The exotic pet trade may be the principal mechanism for the establishment of invasive reptile species. The escape or deliberate release of captive reptiles has resulted in the establishment of many species globally. In Australia, exposure to this entry pathway has been limited due to long-term legislation preventing the importation and keeping of exotic reptiles by private keepers. Despite this, the corn snake Elaphe guttata is still illegally maintained in many private collections, resulting in occasional reports of released or escaped specimens. We recorded 79 free-ranging corn snakes captured by three wildlife organizations between 2002 and 2014 in the greater Sydney region, Australia. There was an increasing encounter rate over time, suggesting that this is a growing problem and may result in the establishment and spread of this species into Australia. Corn snakes were recorded throughout the Sydney region. No snake clusters were found and this suggests that populations may not have become established in the wild yet.

BackgroundRapid identification of subject experts for medical topics helps in improving the implementation of discoveries by speeding the time to market drugs and aiding in clinical trial recruitment, etc. Identifying such people who influence opinion through social network analysis is gaining prominence. In this work, we explore how to combine named entity recognition from unstructured news articles with social network analysis to discover opinion leaders for a given medical topic.MethodsWe employed a Conditional Random Field algorithm to extract three categories of entities from health-related new articles: Person, Organization and Location. We used the latter two to disambiguate polysemy and synonymy for the person names, used simple rules to identify the subject experts, and then applied social network analysis techniques to discover the opinion leaders among them based on their media presence. A network was created by linking each pair of subject experts who are mentioned together in an article. The social network analysis metrics (including centrality metrics such as Betweenness, Closeness, Degree and Eigenvector) are used for ranking the subject experts based on their power in information flow.ResultsWe extracted 734,204 person mentions from 147,528 news articles related to obesity from January 1, 2007 through July 22, 2010. Of these, 147,879 mentions have been marked as subject experts. The F-score of extracting person names is 88.5%. More than 80% of the subject experts who rank among top 20 in at least one of the metrics could be considered as opinion leaders in obesity.ConclusionThe analysis of the network of subject experts with media presence revealed that an opinion leader might have fewer mentions in the news articles, but a high network centrality measure and vice-versa. Betweenness, Closeness and Degree centrality measures were shown to supplement frequency counts in the task of finding subject experts. Further, opinion leaders missed in scientific publication network analysis could be retrieved from news articles.

Background: We are witnessing an exponential increase in biomedical research citations in PubMed. However, translating biomedical discoveries into practical treatments is estimated to take around 17 years, according to the 2000 Yearbook of Medical Informatics, and much information is lost during this transition. Pharmaceutical companies spend huge sums to identify opinion leaders and centers of excellence. Conventional methods such as literature search, survey, observation, self‐identification, expert opinion, and sociometry not only need much human effort, but are also non‐comprehensive. Such huge delays and costs can be reduced by “connecting those who produce the knowledge with those who apply it”. A humble step in this direction is large‐scale discovery of persons and organizations involved in specific areas of research. This can be achieved by automatically extracting and disambiguating author names and affiliation strings retrieved through Medical Subject Heading (MeSH) terms and other keywords associated with articles in PubMed. In this study, we propose NEMO (Normalization Engine for Matching Organizations), a system for extracting organization names from the affiliation strings provided in PubMed abstracts, building a thesaurus (list of synonyms) of organization names, and subsequently normalizing them to a canonical organization name using the thesaurus. Results: We used a parsing process that involves multi‐layered rule matching with multiple dictionaries. The normalization process involves clustering based on weighted local sequence alignment metrics to address synonymy at word level, and local learning based on finding connected components to address synonymy. The graphical user interface and java client library of NEMO are available at http://lnxnemo.sourceforge.net. Conclusion: NEMO associates each biomedical paper and its authors with a unique organization name and the geopolitical location of that organization. This system provides more accurate information about organizations than the raw affiliation strings provided in PubMed abstracts. It can be used for : a) bimodal social network analysis that evaluates the research relationships between individual researchers and their institutions; b) improving author name disambiguation; c) augmenting National Library of Medicine (NLM)’s Medical Articles Record System (MARS) system for correcting errors due to OCR on affiliation strings that are in small fonts; and d) improving PubMed citation indexing strategies (authority control) based on normalized organization name and country.

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The combination of scientific knowledge and experience is the key success for biomedical research. This chapter demonstrates some of the strategies used to help in identifying key opinion leaders with the expertise you need, thus enabling an effort to increase collaborative biomedical research.