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For each motif the corresponding entries in word list were determined and the highest scoring word was identified.
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If a positive or negative word is identified, its new sentiment value is calculated by using Eq. (3) (e.g. enjoy: 20, do not enjoy: −40).
Each word is identified through is nucleotide sequence and contains information about the expected number of sequences it was computed to occur in (E_S) as well as the expected number of total occurrences in the set of sequences (E).
Once the key words were identified, they traced and backtracked all possible paths that branched from the identified words or phrases in the mind map.
The envelope extracted using the Hilbert transform reveal that the envelope is most important for speech reception, namely the words are identified according to the envelope [20].
Once all positive and negative words are identified in a sentence and their local context is verified, a combining process is performed in order to obtain the final sentiment value.
606 electronic records containing our key words were identified.
For each set of promoters, the statistically overrepresented words were identified.
Words were identified 615 ms after their uniqueness point, pseudoword variants were identified 674 ms after their deviation points.
Key words were identified as search terms: aspiration, dysphagia, swallowing difficulty, dementia, Alzheimer, PEG, and enteral and feeding tube.
Sentences consisted of five words, including three key words (e.g., the lunch was very early), scored correct when all key words were identified.
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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