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The median follow-up time was 18 matches (range 1 29 matches).
The top protein matches range between 50 to over 500 amino acids in length and would thus indicate that the predicted open reading frame generated from this library did not have any bias towards long or short protein sequences (Table 1).
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The latest involved gambling rings in Asia and eastern Europe, and matches ranging from the lower reaches of European leagues to friendly internationals.
Real Madrid's matches ranged from close affairs to comfortable victories.
The average percent identity of the sequence matches ranged from 83% to 99%.
The nucleotide sequence identity between these sequences and their corresponding best matches ranged from 91%to100%0%.
In total, almost 23% of the 2,362 ESTs were comprised of 14 clusters with matches ranging from cathepsin L to beta tubulin.
Matching by dispense codes resulted in fewer matches for the reference data (68.93%), although among codes that had been used, the matches ranged from 77.2% in 1999 to 94.68% in 2009.
As shown in Figure 2, 42% of sequences had significant homology matches (< 1.0E10− 50) in the NCBI database, while 58% of the sequences had matches ranging between 1.0E10-3 1.0E10-310-50.
The length of top UniRef100 matches ranged between 50 to >500 amino acids in length, indicating that the sequences and therefore the cDNAs generated from this library were not biased towards either long or short sequences.
In this section, we first explain the reason for setting the matching range and choose the matching range by evaluating the precision recall performance.
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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