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This method also reduces the large dataset into smaller ones that lessens the computational burden significantly.
The method also reduces cross-contamination among different samples to a minimum.
The method also reduces the human work spent on building the numerical model to generation of the description of the geometry.
The current method also reduces artifact or operator error as the collection of data is performed in an unbiased manner by software that has been provided guidance to determine the area of plaques by trained personnel.
However, the method also reduces, by ~ 3 weeks, the time needed to produce a protein-secreting culture of 10 cells.
This method also reduces the hyper-parameters of the prior distribution from 2 (proportion and variance of SNP with big effects) to 1 (proportion of SNP with big effects), assuming the overall genetic variance is known.
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This method also reduced the cost, blended into our work flow nicely and required no additional equipment.
The method also reduced the relationship between the bulls selected to nearly the same extent as the method that is aimed at maximising genetic gain adjusted for the relationship between bulls.
The data processing method also reduced the impact of GPS point drift, a phenomena where the GPS-recorded position deviates randomly around a true location, likely caused by changes in the satellite configuration.
Fast, three-dimensional (3D) spatial encoded methods also reduce the scan time.
Although the other three methods also reduce the delay, but the rate is slower than the MultiKnap-MC method.
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