanograms per gram. The produced acetonitrile/water extracts were purified, and analytes were enriched in columns packed with homemade molecularly imprinted polymers. Purified extracts were determined by liquid chromatography (LC) coupled with different detection systems (diode array detection - DAD and mass spectrometry - MS) involving the Ascentis RP-Amide as a stationary phase and gradient elution. The combination of UE-MISPE-LC (ultrasonic extraction - molecularly imprinted solid-phase extraction - liquid chromatography) produced high (R ≈ 95–98%) and repeatable (RSD < 3%) recovery values for ZON and α-ZOL

Genetic Analysis of Hematological Parameters in Incipient Lines of the Collaborative Cross

Hematological parameters, including red and white blood cell counts and hemoglobin concentration, are widely used clinical indicators of health and disease. These traits are tightly regulated in healthy individuals and are under genetic control. Mutations in key genes that affect hematological parameters have important phenotypic consequences, including multiple variants that affect susceptibility to malarial disease. However, most variation in hematological traits is continuous and is presumably influenced by multiple loci and variants with small phenotypic effects. We used a newly developed mouse resource population, the Collaborative Cross (CC), to identify genetic determinants of hematological parameters. We surveyed the eight founder strains of the CC and performed a mapping study using 131 incipient lines of the CC. Genome scans identified quantitative trait loci for several hematological parameters, including mean red cell volume (Chr 7 and Chr 14), white blood cell count (Chr 18), percent neutrophils/lymphocytes (Chr 11), and monocyte number (Chr 1). We used evolutionary principles and unique bioinformatics resources to reduce the size of candidate intervals and to view functional variation in the context of phylogeny. Many quantitative trait loci regions could be narrowed sufficiently to identify a small number of promising candidate genes. This approach not only expands our knowledge about hematological traits but also demonstrates the unique ability of the CC to elucidate the genetic architecture of complex traits