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A taxonomy is built based upon data representations, i.e., tensor imaging data, brain network data, and multi-view data, following which the relationships between different data mining algorithms and different neuroimaging applications are summarized.
The resulting distribution was determined using a robust Gaussian distribution fitting procedure with outlier removal (10% of the data), following which the mean and standard deviation (SD) were calculated.
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Interview questions were informed by the relevant literature, a pilot interview with one NP participant and an observation of two nurse practitioners at one site prior to data collection, following which questions were revised.
Laboratory data for each month at each health facility were transcribed into formatted tables, following which data were double entered into an MS Access database.
Cows were fed their respective diet for at least 28 d, following which data and samples were collected over 6 d.
We assumed a constant distribution of inpatient and outpatient cases (as observed in the AOK PLUS data set for 2007 2010), following which we estimated the number of cases in both the groups inpatient and outpatient for the year 2005.
They were used as a framework to categorise the data from each focus group, following which each theme was developed by identifying subordinate themes in the data assigned to it.
Images were initially reviewed blind, following which, clinical data were made available, including pulmonary function tests (PFTs) and aspiration risk, to assist with interpretation.
Microarrays were scanned in Agilent Gene Scanner (Affymetrix), following which raw data processing was performed with the Microarray Suite software, version 5, with a global scaling factor for target intensity equal to 500.
The data were subjected to dimensionality analysis, following which two-dimensional inversions for the TE and TM modes were carried out.
The user is then asked to define simulation settings, following which, pertinent spatial data is extracted from raster files and sent to variables in the VENSIM simulation model.
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