The software for interactive evaluation of mass spectrometric imaging heterogeneity

Evgeny Zhvansky; Ekaterina Zhdanova; Maxim Belenikin; Maria Shamraeva; Sergei Silkin; Konstantin Bocharov; Anatoly A. Sorokin

doi:10.12688/f1000research.76828.1

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Software Tool Article

The software for interactive evaluation of mass spectrometric imaging heterogeneity

[version 1; peer review: 1 approved with reservations]

Evgeny Zhvansky ¹, Ekaterina Zhdanova¹, Maxim Belenikin¹, [...] Maria Shamraeva¹, Sergei Silkin¹, Konstantin Bocharov^1,2, Anatoly A. Sorokin^1,3

Evgeny Zhvansky ¹, Ekaterina Zhdanova¹, [...] Maxim Belenikin¹, Maria Shamraeva¹, Sergei Silkin¹, Konstantin Bocharov^1,2, Anatoly A. Sorokin^1,3

PUBLISHED 25 Jan 2022

Author details Author details

¹ Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation
² Semenov Federal Center of Chemical Physics, Moscow, Russian Federation
³ Department of Biochemistry and Systems Biology, University of Liverpool, Liverpool, UK

Evgeny Zhvansky
Roles: Conceptualization, Formal Analysis, Methodology, Software, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Ekaterina Zhdanova
Roles: Investigation, Resources, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Maxim Belenikin
Roles: Data Curation, Investigation, Resources, Validation

Maria Shamraeva
Roles: Data Curation, Writing – Original Draft Preparation

Sergei Silkin
Roles: Data Curation

Konstantin Bocharov
Roles: Data Curation, Writing – Review & Editing

Anatoly A. Sorokin
Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Supervision, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Bioinformatics gateway.

Abstract

Mass spectrometry imaging is a promising tool complement to the histology study for evaluation of presence of different tissue types in the sample. To make this method faster, more accurate and precise we have presented earlier the cosine similarity measure maps (CSMM). The method provides the spatial distribution of cosine similarity measure metrics between chosen MSI pixel and the rest of the image. In cases when samples under test are heterogeneous and not guaranteed to have clear clusters with distinct borders, it is interesting to analyze the heterogeneity, area borders and their sensitivity to reference CSMM pixel selection. Here we present the software for interactive building of CSMM for different parameters, their visual analysis and saving such CSMM in publication-ready quality without additional programming. Source code, example datasets, binaries, and other information are available at https://github.com/EvgenyZhvansky/Interactive_CSMM.

Keywords

mass spectrometric imaging, interactive analysis, clustering, labeling

Corresponding author: Evgeny Zhvansky

Competing interests: No competing interests were disclosed.

Grant information: The research was supported by the Ministry of Science and Higher Education of the Russian Federation (agreement # 075-00337-20-02, project # 0714-2020-0006).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2022 Zhvansky E et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Zhvansky E, Zhdanova E, Belenikin M et al. The software for interactive evaluation of mass spectrometric imaging heterogeneity [version 1; peer review: 1 approved with reservations]. F1000Research 2022, 11:92 (https://doi.org/10.12688/f1000research.76828.1) First published: 25 Jan 2022, 11:92 (https://doi.org/10.12688/f1000research.76828.1) Latest published: 25 Jan 2022, 11:92 (https://doi.org/10.12688/f1000research.76828.1)

Introduction

Mass spectrometry imaging (MSI) is a technique of building a map of the spatial distribution of molecular features across the tissue of interest without pretreatment.¹^–⁴ This technique is a promising complement to the gold standard of tissue analysis – histology study that is a time-consuming, labor-intensive, and sometimes subjective method.

