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To date, SHG holography methods have selected one particular pair of excitation and recording polarizations.
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By using combinatorial mutagenesis and selection methods we have selected variants of the Pm promoter with improved expression properties in E. coli, and we have also shown that such methods are highly useful to generate new basic knowledge of bacterial gene expression [ 5– 7].
From these methods we have selected the penalty function approach.
Using a stringent and unsupervised version of ISA, see Materials and Methods, we have selected two modules, which contained FBN1 and are composed by 148 and 154 genes, respectively (supplementary material Table S4).
Instead we aim to achieve a better understanding of the analytic properties of the methods we have selected, features of the data that may be problematic for each, and which may be most appropriate for particular situations.
Further, the methods we have selected are well-suited to a computationally intensive bootstrap approach included in our pipeline for generating distributions of species tree topologies, and the more computationally intensive of the single-stage methods would not be easily accommodated within this framework.
To develop the fuzzy VM selection method we have selected three distinguished inputs and each of them offers some advantages over others and different researches have already proven them.
We have estimated image pair similarities by JacS and tf-idf-based method and have selected image pairs which are selected in both methods to make the accuracy higher.
Using this method, we have selected more than 20 lines of Chinese cabbage with heat resistance.
With the MCODE method we have selected only one Int-PI module, Int-5 which overlaps with Ortho-4.
To test the classification specificity of our method, we have selected 800 proteins (400 pairs) with the first 100 pairs sharing an immediate parent GO term (level 1); second 100 pairs sharing a common parent separated by an edge distance of 2 in the GO tree (level 2).
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