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