Synthesis and structural characterization of thin multi-walled carbon nanotubes with a partially facetted cross section by a floating reactant method

ArticleCarbon. 43(11):2243-2250 (2005)journal articl

Normalizing biomedical terms by minimizing ambiguity and variability

<p>Abstract</p> <p>Background</p> <p>One of the difficulties in mapping biomedical named entities, e.g. genes, proteins, chemicals and diseases, to their concept identifiers stems from the potential variability of the terms. Soft string matching is a possible solution to the problem, but its inherent heavy computational cost discourages its use when the dictionaries are large or when real time processing is required. A less computationally demanding approach is to normalize the terms by using heuristic rules, which enables us to look up a dictionary in a constant time regardless of its size. The development of good heuristic rules, however, requires extensive knowledge of the terminology in question and thus is the bottleneck of the normalization approach.</p> <p>Results</p> <p>We present a novel framework for discovering a list of normalization rules from a dictionary in a fully automated manner. The rules are discovered in such a way that they minimize the ambiguity and variability of the terms in the dictionary. We evaluated our algorithm using two large dictionaries: a human gene/protein name dictionary built from BioThesaurus and a disease name dictionary built from UMLS.</p> <p>Conclusions</p> <p>The experimental results showed that automatically discovered rules can perform comparably to carefully crafted heuristic rules in term mapping tasks, and the computational overhead of rule application is small enough that a very fast implementation is possible. This work will help improve the performance of term-concept mapping tasks in biomedical information extraction especially when good normalization heuristics for the target terminology are not fully known.</p

Variation of radiocesium concentrations in cedar pollen in the Okutama area since the Fukushima Daiichi Nuclear Power Plant Accident

Due to releases of radionuclides in the Fukushima Daiichi Nuclear Power Plant Accident, radiocesium (¹³⁴Cs and ¹³⁷Cs) has been incorporated into large varieties of plant species and soil types. There is a possibility that radiocesium taken into plants is being diffused by pollen. Radiocesium concentrations in cedar pollen have been measured in Ome City, located in the Okutama area of metropolitan Tokyo, for the past 3 years. In this research, the variation of radiocesium concentrations was analysed by comparing data from 2011 to 2014. Air dose rates at 1 m above the ground surface in Ome City from 2011 to 2014 showed no significant difference. Concentration of ¹³⁷Cs contained in the cedar pollen in 2012 was about half that in 2011. Between 2012 and 2014, the concentration decreased by approximately one fifth, which was similar to the result of a press release distributed by the Japanese Ministry of Agriculture, Forestry and Fisheries

Text Mining the History of Medicine

Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it can be difficult for researchers to explore and search such large volumes of data in an efficient manner. Text mining (TM) methods can help, through their ability to recognise various types of semantic information automatically, e.g., instances of concepts (places, medical conditions, drugs, etc.), synonyms/variant forms of concepts, and relationships holding between concepts (which drugs are used to treat which medical conditions, etc.). TM analysis allows search systems to incorporate functionality such as automatic suggestions of synonyms of user-entered query terms, exploration of different concepts mentioned within search results or isolation of documents in which concepts are related in specific ways. However, applying TM methods to historical text can be challenging, according to differences and evolutions in vocabulary, terminology, language structure and style, compared to more modern text. In this article, we present our efforts to overcome the various challenges faced in the semantic analysis of published historical medical text dating back to the mid 19th century. Firstly, we used evidence from diverse historical medical documents from different periods to develop new resources that provide accounts of the multiple, evolving ways in which concepts, their variants and relationships amongst them may be expressed. These resources were employed to support the development of a modular processing pipeline of TM tools for the robust detection of semantic information in historical medical documents with varying characteristics. We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system. The novel resources are available for research purposes, while the processing pipeline and its modules may be used and configured within the Argo TM platform

