An ontology to clarify homology-related concepts.

Although homology is a fundamental concept in biology and is one of the shared channels of communication universal to all biology, it is difficult to find a consensus definition. Indeed, the interpretations of homology have changed as biology has progressed. New terms, such as paramorphism, have been introduced into the literature with mixed success. In addition, different research fields operate with different definitions of homology, for example the mechanistic usage of evo-devo is not strictly historical and would not be acceptable in cladistics. This makes a global understanding of homology complex, whereas the integration of evolutionary concepts into bioinformatics and genomics is increasingly important. We propose an ontology organizing homology and related concepts and hope this solution will also facilitate the integration and sharing of knowledge among the community.

@neurIST infrastructure for advanced disease management through integration of heterogeneous data, computing, and complex processing services

The increasing volume of data describing humandisease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the@neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system’s architecture is generic enough that it could be adapted to the treatment of other diseases.Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers cliniciansthe tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medicalresearchers gain access to a critical mass of aneurysm related data due to the system’s ability to federate distributed informationsources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access andwork on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand forperforming computationally intensive simulations for treatment planning and research.

From a pattern-language to a pattern ontology approach for CSCL script design

Collaborative activities, in which students actively interact with each other, have proved to provide significant learning benefits. In Computer-Supported Collaborative Learning (CSCL), these collaborative activities are assisted by technologies. However, the use of computers does not guarantee collaboration, as free collaboration does not necessary lead to fruitful learning. Therefore, practitioners need to design CSCL scripts that structure the collaborative settings so that they promote learning. However, not all teachers have the technical and pedagogical background needed to design such scripts. With the aim of assisting teachers in designing effective CSCL scripts, we propose a model to support the selection of reusable good practices (formulated as patterns) so that they can be used as a starting point for their own designs. This model is based on a pattern ontology that computationally represents the knowledge captured on a pattern language for the design of CSCL scripts. A preliminary evaluation of the proposed approach is provided with two examples based on a set of meaningful interrelated patters computationally represented with the pattern ontology, and a paper prototyping experience carried out with two teaches. The results offer interesting insights towards the implementation of the pattern ontology in software tools.

Nursing diagnoses and outcomes related to the circulatory-system terms (ICNP®) represented in an ontology

The aim of the present study was to develop titles of Nursing Diagnoses and Outcomes (ND/NO) through the relationship between the terms of the Focus axis, limited to the Circulatory System Process, and the terms of other ICNP® axes and to integrate these terms into an ontology. Titles were developed linking 17 terms of the focus axis, which were evaluated by expert nurses in five Brazilian cities. Titles whose use concordance was above 0.80 were included in the ontology. In total, 89 titles for ND/NO were supported in the literature, and 19 were not supported; 37 were assessed as eligible for use in healthcare practice and were included in the ontology. The construction of ND/NO titles based on the ICNP® and using a formal representation of knowledge is a task that requires deepening concepts used for nursing and adequate classification revisions. The elaborated titles will facilitate the composition of diagnostics that are more consistent with practice.

