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@@ -41,7 +41,7 @@ Nevertheless, overall per age influenza burdens varied per season.
 Seasonal age variability was shown in \cite{greenMortalityAttributableInfluenza2013}, where an age shift in Wales and England seasonal influenza burden was observed following the 2009 swine flue pandemic.
 It is also estimated that globally 291.243–645.832 influenza associated seasonal deaths occur annually \citep{iulianoEstimatesGlobalSeasonal2018}.
 These varying demographic statistics and the volume of influenza patients can confound decision making on national and international public health policies.
-Rapid knowledge extraction of vaccine efficacy data from clinical datasets and implementation of that knowledge can be a valuable asset for fighting future seasonal influenza outbreaks.
+Rapid knowledge extraction of vaccine efficacy from clinical datasets and implementation of that knowledge can be a valuable asset for fighting future seasonal influenza outbreaks.
 
 \subsection{Vaccine success criteria}
 
@@ -66,7 +66,7 @@ These pathways are important for viral entry, replication, and propagation, and
 The activation of these pathways is commonly meditated by the phosphorylation and dephosphorylation of several proteins, including \gls{bu:stat}.
 One example is the JAK-STAT signaling pathway in \gls{bu:bcell}s, where a large set of \gls{bu:bcell} receptors is known to bind \gls{bu:cytokine}s produced by \gls{bu:cd4pos}s and this results in downstream biological processes that make the immune response to a vaccination \citep{Papin_2004}.
 Further, these pathways are found in all \gls{bu:pbmc} and control a great amount of biological programs  \citep{Cantrell_2015}.
-In general, the phosphorylation pattern of these pathways in \gls{bu:pbmc}s are used in clinical studies as a measure of cell activation in response to \gls{bu:cytokine} stimulation  \citep{Toapanta_2012,tomicFluPRINTDatasetMultidimensional2019}.
+In general, the phosphorylation pattern of these pathways in \gls{bu:pbmc}s are used in clinical studies as a measure of cell activation in response to \gls{bu:cytokine} stimulation or absence thereof \citep{Toapanta_2012,tomicFluPRINTDatasetMultidimensional2019}.
 
 Machine learning has been applied to clinical datasets to find influenza protection markers, such as the described \gls{bu:tcell} populations, \gls{bu:titer}s of \gls{bu:pbmc}s and related molecules, or \gls{bu:cytokine} signalling related activity \citep{furmanApoptosisOtherImmune2013, sobolevAdjuvantedInfluenzaH1N1Vaccination2016, tsangGlobalAnalysesHuman2014}.
 However, these studies suffer from multiple issues, such as: inconsistencies between findings depending on the epidemic season, only focussing on one type of biological assay to get data, and a low amount of donors/samples.