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In the second step mutations extracted from the text are mapped onto the candidate sequences using regular expressions generated from the mutation mentions extracted from the text.
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All VAD features mentioned are extracted from the short-term analysis frames (usually 20 to 40 ms), and decisions are made at each frame.
The researched mentions are only extracted from the post title and post snippet.
At the top level, 3075 mentions (12%) were extracted from the main title and description.
Some of the mentioned data are extracted from database of the net department, and other needed data are collected through interview with experts.
Perioperative data regarding the patients, interventions, and outcomes mentioned above were extracted from the included studies.
Data on the above mentioned indicators were extracted from included records and entered into a computerized database on Microsoft Excel.
Epidemiologic data on relevant covariates, previously obtained by questionnaire within each of the above mentioned studies, were extracted from existing databases and assembled.
As an example, just one of many, I mention an extract from his 2003 book The Other Russia, a series of essays for his followers subtitled Outlines for the Future.
Table 1 shows the number of gene/protein entities (both mentions and distinct entities) extracted from MEDLINE and PMC with the two gene normalization tools.
The previously mentioned features can be extracted from any color image.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com