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This hierarchical estimation method first constructs estimation structures by analyzing the dimensional characteristics and geometric relationships of the anthropometric variables and then develops regression models based on the estimation structures.
Briefly, this method first constructs pools of uncloned, adaptor-ligated genomic DNA fragments.
Our method first constructs a high-quality, diverse ensemble of structures respresentative of the crystallographic data.
The median-joining method first constructs the minimum spanning networks before adding a few consensus sequences that function as median vectors in order to arrive at the most parsimonious networks [ 48].
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We propose S uperT riplets, a method that first constructs a supertree using a polynomial agglomerative scheme, and, secondly, performs fast local search to find the asymmetric median supertree according to a triplet dissimilarity.
In this method, we first construct a center line from a set of predefined waypoints, which are usually obtained from a lane-level map.
In this method, we first construct a hypothetical potential field of all the data points, and show that this potential field is closely related to nonparametric estimation of the global probability density function of the data points.
To illustrate the method, we first construct a weighted graph in the composition space.
We used this method to first construct a weighted network based on the pairwise correlations among all transcripts considered, using soft thresholding with parameter values chosen to produce approximately a scale-free topology.
The aim of this work was to use these new methods first to construct a typology of QoL change patterns (CP) in the context of a phase III therapeutic trial in patients with locally-advanced and metastatic gastrointestinal cancer, and secondly to describe identified patterns using the variables collected.
By applying the method first in order to gain construct validity, it would be possible to assess and understand the construct's relationships with other constructs and outcomes more accurately.
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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