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Here we consider a new approach to solving a one-stage optimization problem which is based on transformation of chance constraints into deterministic ones.
This approach is based on transformation of chance constraints into deterministic ones.
The first is the Short Time series Expression Miner (STEM) algorithm and software developed by Ernst et al., where all genes are clustered into one of a set of pre-defined patterns based on transformation of gene profiles into "units of change" [ 23].
The transient gene expression assay [ 6, 11, 12, 18] is based on transformation of single leaf epidermal cells, followed by subsequent inoculation of transformed leaves with specific powdery mildew isolates.
Based on transformation efficiency, survival rate, and assuming only 10% of transgenics are germline transformants, Li et al. (2011) estimated that generation of a single germline transformed line would require 167 transgenic iL3s in S. stercoralis and 20 transgenic iL3s in S. ratti.
The method is based on transformation of collections of output functions into state variables.
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Alternatives based on transformations of the model have been suggested ([15, 50]) but, as reported in [33], they fall short when compared to SMC methods.
Moreover we provide a novel derivation of this q-discrete system based on transformations obtained with the help of affine Weyl groups.
Casdagli et al. [35] give a thorough treatment of such techniques based on transformations of univariate maps, showing how optimal noise reduction can be achieved.
We developed an alternative cloning procedure based on transformation-associated recombination (TAR) in the yeast Saccharomyces cerevisiae [ 3, 4].
The semantics of transformation rules is defined based on graph transformation theory.
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