1,505 research outputs found

    Selective Metal Cation Capture by Soft Anionic Metal-Organic Frameworks via Drastic Single-Crystal-to-Single-Crystal Transformations

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    In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension. In contrast to previous work our model does not impose a prior on the rate at which documents are added to the corpus nor does it adopt the Markovian assumption which overly restricts the type of changes that the model can capture. Our key technical contribution is a framework based on (i) discretization of time into epochs, (ii) epoch-wise topic discovery using a hierarchical Dirichlet process-based model, and (iii) a temporal similarity graph which allows for the modelling of complex topic changes: emergence and disappearance, evolution, and splitting and merging. The power of the proposed framework is demonstrated on the medical literature corpus concerned with the autism spectrum disorder (ASD) - an increasingly important research subject of significant social and healthcare importance. In addition to the collected ASD literature corpus which we will make freely available, our contributions also include two free online tools we built as aids to ASD researchers. These can be used for semantically meaningful navigation and searching, as well as knowledge discovery from this large and rapidly growing corpus of literature.Comment: In Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 201

    MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge

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    <p>Abstract</p> <p>Background</p> <p>Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly applied to disease-effect relation. With the advancement in biomedical science, it has become imperative to extract and combine information from multiple disjoint researches, studies and articles to infer new hypotheses and expand knowledge.</p> <p>Methods</p> <p>We propose MKEM, a Multi-level Knowledge Emergence Model, to discover implicit relationships using Natural Language Processing techniques such as Link Grammar and Ontologies such as Unified Medical Language System (UMLS) MetaMap. The contribution of MKEM is as follows: First, we propose a flexible knowledge emergence model to extract implicit relationships across different levels such as molecular level for gene and protein and Phenomic level for disease and treatment. Second, we employ MetaMap for tagging biological concepts. Third, we provide an empirical and systematic approach to discover novel relationships.</p> <p>Results</p> <p>We applied our system on 5000 abstracts downloaded from PubMed database. We performed the performance evaluation as a gold standard is not yet available. Our system performed with a good precision and recall and we generated 24 hypotheses.</p> <p>Conclusions</p> <p>Our experiments show that MKEM is a powerful tool to discover hidden relationships residing in extracted entities that were represented by our Substance-Effect-Process-Disease-Body Part (SEPDB) model. </p

    Large-scale latitudinal and vertical distributions of NMHCs and selected halocarbons in the troposphere over the Pacific Ocean during the March-April 1999 Pacific Exploratory Mission (PEM-Tropics B)

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    Nonmethane hydrocarbons (NMHCs) and selected halocarbons were measured in whole air samples collected over the remote Pacific Ocean during NASA's Global Tropospheric Experiment (GTE) Pacific Exploratory Mission-Tropics B (PEM-Tropics B) in March and early April 1999. The large-scale spatial distributions of NMHCs and C2Cl4 reveal a much more pronounced north-south interhemispheric gradient, with higher concentrations in the north and lower levels in the south, than for the late August to early October 1996 PEM-Tropics A experiment. Strong continental outflow and winter-long accumulation of pollutants led to seasonally high Northern Hemisphere trace gas levels during PEM-Tropics B. Observations of enhanced levels of Halon 1211 (from developing Asian nations such as the PRC) and CH3Cl (from SE Asian biomass burning) support a significant southern Asian influence at altitudes above 1 km and north of 10° N. By contrast, at low altitude over the North Pacific the dominance of urban/industrial tracers, combined with low levels of Halon 1211 and CH3Cl, indicate a greater influence from developed nations such as Japan, Europe, and North America. Penetration of air exhibiting aged northern hemisphere characteristics was frequently observed at low altitudes over the equatorial central and western Pacific south to ∼5° S. The relative lack of southern hemisphere biomass burning sources and the westerly position of the South Pacific convergence zone contributed to significantly lower PEM-Tropics B mixing ratios of the NMHCs and CH3Cl south of 10° S compared to PEM-Tropics A. Therefore the trace gas composition of the South Pacific troposphere was considerably more representative of minimally polluted tropospheric conditions during PEM-Tropics B. Copyright 2001 by the American Geophysical Union
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