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As the OOGP model was represented as a surface model, which represents objects with different surfaces, related surfaces that belong to one module were grouped together.
Genes not classified in a correlated module were grouped in a grey 'rest of data' module.
Learning outcomes that addressed the same EBHC competencies in a specific module were grouped together.
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Modules were grouped by the cluster algorithm CLICK, which does not make prior assumptions and avoids potential biases of the relatively limited CpG loci available here since they do not require pre-determined seeds [42].
Modules were grouped using a stringency threshold of 0.75.
The modules are grouped in three main functional blocks.
At the beginning of the simulation, the network description (NED) language is first used to describe the model structure, then the simple modules are grouped into the compound module by connection or combination, and, finally, the largest compound module performs the simulation.
The mean HADS ratings after Module M1-M3 formed a cognitive block and the mean HADS scores for Module M4-M9 were grouped into an exposure block.
Eight items were generated for the PRE module, which were grouped into three hypothetical domains (General feelings about injections, Feelings about giving self-injections, and Satisfaction with self-injection) (Table 1).
Non-module genes were grouped into a single (improper) grey module.
The experiment modules were Groups A, B, C, and D, and mixed with allochroic powders of different specifications.
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