Each pixel of raw MSI-map is a mass spectrum of the corresponding location in the sample. This multidimensional map is almost impossible to interpret by naked eyes and has to be converted to a simpler representation for better visualization and usability.⁵^–¹²

In our previous work, we introduced¹³ a fast, precise, and accurate imaging tool based on the interactive building of the cosine similarity measure maps (CSMM) between the reference pixel and the rest of the image. Introduced technique well suited for visual estimation of presence, location, and level of heterogeneity of homogeneous regions in the image. It also allows extraction of the region reference mass spectra, and evaluation of the influence of the reference pixels on the distribution of similarity characteristics on the map.

Here we present a user-friendly interface for building and analysis of CSMMs of MSI data.

Methods

Implementation

Here we introduce Interactive CSMM, a MATLAB app, which provides an intuitive graphical interface for interactive evaluation of mass spectrometric imaging heterogeneity. An example of the interface is shown in Figure 1. Interactive CSMM was created in MATLAB R2019b. We also introduce the Python script for converting standard raw imzML file format to mat-file, which is required by the Interactive CSMM. The script currently depends on the following libraries: numpy, psutil, pyimzML and scipy.

Figure 1. On the left panel, you can see the input fields for setting the parameters.

On the right panel on the top left CSMM is located, on the top right the result of CSMM median smoothing is displayed, on the bottom left is a map of the boundaries of homogeneous regions, and the mass spectrum of the reference pixel is shown on the bottom right.

Operation

The Interactive CSMM can be launched locally from any computer with MATLAB (R2019b or higher; lower versions also might work properly) installed. Installation and launching instructions are also available. All interfaces and plots of Interactive CSMM are highly interactive, allowing users to visualize data in real-time with interactive selection reference mass spectrum, as well as store the results of the analysis.

Use cases

The program 1) allows the user to interactively select the reference pixel, specify the mass range and other parameters for data binning 2) calculates the CSMM of all imaging data with respect to the selected pixel 3) label homogeneous areas and save the assigned area number, coordinates of the reference pixel and the name of the CSMM image file built on this pixel to a text file 4) save publication-ready images of CSMM with specified resolution.

To improve the interpretability of the image and clean up the pixelation, outliers, and measurement artifacts the program provides the smoothed version of CSMM, the visual map of the boundaries of homogeneous zones,¹³ and the mass spectrum of the reference pixel.

The presence in the spectra ions distributed over the tissue equally could cause blurring of the picture. So, we provide users with options to specify ranges is m/z which are reflecting the tissue heterogeneity, and compare positions and shape of homogeneity regions obtained in different m/z ranges.

Another effect that complicates the interpretation of MS images is the presence of transition zones due to gradual changes in the ion’s intensity within such zones. In our method, due to the building of the CSMM over the mass range, the transition zones become clearly visible because they include the peaks of both boundary zones. That effect is more difficult to achieve with standard imaging approaches (Supplementary Materials of the method describing article¹³), which consider the distribution of individual ions.

The additional benefit of our method is that there is no data preprocessing is required other than binning. The parameters of binding can be changed online. No alignment is required as well, it can be replaced with a larger binning. It could be also shown that the method works well with non-normalized data and there is no need for baseline correction.

CSMM allows you to define zones of least and greatest similarity. It does not automatically divide the measured map into zones. But you can find areas with similar spectra by varying the reference pixel. By changing the other parameters (to a greater extent, the mass range), it is possible to optimize the CSMM color map and improve the visualization of heterogeneity of the measured sample. The smooth changes in heterogeneity can be observed as smooth color changes on the CSMM.

We tested this method on different data sources¹⁴ (measured with different ion sources: MALDI-imaging, DESI-imaging; and different mass analyzers TOF MS, Orbitrap, ICR MS) (Supplementary Materials of the method describing article¹³). Our article presents CSMMs for colorectal adenocarcinoma data.

Preprocessing steps and descriptions of operations are presented in the manual.