Supporting the education evidence portal via text mining

The UK Education Evidence Portal (eep) provides a single, searchable, point of access to the contents of the websites of 33 organizations relating to education, with the aim of revolutionizing work practices for the education community. Use of the portal alleviates the need to spend time searching multiple resources to find relevant information. However, the combined content of the websites of interest is still very large (over 500 000 documents and growing). This means that searches using the portal can produce very large numbers of hits. As users often have limited time, they would benefit from enhanced methods of performing searches and viewing results, allowing them to drill down to information of interest more efficiently, without having to sift through potentially long lists of irrelevant documents. The Joint Information Systems Committee (JISC)-funded ASSIST project has produced a prototype web interface to demonstrate the applicability of integrating a number of text-mining tools and methods into the eep, to facilitate an enhanced searching, browsing and document-viewing experience. New features include automatic classification of documents according to a taxonomy, automatic clustering of search results according to similar document content, and automatic identification and highlighting of key terms within documents

The degree of acute descending control of spinal nociception in an area of primary hyperalgesia is dependent on the peripheral domain of afferent input

Descending controls of spinal nociceptive processing play a critical role in the development of inflammatory hyperalgesia. Acute peripheral nociceptor sensitization drives spinal sensitization and activates spino–supraspinal–spinal loops leading to descending inhibitory and facilitatory controls of spinal neuronal activity that further modify the extent and degree of the pain state. The afferent inputs from hairy and glabrous skin are distinct with respect to both the profile of primary afferent classes and the degree of their peripheral sensitization. It is not known whether these differences in afferent input differentially engage descending control systems to different extents or in different ways. Injection of complete Freund's adjuvant resulted in inflammation and swelling of hairy hind foot skin in rats, a transient thermal hyperalgesia lasting 72 h). In hairy skin, transient hyperalgesia was associated with sensitization of withdrawal reflexes to thermal activation of either A- or C-nociceptors. The transience of the hyperalgesia was attributable to a rapidly engaged descending inhibitory noradrenergic mechanism, which affected withdrawal responses to both A- and C-nociceptor activation and this could be reversed by intrathecal administration of yohimbine (α-2-adrenoceptor antagonist). In glabrous skin, yohimbine had no effect on an equivalent thermal inflammatory hyperalgesia. We conclude that acute inflammation and peripheral nociceptor sensitization in hind foot hairy skin, but not glabrous skin, rapidly activates a descending inhibitory noradrenergic system. This may result from differences in the engagement of descending control systems following sensitization of different primary afferent classes that innervate glabrous and hairy skin

Preparation of Fe-Pt thin-sheet magnets using exfoliation behavior

In this research, Fe-Pt thin sheets thicker than 10 microns with Fe contents ranging from 50 to 60 at.% were prepared. Isotropic Fe-Pt thin sheets could be obtained by taking advantage of the exfoliation behavior after depositing Fe-Pt films on Si substrates using a laser ablation technique. A post-annealing process was used to obtain the L10 phase, and the (BH)max value of Fe-Pt thin sheets showed approximately 70 kJ/m3. Moreover, the test of a cantilever containing the obtained Fe-Pt thin sheet showed good mechanical characteristics

Mining metabolites: extracting the yeast metabolome from the literature

Text mining methods have added considerably to our capacity to extract biological knowledge from the literature. Recently the field of systems biology has begun to model and simulate metabolic networks, requiring knowledge of the set of molecules involved. While genomics and proteomics technologies are able to supply the macromolecular parts list, the metabolites are less easily assembled. Most metabolites are known and reported through the scientific literature, rather than through large-scale experimental surveys. Thus it is important to recover them from the literature. Here we present a novel tool to automatically identify metabolite names in the literature, and associate structures where possible, to define the reported yeast metabolome. With ten-fold cross validation on a manually annotated corpus, our recognition tool generates an f-score of 78.49 (precision of 83.02) and demonstrates greater suitability in identifying metabolite names than other existing recognition tools for general chemical molecules. The metabolite recognition tool has been applied to the literature covering an important model organism, the yeast Saccharomyces cerevisiae, to define its reported metabolome. By coupling to ChemSpider, a major chemical database, we have identified structures for much of the reported metabolome and, where structure identification fails, been able to suggest extensions to ChemSpider. Our manually annotated gold-standard data on 296 abstracts are available as supplementary materials. Metabolite names and, where appropriate, structures are also available as supplementary materials