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New York, NY, United States

Thomson Reuters Corporation is a major multinational mass media and information firm founded in Toronto and based in New York City and Toronto. It was created by the Thomson Corporation's purchase of British-based Reuters Group on 17 April 2008, and today is majority owned by The Woodbridge Company, a holding company for the Thomson family. The company operates in more than 100 countries, and has more than 60,000 employees around the world. Thomson Reuters was ranked as Canada's "leading corporate brand" in the 2010 Interbrand Best Canadian Brands ranking. Thomson Reuters' operational headquarters are located at 3 Times Square, Manhattan, New York City; its legal domicile is located at 333 Bay Street, Suite 400, Toronto, Ontario M5H 2R2, Canada. Wikipedia.

Background: Psychoactive medications, such as antidepressants, are one of the most widely prescribed categories of drugs in the US; yet few studies have comprehensively examined the conditions for which psychoactive medications are prescribed. To our knowledge, no prior study has examined the extent to which psychoactive medications are prescribed for non-psychiatric somatic illnesses or the main types of psychiatric disorders for which psychoactive medications are being used. Objective: To examine the diagnoses for which psychiatric medications are being prescribed in the US by analysing data from a nationally representative survey of physicians. Methods: The data were obtained from the 2005 National Disease and Therapeutic Index (NDTI), a continuing survey of a US office-based panel of physicians. The 2005 physician panel consisted of approximately 4000 physicians reporting quarterly, which was projected to a universe of 500 722 physicians. The study focused on the diagnoses that were given as the primary reason for prescribing the following types of psychotropic medications: antidepressants, antipsychotics and anti-anxiety medications. Results: Of the total number of antidepressant drug mentions, 92.7% were prescribed for psychiatric conditions. The most common (65.3%) were mood disorders (e.g. depression), followed by anxiety disorders (16.4%), which together comprised 81.7% of all antidepressant drug mentions. Of the total number of anti-anxiety drug mentions, 67.7% were prescribed for psychiatric conditions. The most common diagnosis was anxiety disorders (comprising 39.6% of all drug mentions), followed by mood disorders (comprising 18.9% of all drug mentions). Almost one-third of anxiety medication drug mentions were for non-psychiatric conditions or conditions of unspecified type. Of the total number of antipsychotic drug mentions, 98.9% were prescribed for psychiatric conditions. The most common diagnoses, comprising 39.0% of all drug mentions, were mood disorders such as depression and bipolar disorder. The second most common psychiatric diagnosis was schizophrenia or other psychotic disorders, comprising 34.5% of drug mentions. Approximately 7.4% of drug mentions were for delirium, dementia, amnestic or other cognitive disorders. Attention-deficit/ conduct/disruptive behaviour disorders were the diagnoses indicated on 5.7% of all antipsychotic drug mentions. Anxiety disorders were indicated on 5.5% of antipsychotic drug mentions. Disorders usually diagnosed in infancy/childhood/ adolescence (e.g. autism) comprised 2.3% of antipsychotic drug mentions. Conclusions: This research provides a broad view of the nature of psychoactive medication prescribing, which may serve as a guide to future research, policy and education about these medications, their perceived benefits and risks, and their uses. © 2010 Adis Data Information BV. All rights reserved.

Bessarabova M.,Thomson Reuters
BMC bioinformatics | Year: 2012

As it is the case with any OMICs technology, the value of proteomics data is defined by the degree of its functional interpretation in the context of phenotype. Functional analysis of proteomics profiles is inherently complex, as each of hundreds of detected proteins can belong to dozens of pathways, be connected in different context-specific groups by protein interactions and regulated by a variety of one-step and remote regulators. Knowledge-based approach deals with this complexity by creating a structured database of protein interactions, pathways and protein-disease associations from experimental literature and a set of statistical tools to compare the proteomics profiles with this rich source of accumulated knowledge. Here we describe the main methods of ontology enrichment, interactome topology and network analysis applied on a comprehensive, manually curated and semantically consistent knowledge source MetaBase and demonstrate several case studies in different disease areas.

Interdisciplinarity can be manifest in many forms: through collaboration or communication between scientists working in different fields or through the work of individual scientists who employ concepts or methods across disciplines. This latter form of interdisciplinarity is addressed here with the goal of understanding how ideas in different fields come together to create new opportunities for discovery. Maps of science are used to suggest possible interdisciplinary links which are then analyzed by co-citation context analysis. Interdisciplinary links are identified by juxtaposing a clustering and mapping of documents against a journal-based categorization of the same document clusters. Links between clusters are characterized as interdisciplinary based on the dissonance of their category assignments. To verify and probe more deeply into the meaning of interdisciplinary links, co-citation contexts for selected links from five separate cases are analyzed in terms of prominent cue words. This analysis reveals that interdisciplinary connections are often based on authors' perceptions of analogous problems across scientific domains. Cue words drawn from the citation contexts also suggest that these connections are viewed as important and ripe with both opportunity and risk. © 2009 AkadÉmiai KiadÓ, Budapest, Hungary.

Thomson Reuters | Date: 2015-04-07

Various embodiments of the invention provide solutions (including inter alia, systems, methods and software) for dealing with online fraud. Some embodiments function to access and/or obtain information from (and/or receive data from) a data source; the data might, for example, indicate a possible instance of online fraud. Certain embodiments, therefore, can be configured to analyze the data, e.g., to determine whether the data indicate a likely instance of online fraud. Such instances may be further investigated, and/or a response may be initiated. Data sources can include, without limitation, web pages, email messages, online chat sessions, domain zone files, newsgroups (and/or postings thereto), etc. Data obtained from the data sources can include, without limitation, suspect domain registrations, uniform resource locators, references to trademarks, advertisements, etc.

The present invention makes legal research more efficient by selecting clusters in response to the behavior of a user (e.g., a legal professional such as a paralegal, lawyer, or judge). The clusters, which are formed prior to the user accessing a legal document (and thus, providing user behavior to a system), are identified to the based upon a set of metadata associated with the legal document. At least two clusters are identified and a signal associated therewith is transmitted to the user. Each cluster is associated with a unique legal topic. Further, each cluster may comprise primary and/or secondary authority.

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