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The development of methods to study mast cell biology using cell culture and murine models has proven invaluable in this regard.
Assimilation of hyperspectral sounder data into numerical weather prediction (NWP) models has proven vital to generating accurate model analyses of tropospheric temperature and humidity where few conventional observations exist.
The assimilation of Earth observation (EO) data into crop models has proven to be an efficient way to improve yield prediction at a regional scale by estimating key unknown crop management practices.
Although the LogP family of models has proven to be a valuable tool for assessing the communication performance of parallel architectures, non-intrusive LogP parameter assessment of real systems remains a difficult task.
However, translation of disease pathology into mouse models has proven to be challenging, primarily due to the complex genetic architecture of schizophrenia and the difficulties in the re-creation of susceptibility alleles in the mouse genome.
Generation and curation of plant genome-scale metabolic models has proven far more challenging, not the least of which is our incomplete knowledge of compartmentation and organelle transporters in plants.
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As a result, the development of general physics-based models has proved extremely challenging.
Sparsity-based models have proven to be very effective in most image processing applications.
These two classes of models have proven to be difficult to dissociate.
Numerical ground water models have proven to be essential when dealing with complex aquifer situations.
Again, these business models have proven successful and have been modeled by others.
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