FIX silly char issues

This commit is contained in:
Nathan Dwarshuis 2021-07-29 12:50:59 -04:00
parent 6d9084fe92
commit 0c98ecce44
1 changed files with 10 additions and 10 deletions

View File

@ -1948,12 +1948,12 @@ provide these benefits.
The first DOE resulted in a randomized 18-run I-optimal custom design where each
DMS parameter was evaluated at three levels: IL2 concentration (10, 20, and 30
U/μL), DMS concentration (500, 1500, 2500 carrier/μL), and functionalized
U/uL), DMS concentration (500, 1500, 2500 carrier/uL), and functionalized
antibody percent (60\%, 80\%, 100\%). These 18 runs consisted of 14 unique
parameter combinations where 4 of them were replicated twice to assess
prediction error. Process parameters for the ADOE were evaluated at multiple
levels: IL2 concentration (30, 35, and 40 U/μL), DMS concentration (500, 1000,
1500, 2000, 2500, 3000, 3500 carrier/μL), and functionalized antibody percent
levels: IL2 concentration (30, 35, and 40 U/uL), DMS concentration (500, 1000,
1500, 2000, 2500, 3000, 3500 carrier/uL), and functionalized antibody percent
(100\%) as depicted in Fig.1b. To further optimize the initial region explored
(DOE) in terms of total live CD4+ TN+TCM cells, a sequential adaptive
design-of-experiment (ADOE) was designed with 10 unique parameter combinations,
@ -1998,12 +1998,12 @@ Cytokines were quantified via Luminex as described in
\subsection{NMR metabolomics}
Prior to analysis, samples were centrifuged at \SI{2990}{\gforce} for
\SI{20}{\minute} at \SI{4}{\degreeCelsius} to clear any debris. 5 μL of 100/3 mM
\SI{20}{\minute} at \SI{4}{\degreeCelsius} to clear any debris. 5 uL of 100/3 mM
DSS-D6 in deuterium oxide (Cambridge Isotope Laboratories) were added to 1.7 mm
NMR tubes (Bruker BioSpin), followed by 45 μL of media from each sample that was
added and mixed, for a final volume of 50 μL in each tube. Samples were prepared
NMR tubes (Bruker BioSpin), followed by 45 uL of media from each sample that was
added and mixed, for a final volume of 50 uL in each tube. Samples were prepared
on ice and in predetermined, randomized order. The remaining volume from each
sample in the rack (4 μL) was combined to create an internal pool. This
sample in the rack (approx. 4 uL) was combined to create an internal pool. This
material was used for internal controls within each rack as well as metabolite
annotation.
@ -2017,8 +2017,8 @@ annotation.
One-dimensional spectra were manually phased and baseline corrected in TopSpin.
Two-dimensional spectra were processed in NMRpipe37. One dimensional spectra
were referenced, water/end regions removed, and normalized with the PQN
algorithm38 using an in-house MATLAB (The MathWorks, Inc.) toolbox
(https://github.com/artedison/Edison_Lab_Shared_Metabolomics_UGA).
algorithm38 using an in-house MATLAB (The MathWorks, Inc.) toolbox.
% (https://github.com/artedison/Edison_Lab_Shared_Metabolomics_UGA).
To reduce the total number of spectral features from approximately 250 peaks and
enrich for those that would be most useful for statistical modeling, a
@ -2158,7 +2158,7 @@ determined for RF, GBM, CIF, LASSO, PLSR, and SVM, respectively. Using these
scores, key predictive variables were selected if their importance scores were
within the 80th percentile ranking for the following ML methods: RF, GBM, CIF,
LASSO, PLSR, SVM while for SR variables present in >30\% of the top-performing
SR models from DataModeler (R2≥ 90\%, Complexity ≥ 100) were chosen to
SR models from DataModeler (R2>= 90\%, Complexity >= 100) were chosen to
investigate consensus except for NMR media models at day 4 which considered a
combination of the top-performing results of models excluding lactate ppms, and
included those variables which were in > 40\% of the best performing models.