Ontology Mapping Evolution

Introdução Hoje em dia, o conceito de ontologia (Especificação explícita de uma conceptualização [Gruber, 1993]) é um conceito chave em sistemas baseados em conhecimento em geral e na Web Semântica em particular. Entretanto, os agentes de software nem sempre concordam com a mesma conceptualização, justificando assim a existência de diversas ontologias, mesmo que tratando o mesmo domínio de discurso. Para resolver/minimizar o problema de interoperabilidade entre estes agentes, o mapeamento de ontologias provou ser uma boa solução. O mapeamento de ontologias é o processo onde são especificadas relações semânticas entre entidades da ontologia origem e destino ao nível conceptual, e que por sua vez podem ser utilizados para transformar instâncias baseadas na ontologia origem em instâncias baseadas na ontologia destino. Motivação Num ambiente dinâmico como a Web Semântica, os agentes alteram não só os seus dados mas também a sua estrutura e semântica (ontologias). Este processo, denominado evolução de ontologias, pode ser definido como uma adaptação temporal da ontologia através de alterações que surgem no domínio ou nos objectivos da própria ontologia, e da gestão consistente dessas alterações [Stojanovic, 2004], podendo por vezes deixar o documento de mapeamento inconsistente. Em ambientes heterogéneos onde a interoperabilidade entre sistemas depende do documento de mapeamento, este deve reflectir as alterações efectuadas nas ontologias, existindo neste caso duas soluções: (i) gerar um novo documento de mapeamento (processo exigente em termos de tempo e recursos computacionais) ou (ii) adaptar o documento de mapeamento, corrigindo relações semânticas inválidas e criar novas relações se forem necessárias (processo menos existente em termos de tempo e recursos computacionais, mas muito dependente da informação sobre as alterações efectuadas). O principal objectivo deste trabalho é a análise, especificação e desenvolvimento do processo de evolução do documento de mapeamento de forma a reflectir as alterações efectuadas durante o processo de evolução de ontologias. Contexto Este trabalho foi desenvolvido no contexto do MAFRA Toolkit1. O MAFRA (MApping FRAmework) Toolkit é uma aplicação desenvolvida no GECAD2 que permite a especificação declarativa de relações semânticas entre entidades de uma ontologia origem e outra de destino, utilizando os seguintes componentes principais: Concept Bridge – Representa uma relação semântica entre um conceito de origem e um de destino; Property Bridge – Representa uma relação semântica entre uma ou mais propriedades de origem e uma ou mais propriedades de destino; Service – São aplicados às Semantic Bridges (Property e Concept Bridges) definindo como as instâncias origem devem ser transformadas em instâncias de destino. Estes conceitos estão especificados na ontologia SBO (Semantic Bridge Ontology) [Silva, 2004]. No contexto deste trabalho, um documento de mapeamento é uma instanciação do SBO, contendo relações semânticas entre entidades da ontologia de origem e da ontologia de destino. Processo de evolução do mapeamento O processo de evolução de mapeamento é o processo onde as entidades do documento de mapeamento são adaptadas, reflectindo eventuais alterações nas ontologias mapeadas, tentando o quanto possível preservar a semântica das relações semântica especificadas. Se as ontologias origem e/ou destino sofrerem alterações, algumas relações semânticas podem tornar-se inválidas, ou novas relações serão necessárias, sendo por isso este processo composto por dois sub-processos: (i) correcção de relações semânticas e (ii) processamento de novas entidades das ontologias. O processamento de novas entidades das ontologias requer a descoberta e cálculo de semelhanças entre entidades e a especificação de relações de acordo com a ontologia/linguagem SBO. Estas fases (“similarity measure” e “semantic bridging”) são implementadas no MAFRA Toolkit, sendo o processo (semi-) automático de mapeamento de ontologias descrito em [Silva, 2004]. O processo de correcção de entidades SBO inválidas requer um bom conhecimento da ontologia/linguagem SBO, das suas entidades e relações, e de todas as suas restrições, i.e. da sua estrutura e semântica. Este procedimento consiste em (i) identificar as entidades SBO inválidas, (ii) a causa da sua invalidez e (iii) corrigi-las da melhor forma possível. Nesta fase foi utilizada informação vinda do processo de evolução das ontologias com o objectivo de melhorar a qualidade de todo o processo. Conclusões Para além do processo de evolução do mapeamento desenvolvido, um dos pontos mais importantes deste trabalho foi a aquisição de um conhecimento mais profundo sobre ontologias, processo de evolução de ontologias, mapeamento etc., expansão dos horizontes de conhecimento, adquirindo ainda mais a consciência da complexidade do problema em questão, o que permite antever e perspectivar novos desafios para o futuro.

Iowa Brownfield Reuse Guide, September 15, 2008

Brownfield sites come in all shapes and sizes, from vacated industrial sites to a single building plagued with asbestos materials. Brownfield sites are found in both urban and rural settings and present challenges that make the cleanup and redevelopment of these sites unique as compared with other real estate projects.

The Gene Ontology: enhancements for 2011.

The Gene Ontology (GO) (http://www.geneontology.org) is a community bioinformatics resource that represents gene product function through the use of structured, controlled vocabularies. The number of GO annotations of gene products has increased due to curation efforts among GO Consortium (GOC) groups, including focused literature-based annotation and ortholog-based functional inference. The GO ontologies continue to expand and improve as a result of targeted ontology development, including the introduction of computable logical definitions and development of new tools for the streamlined addition of terms to the ontology. The GOC continues to support its user community through the use of e-mail lists, social media and web-based resources.

Complete Ontology- Three level Integration

This file contains the complete ontology (OntoProcEDUOC_OKI_Final.owl). At loading time to edit, the OKI ontology corresponding to the implementation level (OntoOKI_DEFINITIVA.owl)must be imported.

Semantic domains and supersens tagging for domain-specific ontology learning

In this paper we propose a novel unsupervised approach to learning domain-specific ontologies from large open-domain text collections. The method is based on the joint exploitation of Semantic Domains and Super Sense Tagging for Information Retrieval tasks. Our approach is able to retrieve domain specific terms and concepts while associating them with a set of high level ontological types, named supersenses, providing flat ontologies characterized by very high accuracy and pertinence to the domain.

The Gene Ontology in 2010: extensions and refinements.

The Gene Ontology (GO) Consortium (http://www.geneontology.org) (GOC) continues to develop, maintain and use a set of structured, controlled vocabularies for the annotation of genes, gene products and sequences. The GO ontologies are expanding both in content and in structure. Several new relationship types have been introduced and used, along with existing relationships, to create links between and within the GO domains. These improve the representation of biology, facilitate querying, and allow GO developers to systematically check for and correct inconsistencies within the GO. Gene product annotation using GO continues to increase both in the number of total annotations and in species coverage. GO tools, such as OBO-Edit, an ontology-editing tool, and AmiGO, the GOC ontology browser, have seen major improvements in functionality, speed and ease of use.