Conclusion

We presented software that allows users to quickly evaluate the presence and structure of heterogeneous areas in the sample, and manually make a dataset of feature spectrum for the homogeneous zones. By varying the reference spectrum and the mass range used in the construction of the CSMM, it is possible to understand which mass ranges most reflect the heterogeneity and make the boundaries between the zones more contrasting. A more detailed discussion is presented in the method describing article.¹³

Data availability

GigaDB: Supporting materials for “Benchmark datasets for 3D MALDI- and DESI-Imaging Mass Spectrometry”, http://dx.doi.org/10.5524/100131.¹⁴

This project contains the following underlying data:

- 3DMouseKidney.ibd
- 3DMouseKidney.imzML
- 3D_Mouse_Pancreas.ibd
- 3D_Mouse_Pancreas.imzML
- 3D_OSCC.ibd
- 3D_OSCC.imzML
- ColAd_Individual.zip
- Colorectal_Adenocarcinoma.h5
- Microbe_Interaction_3D_Timecourse_LP.ibd
- Microbe_Interaction_3D_Timecourse_LP.imzML

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).

Data are also available at Metabolights: MTBLS176: Benchmark datasets for 3D MALDI- and DESI-Imaging Mass Spectrometry.

Software availability

Source code available from: https://github.com/EvgenyZhvansky/Interactive_CSMM/.

Archived source code at time of publication: http://doi.org/10.5281/zenodo.5776541.

License: Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Author contributions

Conceptualization A.S., E.S.Z.; methodology A.S., E.S.Z.; software E.S.Z.; investigation M.B., E.V.Z; writing — original draft preparation E.S.Z., E.V.Z., M.S.; writing — review and editing E.S.Z., E.V.Z., A.S., K.B.; visualization E.S.Z.; supervision A.S.; project administration A.S.; funding acquisition A.S.; formal Analysis E.S.Z.; data curation M.B., M.S., S.S., K.B.; validation E.V.Z., M.B.; resources M.B., E.V.Z. All authors have read and agreed to the current version of the manuscript.

Acknowledgments

The research used the equipment of Shared Research Facilities of N.N. Semenov Federal Research Center for Chemical Physics of the Russian Academy of Sciences.

