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In the detection module of two kinds of target objects, behind the CNN model, we add the logistic regression model to realize the classification of the image second category.
In other extensions to our basic model we add paternal union status, so that the identification in the IV model only comes from the interaction terms between union status and occupation.
As we did for the first synthetic model, we add noise to the data of the second model (Fig. 7(a)) and applied both the NSTD method, and the proposed method, so as to determine the lineaments, yielding the results given in Figs. 7(b) and 7(c), respectively.
To incorporate transcriptional regulation into the model, we add a transcription factor, TA, which like EA is produced from C. TA can act as an enhancer or repressor for the production of EA, with the sign and strength of the regulation as two independent parameters (see Materials and Methods).
For each annotation in a model we add an explicit link to the data entry in the referenced bio-ontology.
In the third model, we add the expectations participants had about the punishment level of their group members.
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Because age was included in the second-best model, we added age to unpacked models in a separate analysis (not shown).
In addition to the constructional selection, which can be illustrated by the NK model, we added environmental selection into the simulation and explored technological innovation as the result of the interaction between these two kinds of selection.
To this model we added the MAMC intervention, cost parameters and details on ART.
In the second model, we added potential confounding variables.
In the second model, we added hospital fixed effects to the baseline model (model 4).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com