Update PRESS_RELEASE.md

PRESS_RELEASE.md

@@ -8,7 +8,7 @@ Without interactive visualizations, exploring datasets through code is sometimes
 
 By contrast, users can easily make edits with Bifrost, letting them focus on extracting insights instead of coding repetitively. The extension is launched as a cell output, smoothly integrating it into the notebook. To help begin data analysis, Bifrost recommends charts based on user-specified columns from their dataset. This allows users to go from a thousand-column dataset to a useful visualization in a matter of seconds. It is also easy to filter and edit a visualization to narrow in on useful insights. After making edits, users can extract an updated pandas dataframe, exporting their insights back to code. All interactions are preserved in a history log, enabling iteration on previous versions of a visualization.
 
-> _“Jupyter Bifrost is a great way to jump into data exploration without writing a ton of code. While it's perfect for students and novices, we’ve also seen it speed up the workflows of seasoned developers. It’s so much easier to find compelling correlations and get into the flow of analysis.” —<b>Jupyter Developer<b>_
+> *“Jupyter Bifrost is a great way to jump into data exploration without writing a ton of code. While it's perfect for students and novices, we’ve also seen it speed up the workflows of seasoned developers. It’s so much easier to find compelling correlations and get into the flow of analysis.” —<b>Jupyter Developer</b>*
 
 To get started, import the Bifrost library and plot any Pandas dataframe to begin interacting with it. Our extension will translate all of your explorations in the graphic interface into Pandas data queries, allowing you to track and reproduce your experiments with large datasets. You can also apply the results of your analysis to the original Pandas DataFrame so that you can pick up where you left off in the code.