Your English writing platform
Discover LudwigSuggestions(1)
Exact(5)
The detection of microcalcification clusters is achieved by the following computerized approach.
The aggregation of the local posterior distributions from all clusters is achieved via an enhanced version of the maximum consensus algorithm.
Finally, the separation of the different populations of clusters is achieved by density gradient centrifugation.
The highest percentage of edge clusters is achieved for almost balanced contribution of topological and attribute similarity.
Our results suggest that functional diversity of these clusters is achieved through the birth-and-death evolution of variable exon duplication, divergence, and deletion, but conservation of the constant exons, which are essential in maintaining their basic functions.
Similar(55)
The crystallization of the clusters was achieved at annealing temperature of 700°C.
Disconnection of random boundaries between the clusters was achieved by the formation of annealing twins through the impingement of the growing clusters during the thermomechanical process.
Once an acceptably optimum configuration of neighboring clusters was achieved, the optimum routing was obtained by calculating the sequence that the transports involved in the process should follow and defining the shovel node associated with each cluster.
An initial solution with six clusters was achieved using the auto-clustering algorithm.
Classification of the translated ORFs into clusters was achieved by breadth-first traversing this network at different e-value thresholds, ranging from E-30 to E-12.
The final clustering is achieved by concatenating the leftover clusters of all iterations.
Write better and faster with AI suggestions while staying true to your unique style.
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