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A literature search was carried out by using three electronic databases from the first available year to April 2010 without language limits.
Relevant articles were identified by searching the following databases from the first date available until August 2014: Ovid MEDLINE; Ovid EMBASE; PubMed; Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials.
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Again, we build our database from the first 80% of each record.
The algorithm builds an initial database from the first 2 min of the data, and adds new entries to the database whenever it encounters a segment that it judges to be relatively clean.
Though, we start with a database built from only 20% of the signal, we are able to achieve performance similar to the offline approach, where we built the database from the first 80%.
To identify and assess trends in the development of LCA as a tool and the evolution of its scope and methodologies, we assessed the publication landscape for LCA in the Scopus database from the first recorded publication in 1978 through 2013.
Based on the brain activity this generated, and the database created from the first phase of the experiment, the computer was asked to select segments of 5,000 hours of randomly selected YouTube clips that best matched the second sequence of clips.
We searched databases from the time of first publication (Medline 1946, Embase 1947, Web of Science 1899) until June 2013.
In addition to the basic architecture, we describe tools to populate the patient's record with data from hospital databases and the first testbed applications we are deploying.
In the initialization step, the Repeated Ad Database is built from the first 300 minutes of the collection using the proposed approach without candidate segmentation.
The type of amyloid and date of diagnosis was obtained for each case from the NAC database and survival from the first assessment at NAC was calculated.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com