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The magnetisation transfer (MT) technique reflects data of a different set of molecular properties than standard T1 and T2 imaging.
Despite applying the model to data of a different temporal and spatial resolution to the training data-set, it still provides a reliable surrogate for rain days, with the model explaining 92% of the variation (R2 = 0.92, df = 1210, mean predicted – mean actual = 0.6 rain days).
The outcome of this process is a map segmented into different areas, each trained to respond to data of a different type.
Such fitting of an X-ray structure into low-resolution data of a different conformational state is often done by domain segmentation followed by fitting each domain of the system as an independent rigid body block.
The purpose of the method presented here is to fit the X-ray structure to raw experimental (in this case simulated) SAXS data of a different conformation of the same molecule.
That is, there are single modal data for a set of patients and then other available data of a different modality from observations on a different set of patients; both sets refer to the same disease although possibly at different stage or phase of the disease.
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In Norway, a fully new EA is needed for the re-use of existing data for a different purpose.
The new research instead looks at three years' worth of data (from a different part of the country) and shows that the rate of return on cows is rather variable.
For learning each partition of data, a different Fuzzy ARTMAP network is used.
Our BMD analysis of these data provides a different perspective of the relationship we observed between lead exposure and blood pressure increase.
However, different assumptions and estimation methods can be developed in the presence of additional validation data or a different structure of quality control data.
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