References

1. McDonnell LA, Römpp A, Balluff B, et al.: Discussion point: reporting guidelines for mass spectrometry imaging. Anal. Bioanal. Chem. 2015 Mar; 407(8): 2035–2045. PubMed Abstract | Publisher Full Text
2. Horatz K, Giampà M, Karpov Y, et al.: Conjugated Polymers as a New Class of Dual-Mode Matrices for MALDI Mass Spectrometry and Imaging. J. Am. Chem. Soc. 2018 Sep 12; 140(36): 11416–11423. PubMed Abstract | Publisher Full Text
3. Buchberger AR, DeLaney K, Johnson J, et al.: Mass spectrometry imaging: A review of emerging advancements and future insights. Anal. Chem. 2018 Jan 2; 90(1): 240–265. PubMed Abstract | Publisher Full Text
4. Hanrieder J, Gerber L, Persson Sandelius Å, et al.: High resolution metabolite imaging in the hippocampus following neonatal exposure to the environmental toxin BMAA using ToF-SIMS. ACS Chem. Neurosci. 2014 Jul 16; 5(7): 568–575. PubMed Abstract | Publisher Full Text
5. Wüllems K, Kölling J, Bednarz H, et al.: Detection and visualization of communities in mass spectrometry imaging data. BMC Bioinform. 2019 Jun 4; 20(1): 303. PubMed Abstract | Publisher Full Text
6. Kaddi CD, Parry RM, Wang MD: Multivariate hypergeometric similarity measure. IEEE/ACM Trans. Comput. Biol. Bioinform. 2013 Dec; 10(6): 1505–1516. PubMed Abstract | Publisher Full Text
7. Alexandrov T, Kobarg JH: Efficient spatial segmentation of large imaging mass spectrometry datasets with spatially aware clustering. Bioinformatics. 2011 Jul 1; 27(13): i230–i238. PubMed Abstract | Publisher Full Text
8. Wüllems K, Zurowietz A, Zurowietz M, et al.: Fast visual exploration of mass spectrometry images with interactive dynamic spectral similarity pseudocoloring. Sci. Rep. 2021 Feb 25; 11(1): 4606. PubMed Abstract | Publisher Full Text
9. Race AM, Palmer AD, Dexter A, et al.: Spectralanalysis: software for the masses. Anal. Chem. 2016 Oct 4; 88(19): 9451–9458. PubMed Abstract | Publisher Full Text
10. Bokhart MT, Nazari M, Garrard KP, et al.: MSiReader v1.0: Evolving Open-Source Mass Spectrometry Imaging Software for Targeted and Untargeted Analyses. J. Am. Soc. Mass Spectrom. 2018 Jan; 29(1): 8–16. PubMed Abstract | Publisher Full Text
11. Klinkert I, Chughtai K, Ellis SR, et al.: Methods for full resolution data exploration and visualization for large 2D and 3D mass spectrometry imaging datasets. Int. J. Mass Spectrom. 2014 Apr; 362: 40–47. Publisher Full Text
12. Ràfols P, Torres S, Ramírez N, et al.: rMSI: an R package for MS imaging data handling and visualization. Bioinformatics. 2017 Aug 1; 33(15): 2427–2428. PubMed Abstract | Publisher Full Text
13. Zhvansky ES, Ivanov DG, Sorokin AA, et al.: Interactive Estimation of Heterogeneity from Mass Spectrometry Imaging. Anal. Chem. 2021 Mar 2; 93(8): 3706–3709. PubMed Abstract | Publisher Full Text
14. Oetjen J, Veselkov K, Watrous J, et al.: Supporting materials for “Benchmark datasets for 3D MALDI- and DESI-Imaging Mass Spectrometry.”. GigaScience Database. 2015; 4: 20. PubMed Abstract | Publisher Full Text

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Version 1

VERSION 1 PUBLISHED 25 Jan 2022

Author details Author details

¹ Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation
² Semenov Federal Center of Chemical Physics, Moscow, Russian Federation
³ Department of Biochemistry and Systems Biology, University of Liverpool, Liverpool, UK

Evgeny Zhvansky
Roles: Conceptualization, Formal Analysis, Methodology, Software, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Ekaterina Zhdanova
Roles: Investigation, Resources, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Maxim Belenikin
Roles: Data Curation, Investigation, Resources, Validation

Maria Shamraeva
Roles: Data Curation, Writing – Original Draft Preparation

Sergei Silkin
Roles: Data Curation

Konstantin Bocharov
Roles: Data Curation, Writing – Review & Editing

Anatoly A. Sorokin
Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Supervision, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The research was supported by the Ministry of Science and Higher Education of the Russian Federation (agreement # 075-00337-20-02, project # 0714-2020-0006).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Article Versions (1)

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Published: 25 Jan 2022, 11:92

https://doi.org/10.12688/f1000research.76828.1

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© 2022 Zhvansky E et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Zhvansky E, Zhdanova E, Belenikin M et al. The software for interactive evaluation of mass spectrometric imaging heterogeneity [version 1; peer review: 1 approved with reservations]. F1000Research 2022, 11:92 (https://doi.org/10.12688/f1000research.76828.1)

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Reviewer Report 10 Feb 2023

Jean-Nicolas Audinot, Material Research and Technology Department, Luxembourg Institute of Science and Technology, Rue du Brill, Belvaux, Luxembourg

Approved with Reservations

https://doi.org/10.5256/f1000research.80808.r160344

This publication is about the description of a software that allows users to evaluate the presence and structure of heterogeneous areas in the (biological) sample. By varying the reference spectrum and the mass range used, the software allow the construction ... Continue reading

This publication is about the description of a software that allows users to evaluate the presence and structure of heterogeneous areas in the (biological) sample. By varying the reference spectrum and the mass range used, the software allow the construction of the the cosine similarity measure maps (CSMM). The objective of this work (data treatment) is to understand which mass ranges most reflect the heterogeneity. This work was already presented in a previous publication¹.

