Evaluation of Surgical Treatment of Congenital Heart Disease in Patients Aged Above 16 Years

Background: The increasing number of children with evolving congenital heart diseases demands greater preparation of professionals and institutions that handle them. Objective: To describe the profile of patients aged over 16 years with congenital heart disease, who have undergone surgery, and analyze the risk factors that predict hospital mortality. Methods: One thousand five hundred twenty patients (mean age 27 +/- 13 years) were operated between January 1986 and December 2010. We performed a descriptive analysis of the epidemiological profile of the study population and analyzed risk factors for hospital mortality, considering the complexity score, the year in which surgery was performed, the procedure performed or not performed by the pediatric surgeon and reoperation. Results: There was a significant increase in the number of cases from the year 2000. The average complexity score was 5.4 and the septal defects represented 45% of cases. Overall mortality was 7.7% and most procedures (973 or 61.9%) with greater complexity were performed by pediatric surgeons. Complexity (OR 1.5), reoperation (OR 2.17) and pediatric surgeon (OR 0.28) were independent risk factors influencing mortality. Multivariate analysis showed that the year in which the surgery was performed (OR 1.03), the complexity (OR 1.44) and the pediatric surgeon (OR 0.28) influenced the result. Conclusion: There is an increasing number of patients aged 16 years which, despite the large number of simple cases, the most complex ones were referred to pediatric surgeons, who had lower mortality, especially in recent years. (Arq Bras Cardiol 2012;98(5):390-397)

An Overview of the Use of Metadata in Agriculture

Metadata is data that fully describes the data and the areas they represent, allowing the user to decide on their use as best as possible. Allow reporting on the existence of a set of data linked to specific needs. The use of metadata has the purpose of documenting and organizing a structured organizational data in order to minimize duplication of efforts to locate them and to facilitate maintenance. It also provides the administration of large amounts of data, discovery, retrieval and editing features. The global use of metadata is regulated by a technical group or task force composed of several segments such as industries, universities and research firms. Agriculture in particular is a good example for the development of typical applications using metadata is the integration of systems and equipment, allowing the implementation of techniques used in precision agriculture, the integration of different computer systems via webservices or other type of solution requires the integration of structured data. The purpose of this paper is to present an overview of the standards of metadata areas consolidated as agricultural.

A UML profile for the OBO relation ontology

Background: Ontologies have increasingly been used in the biomedical domain, which has prompted the emergence of different initiatives to facilitate their development and integration. The Open Biological and Biomedical Ontologies (OBO) Foundry consortium provides a repository of life-science ontologies, which are developed according to a set of shared principles. This consortium has developed an ontology called OBO Relation Ontology aiming at standardizing the different types of biological entity classes and associated relationships. Since ontologies are primarily intended to be used by humans, the use of graphical notations for ontology development facilitates the capture, comprehension and communication of knowledge between its users. However, OBO Foundry ontologies are captured and represented basically using text-based notations. The Unified Modeling Language (UML) provides a standard and widely-used graphical notation for modeling computer systems. UML provides a well-defined set of modeling elements, which can be extended using a built-in extension mechanism named Profile. Thus, this work aims at developing a UML profile for the OBO Relation Ontology to provide a domain-specific set of modeling elements that can be used to create standard UML-based ontologies in the biomedical domain. Results: We have studied the OBO Relation Ontology, the UML metamodel and the UML profiling mechanism. Based on these studies, we have proposed an extension to the UML metamodel in conformance with the OBO Relation Ontology and we have defined a profile that implements the extended metamodel. Finally, we have applied the proposed UML profile in the development of a number of fragments from different ontologies. Particularly, we have considered the Gene Ontology (GO), the PRotein Ontology (PRO) and the Xenopus Anatomy and Development Ontology (XAO). Conclusions: The use of an established and well-known graphical language in the development of biomedical ontologies provides a more intuitive form of capturing and representing knowledge than using only text-based notations. The use of the profile requires the domain expert to reason about the underlying semantics of the concepts and relationships being modeled, which helps preventing the introduction of inconsistencies in an ontology under development and facilitates the identification and correction of errors in an already defined ontology.

Semantic integration of gene expression analysis tools and data sources using software connectors

Abstract Background The study and analysis of gene expression measurements is the primary focus of functional genomics. Once expression data is available, biologists are faced with the task of extracting (new) knowledge associated to the underlying biological phenomenon. Most often, in order to perform this task, biologists execute a number of analysis activities on the available gene expression dataset rather than a single analysis activity. The integration of heteregeneous tools and data sources to create an integrated analysis environment represents a challenging and error-prone task. Semantic integration enables the assignment of unambiguous meanings to data shared among different applications in an integrated environment, allowing the exchange of data in a semantically consistent and meaningful way. This work aims at developing an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources. The proposed methodology relies on software connectors to support not only the access to heterogeneous data sources but also the definition of transformation rules on exchanged data. Results We have studied the different challenges involved in the integration of computer systems and the role software connectors play in this task. We have also studied a number of gene expression technologies, analysis tools and related ontologies in order to devise basic integration scenarios and propose a reference ontology for the gene expression domain. Then, we have defined a number of activities and associated guidelines to prescribe how the development of connectors should be carried out. Finally, we have applied the proposed methodology in the construction of three different integration scenarios involving the use of different tools for the analysis of different types of gene expression data. Conclusions The proposed methodology facilitates the development of connectors capable of semantically integrating different gene expression analysis tools and data sources. The methodology can be used in the development of connectors supporting both simple and nontrivial processing requirements, thus assuring accurate data exchange and information interpretation from exchanged data.

Ontology for Data Representation in the Production of Cotton Fiber in Brazil

An important feature in computer systems developed for the agricultural sector is to satisfy the heterogeneity of data generated in different processes. Most problems related with this heterogeneity arise from the lack of standard for different computing solutions proposed. An efficient solution for that is to create a single standard for data exchange. The study on the actual process involved in cotton production was based on a research developed by the Brazilian Agricultural Research Corporation (EMBRAPA) that reports all phases as a result of the compilation of several theoretical and practical researches related to cotton crop. The proposition of a standard starts with the identification of the most important classes of data involved in the process, and includes an ontology that is the systematization of concepts related to the production of cotton fiber and results in a set of classes, relations, functions and instances. The results are used as a reference for the development of computational tools, transforming implicit knowledge into applications that support the knowledge described. This research is based on data from the Midwest of Brazil. The choice of the cotton process as a study case comes from the fact that Brazil is one of the major players and there are several improvements required for system integration in this segment.