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One (3) reported identification of Yersinia pestis specific genes in teeth from patients who died during the Justinian plague; another proposed identification of Y. pestis strains by using variable numbers of tandem repeats analysis (VNTR) (4).
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Mice were obtained by overnight trapping using variable numbers of Longworth traps assigned randomly amongst 80 permanently marked stations (1 or 0 traps/station).
The total number of medical personnel, other technical personnel, and the number of non-medical personnel was used as an input variable by Cheng et al. [13] who used variable numbers of doctors, nurses, and the number of administrative and other staff.
The optimal cell density was confirmed by using a variable number of cells.
To determine detailed associations between environmental and human clinical samples, we examined the genetic diversity among these samples by using multilocus variable number of tandem repeats analysis (MLVA) (13 ).
A total of 187 colonies were typed by using multilocus variable-number tandem-repeat (VNTR) analysis as described (9 – 12 ).
This study aimed to determine the S. Enteritidis lineages in Japan over the last few decades by using multilocus variable-number tandem repeat analysis (MLVA).
To assess the clonal links, the four isolates were genotyped by using the variable-number tandem-repeat (VNTR) method (with a 15-locus set) associated with MLST analysis [ 7, 8].
Tissues with cycle threshold (Ct) values <34 (1/case) were typed by using multilocus variable-number tandem-repeat analyses (MLVA) for 11 loci, as described (2, 5 ); results were compared with known MLVA typing data from the Netherlands.
We characterized the isolates by using multilocus variable-number tandem repeat analysis (MLVA) and multiple antigen sequence typing (MAST) to partially sequence the genes encoding pertactin (prn), B. pertussis toxin S1 subunit (ptxA, also designated ptxS1), B. pertussis toxin promoter (ptxP), and tracheal colonization factor A (tcfA).
This study hopes to contribute to literature for making better decisions on selection of appropriate cluster methods by using subgroup sizes, variable numbers, subgroup means and variances.
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