Massive open star clusters using the VVV survey III. A young massive cluster at the far edge of the Galactic bar

Context. Young massive clusters are key to map the Milky Way’s structure, and near-infrared large area sky surveys have contributed strongly to the discovery of new obscured massive stellar clusters. Aims. We present the third article in a series of papers focused on young and massive clusters discovered in the VVV survey. This article is dedicated to the physical characterization of VVV CL086, using part of its OB-stellar population. Methods. We physically characterized the cluster using JHKS near-infrared photometry from ESO public survey VVV images, using the VVV-SkZ pipeline, and near-infrared K-band spectroscopy, following the methodology presented in the first article of the series. Results. Individual distances for two observed stars indicate that the cluster is located at the far edge of the Galactic bar. These stars, which are probable cluster members from the statistically field-star decontaminated CMD, have spectral types between O9 and B0 V. According to our analysis, this young cluster (1.0 Myr < age < 5.0 Myr) is located at a distance of 11+5-6 kpc, and we estimate a lower limit for the cluster total mass of (2.8+1.6-1.4) · 103 M⊙. It is likely that the cluster contains even earlier and more massive stars.

Enrichment of the Phenotypic and Genotypic Data Warehouse analysis using Question Answering systems to facilitate the decision making process in cereal breeding programs

Currently there are an overwhelming number of scientific publications in Life Sciences, especially in Genetics and Biotechnology. This huge amount of information is structured in corporate Data Warehouses (DW) or in Biological Databases (e.g. UniProt, RCSB Protein Data Bank, CEREALAB or GenBank), whose main drawback is its cost of updating that makes it obsolete easily. However, these Databases are the main tool for enterprises when they want to update their internal information, for example when a plant breeder enterprise needs to enrich its genetic information (internal structured Database) with recently discovered genes related to specific phenotypic traits (external unstructured data) in order to choose the desired parentals for breeding programs. In this paper, we propose to complement the internal information with external data from the Web using Question Answering (QA) techniques. We go a step further by providing a complete framework for integrating unstructured and structured information by combining traditional Databases and DW architectures with QA systems. The great advantage of our framework is that decision makers can compare instantaneously internal data with external data from competitors, thereby allowing taking quick strategic decisions based on richer data.