diff --git a/code/plot_chirps_in_chasing.py b/code/plot_chirps_in_chasing.py
index 0649d83..ee43196 100644
--- a/code/plot_chirps_in_chasing.py
+++ b/code/plot_chirps_in_chasing.py
@@ -72,7 +72,7 @@ def main(datapath: str):
     bplot1 = ax.boxplot([time_precents, chirps_percents],
                         showfliers=False, patch_artist=True)
     ps.set_boxplot_color(bplot1, ps.gray)
-    ax.set_xticklabels(['Time \nChasing', 'Chirps \nin Chasing'])
+    ax.set_xticklabels(['Time \nchasing', 'Chirps \nin chasing'])
     ax.set_ylabel('Percent')
     ax.scatter(np.ones(len(time_precents))*scatter_time, time_precents,
                facecolor=ps.white, s=size)
diff --git a/poster/main.tex b/poster/main.tex
index 52644c9..3cf718a 100644
--- a/poster/main.tex
+++ b/poster/main.tex
@@ -18,18 +18,18 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
 \column{0.4}
 \myblock[TranspBlock]{Introduction}{
     \textbf{Chirps} are the most common communication signals in weakly electric fish. They are characterized by \textbf{short frequency excursions} and are emitted during various social contexts. It is nearly impossible to reliably \textbf{detect and assign} chirps in freely interacting fish using only a Fourier transform. To overcome these limits, we developed a new method of \textbf{dynamic feature extraction} and classification.
-    \vspace{0cm}
+    \vspace{1cm}
     \begin{tikzfigure}[]
         \label{griddrawing}
         \includegraphics[width=\linewidth]{figs/introplot}
     \end{tikzfigure}
 }
-\myblock[TranspBlock]{Chirp Detection Algorithm}{
+\myblock[TranspBlock]{Chirp detection algorithm}{
     \begin{tikzfigure}[]
         \label{fig:alg1}
         \includegraphics[width=0.9\linewidth]{figs/algorithm1}
     \end{tikzfigure}
-    \vspace{0cm}
+    \vspace{1cm}
     \begin{tikzfigure}[]
         \label{fig:alg2}
         \includegraphics[width=1\linewidth]{figs/algorithm}