12 research outputs found

    Extracting the abstraction pyramid from complex networks

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    <p>Abstract</p> <p>Background</p> <p>At present, the organization of system modules is typically limited to either a multilevel hierarchy that describes the "vertical" relationships between modules at different levels (e.g., module A at level two is included in module B at level one), or a single-level graph that represents the "horizontal" relationships among modules (e.g., genetic interactions between module A and module B). Both types of organizations fail to provide a broader and deeper view of the complex systems that arise from an integration of vertical and horizontal relationships.</p> <p>Results</p> <p>We propose a complex network analysis tool, Pyramabs, which was developed to integrate vertical and horizontal relationships and extract information at various granularities to create a pyramid from a complex system of interacting objects. The pyramid depicts the nested structure implied in a complex system, and shows the vertical relationships between abstract networks at different levels. In addition, at each level the abstract network of modules, which are connected by weighted links, represents the modules' horizontal relationships. We first tested Pyramabs on hierarchical random networks to verify its ability to find the module organization pre-embedded in the networks. We later tested it on a protein-protein interaction (PPI) network and a metabolic network. According to Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), the vertical relationships identified from the PPI and metabolic pathways correctly characterized the <it>inclusion </it>(i.e., <it>part-of</it>) relationship, and the horizontal relationships provided a good indication of the functional closeness between modules. Our experiments with Pyramabs demonstrated its ability to perform knowledge mining in complex systems.</p> <p>Conclusions</p> <p>Networks are a flexible and convenient method of representing interactions in a complex system, and an increasing amount of information in real-world situations is described by complex networks. We considered the analysis of a complex network as an iterative process for extracting meaningful information at multiple granularities from a system of interacting objects. The quality of the interpretation of the networks depends on the completeness and expressiveness of the extracted knowledge representations. Pyramabs was designed to interpret a complex network through a disclosure of a pyramid of abstractions. The abstraction pyramid is a new knowledge representation that combines vertical and horizontal viewpoints at different degrees of abstraction. Interpretations in this form are more accurate and more meaningful than multilevel dendrograms or single-level graphs. Pyramabs can be accessed at <url>http://140.113.166.165/pyramabs.php/</url>.</p

    Critical appraisal of the management of severe malnutrition: 2. Dietary management

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    Abstract: In the dietary management of severe acute malnutrition in children, there is evidence to support the WHO Manual's protocol of cautious feeding of a low energy and protein formula with small frequent feeds in the initial phase of treatment, particularly in kwashiorkor. However, this initial milk diet (WHO F-75) might benefit from increasing the sulphur amino acid, phosphorus and potassium content and reducing the lactose content, but further studies are needed. Careful tube-feeding results in faster initial recovery and weight gain, but has a significant risk of aspiration in poorly supervised settings. Ready-to-use therapeutic food is an important recent advance in the dietary management of malnutrition in ambulatory settings, allowing more effective prevention programmes and earlier discharge from hospital where community follow-up is available. It should be included in future protocols. There is very good evidence on the use of micronutrients such as zinc, and preliminary evidence suggests that smaller doses of daily vitamin A are preferable to a single large dose on admission for severe malnutrition
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