diff --git a/tex/thesis.tex b/tex/thesis.tex
index 7062876..7eceb7a 100644
--- a/tex/thesis.tex
+++ b/tex/thesis.tex
@@ -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.