Merge pull request #21 from kyleniemeyer/patch-1

Update paper.md

paper/paper.md

@@ -22,20 +22,20 @@ bibliography: paper.bib
 # Summary
 
 Partial Wave Spectroscopic (PWS) Microscopy is a novel spectroscopic microscopy technique that quantitatively 
-detects the nanoscale mass density distribution within a biological sample. PWS microscopy achieves sensitivity to nanoscale structures within biological cells by using the spectroscopic content of microscope images, and quantitatively measures nanoarchitectural changes in cells associated with carcinogenesis[@Chandler:2016]. Additionally, PWS does not require exogenous labels and thus works even in unstained samples.
+detects the nanoscale mass density distribution within a biological sample. PWS microscopy achieves sensitivity to nanoscale structures within biological cells by using the spectroscopic content of microscope images, and quantitatively measures nanoarchitectural changes in cells associated with carcinogenesis [@Chandler:2016]. Additionally, PWS does not require exogenous labels and thus works even in unstained samples.
 
 `PWSpy` is a Python module dedicated to the analysis of PWS data. It includes a full suite of tools that prove useful in 
-analyzing experimental data. This includes object-oriented representations of the raw data involved as well as the analyzed output data and auxiliary data used such as ROIs, calibration files, and automated imaging metadata produced by the PWS Acquisition[@PWS_Acquisition] plugin for Micro-Manager[@Micro-Manager]. A single PWS measurement can be treated as a 3D image cube with dimensions (x, y, and wavenumber) and a single PWS experiment may consist of thousands of such images colocalized with widefield fluorescence, confocal microscopy, or other interferometric image cubes. With `PWSpy`, it is trivial to skip the basics of preprocessing and get to the heart of extracting meaningful results from your experimental data. Basic operations such as normalization, camera non-linearity compensation, and calibration can all be handled with the call of a single method so you can trust that they have been performed without error. Additionally, the library provides a means for conveniently loading and storing auxiliary data such as ROIs, notes, and analysis results. Utility functionality for generating visualizations, automatic colocalization, basic modeling of reflectance, loading an manipulation of Micro-Manager position list files, and more are provided in the `utility` subpackage.
+analyzing experimental data. This includes object-oriented representations of the raw data involved as well as the analyzed output data and auxiliary data used such as ROIs, calibration files, and automated imaging metadata produced by the PWS Acquisition [@PWS_Acquisition] plugin for Micro-Manager [@Micro-Manager]. A single PWS measurement can be treated as a 3D image cube with dimensions (x, y, and wavenumber) and a single PWS experiment may consist of thousands of such images colocalized with widefield fluorescence, confocal microscopy, or other interferometric image cubes. With `PWSpy`, it is trivial to skip the basics of preprocessing and get to the heart of extracting meaningful results from your experimental data. Basic operations such as normalization, camera non-linearity compensation, and calibration can all be handled with the call of a single method so you can trust that they have been performed without error. Additionally, the library provides a means for conveniently loading and storing auxiliary data such as ROIs, notes, and analysis results. Utility functionality for generating visualizations, automatic colocalization, basic modeling of reflectance, loading an manipulation of Micro-Manager position list files, and more are provided in the `utility` subpackage.
 
 
 # Statement of need
-Any analysis of raw data generated by PWS microscopy or other related interferometric imaging modalities[@Gladstein:2019] requires loading image data and metadata from a large variety of file formats and performing complex pre-processing steps before any of the real analysis begins. Up until now a great many single-use MATLAB scripts have been written to perform these tasks but a comprehensive and reusable library has never been developed.
+Any analysis of raw data generated by PWS microscopy or other related interferometric imaging modalities [@Gladstein:2019] requires loading image data and metadata from a large variety of file formats and performing complex pre-processing steps before any of the real analysis begins. Up until now a great many single-use MATLAB scripts have been written to perform these tasks but a comprehensive and reusable library has never been developed.
 Any minor variations in how this pre-processing or analysis is performed can result in major differences in final analysis results and any script that fully implements all of the required processing becomes so long as to be unreadable. `PWSpy` provides an object-oriented interface for performing all common file I/O, pre-processing, and analysis tasks related to PWS. 
 This allows users to write succinct and readable scripts/software that can be trusted to process data correctly. Automated unit testing helps ensure that as the code continues to be improved upon the analysis results will stay the same. 
 In order to guarantee that analysis results can always be repeated, results that are saved to file include metadata that fully describes the analysis settings, calibration files, and the Git revision SHA of `PWSpy` that were used in analysis.
 
-The data processing steps described in previous publications [@cherkezyan:2017], [@Gladstein:2019], [@cherkezyan:2013]
-can be found written out clearly in Python under the `analysis` package. This library provides the backend code for the user interfaces found in `PWSpy_GUI`[@PWSpy_GUI]. `PWSpy` was designed to be used by any researchers working with PWS data. It has already been used in one scientific publication [@Li:2021] with many more on the way. `PWSpy` takes care of all the low-level details of PWS analysis so that researchers can focus on the big picture.
+The data processing steps described in previous publications [@cherkezyan:2017; @Gladstein:2019; @cherkezyan:2013]
+can be found written out clearly in Python under the `analysis` package. This library provides the backend code for the user interfaces found in `PWSpy_GUI` [@PWSpy_GUI]. `PWSpy` was designed to be used by any researchers working with PWS data. It has already been used in one scientific publication [@Li:2021] with many more on the way. `PWSpy` takes care of all the low-level details of PWS analysis so that researchers can focus on the big picture.
 
 
 ![Example images produced by PWSpy](figs/gridcells.png)
@@ -103,7 +103,7 @@ experiment agrees well with prediction.](figs/er_grid.png)
 ### Parsing of acquisition engine metadata 
 While the core focus of `PWSpy` is on handling the details of accurately analyzing a single PWS microscopy image cube, any 
 real biology experiment will consist of hundreds or thousands of images spanning multiple imaging conditions and cell plates.
-To make the acquisition of this data easier the PWS Acquisition[@PWS_Acquisition] plugin for Micro-Manager provides flexible automated imaging 
+To make the acquisition of this data easier the PWS Acquisition [@PWS_Acquisition] plugin for Micro-Manager provides flexible automated imaging 
 presented to the user as a set of possible automation steps and a drag-and-drop tree which defines the order that steps
 will be executed in. Examples of possible types of steps that could be included in an automated experiment definition are:
 a time series of sub-steps, execution of substeps at a pre-defined set of XYZ coordinates, acquisition of a PWS image,
@@ -123,4 +123,4 @@ can be found at <https://pwspy.readthedocs.io>. `PWSpy` can be installed using P
 # Acknowledgements
 The authors would like to thank many of the early users of `PWSpy` for providing feedback and bug reports as the project has developed.
 
-# References
+# References