Evaluation of reclamation technologies for wastewater reuse

Reclamation and reuse of wastewater require the use of tools that minimize risks to health and natural ecosystems. There are various types of such tools, among which HACCP (hazardanalysis and critical control points) and barrier systems are gainingimportance. The research reported here aims to determine andevaluate the most efficient combinations of different treatmentsystems—barriers—for the reclamation of secondary effluentsfrom urban sewage treatment plants, and for obtaining water ofsufficient quality for reuse in accordance with existing legislation,in which water disinfection has become one of the keys tocompliance. Several conventional and non-conventional reclamationtechnologies are evaluated. The results lead us to recommendtreatment lines for the different reclaimed water uses established inthe Spanish legislation.

Gene Ontology Function prediction in Mollicutes using Protein-Protein Association Networks

Abstract Background: Many complex systems can be represented and analysed as networks. The recent availability of large-scale datasets, has made it possible to elucidate some of the organisational principles and rules that govern their function, robustness and evolution. However, one of the main limitations in using protein-protein interactions for function prediction is the availability of interaction data, especially for Mollicutes. If we could harness predicted interactions, such as those from a Protein-Protein Association Networks (PPAN), combining several protein-protein network function-inference methods with semantic similarity calculations, the use of protein-protein interactions for functional inference in this species would become more potentially useful. Results: In this work we show that using PPAN data combined with other approximations, such as functional module detection, orthology exploitation methods and Gene Ontology (GO)-based information measures helps to predict protein function in Mycoplasma genitalium. Conclusions: To our knowledge, the proposed method is the first that combines functional module detection among species, exploiting an orthology procedure and using information theory-based GO semantic similarity in PPAN of the Mycoplasma species. The results of an evaluation show a higher recall than previously reported methods that focused on only one organism network.

Reuse of Lime Sludge from Water Softening and Coal Combustion Byproducts, TR-459, 2004

Disposal of lime sludge remains a major challenge to cities in the Midwest. Disposal of lime sludge from water softening adds about 7-10% to the cost of water treatment. Having effective and safe options is essential for future compliance with the regulations of the State of Iowa and within budget restrictions. Dewatering and drying are essential to all reuse applications as this affects transportation costs and utility. Feasibility tests were conducted on some promising applications like SOx control in power generation facilities that burn coal, replacement of limestone as an ingredient in portland cement production, dust control on gravel roads, neutralization of industrial wastewater pH, and combination with fly ash or cement in construction fill applications. A detailed report and analysis of the construction fills application is presented in the second half of the report. A brief discussion of the results directly follows.

Applications for Reuse of Lime Sludge from Water Softening, TR-535, 2005

Lime sludge, an inert material mostly composed of calcium carbonate, is the result of softening hard water for distribution as drinking water. A large city such as Des Moines, Iowa, produces about 30,700 tons of lime sludge (dry weight basis) annually (Jones et al., 2005). Eight Iowa cities representing, according to the United States (U.S.) Census Bureau, 23% of the state’s population of 3 million, were surveyed. They estimated that they collectively produce 64,470 tons of lime sludge (dry weight basis) per year, and they currently have 371,800 tons (dry weight basis) stockpiled. Recently, the Iowa Department of Natural Resources directed those cities using lime softening in drinking water treatment to stop digging new lagoons to dispose of lime sludge. Five Iowa cities with stockpiles of lime sludge funded this research. The research goal was to find useful and economical alternatives for the use of lime sludge. Feasibility studies tested the efficacy of using lime sludge in cement production, power plant SOx treatment, dust control on gravel roads, wastewater neutralization, and in-fill materials for road construction. Applications using lime sludge in cement production, power plant SOx treatment, and wastewater neutralization, and as a fill material for road construction showed positive results, but the dust control application did not. Since the fill material application showed the most promise in accomplishing the project’s goal within the time limits of this research project, it was chosen for further investigation. Lime sludge is classified as inorganic silt with low plasticity. Since it only has an unconfined compressive strength of approximately 110 kPa, mixtures with fly ash and cement were developed to obtain higher strengths. When fly ash was added at a rate of 50% of the dry weight of the lime sludge, the unconfined strength increased to 1600 kPa. Further, friction angles and California Bearing Ratios were higher than those published for soils of the same classification. However, the mixtures do not perform well in durability tests. The mixtures tested did not survive 12 cycles of freezing and thawing and wetting and drying without excessive mass and volume loss. Thus, these mixtures must be placed at depths below the freezing line in the soil profile. The results demonstrated that chemically stabilized lime sludge is able to contribute bulk volume to embankments in road construction projects.