Point 1: The originality with the previous publication¹ is not obvious. I found more information in the previous publication, like the definition of the CSMM. Maybe wrongly, due to lack of time, as I couldn't test all the (new) option, I didn't see any difference with the previous version

Point 2: The expected data is not very clear. the manual provided with the program is really poor. It should at least be enriched with many examples, and especially with processed data. It's a pity, we'll have to wait for publications with this software to really know its potential

Is the rationale for developing the new software tool clearly explained?

Partly
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Partly
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Yes

References

1. Zhvansky ES, Ivanov DG, Sorokin AA, Bugrova AE, et al.: Interactive Estimation of Heterogeneity from Mass Spectrometry Imaging.Anal Chem. 2021; 93 (8): 3706-3709 PubMed Abstract | Publisher Full Text

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Mass Spectrometry Imaging

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Jean-Nicolas Audinot, Luxembourg Institute of Science and Technology, Rue du Brill, Luxembourg

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10 Feb 2023 | for Version 1

Jean-Nicolas Audinot, Material Research and Technology Department, Luxembourg Institute of Science and Technology, Rue du Brill, Belvaux, Luxembourg

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Approved With Reservations

This publication is about the description of a software that allows users to evaluate the presence and structure of heterogeneous areas in the (biological) sample. By varying the reference spectrum and the mass range used, the software allow the construction of the the cosine similarity measure maps (CSMM). The objective of this work (data treatment) is to understand which mass ranges most reflect the heterogeneity. This work was already presented in a previous publication¹.

Point 1: The originality with the previous publication¹ is not obvious. I found more information in the previous publication, like the definition of the CSMM. Maybe wrongly, due to lack of time, as I couldn't test all the (new) option, I didn't see any difference with the previous version

Point 2: The expected data is not very clear. the manual provided with the program is really poor. It should at least be enriched with many examples, and especially with processed data. It's a pity, we'll have to wait for publications with this software to really know its potential

Is the rationale for developing the new software tool clearly explained?

Partly
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Partly
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Yes

References

1. Zhvansky ES, Ivanov DG, Sorokin AA, Bugrova AE, et al.: Interactive Estimation of Heterogeneity from Mass Spectrometry Imaging.Anal Chem. 2021; 93 (8): 3706-3709 PubMed Abstract | Publisher Full Text

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Mass Spectrometry Imaging

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Respond to this report

Responses (0)

[1] 1. McDonnell LA, Römpp A, Balluff B, et al.: Discussion point: reporting guidelines for mass spectrometry imaging. Anal. Bioanal. Chem. 2015 Mar; 407(8): 2035–2045. PubMed Abstract | Publisher Full Text

[2] 2. Horatz K, Giampà M, Karpov Y, et al.: Conjugated Polymers as a New Class of Dual-Mode Matrices for MALDI Mass Spectrometry and Imaging. J. Am. Chem. Soc. 2018 Sep 12; 140(36): 11416–11423. PubMed Abstract | Publisher Full Text

[3] 3. Buchberger AR, DeLaney K, Johnson J, et al.: Mass spectrometry imaging: A review of emerging advancements and future insights. Anal. Chem. 2018 Jan 2; 90(1): 240–265. PubMed Abstract | Publisher Full Text

[4] 4. Hanrieder J, Gerber L, Persson Sandelius Å, et al.: High resolution metabolite imaging in the hippocampus following neonatal exposure to the environmental toxin BMAA using ToF-SIMS. ACS Chem. Neurosci. 2014 Jul 16; 5(7): 568–575. PubMed Abstract | Publisher Full Text

[5] 5. Wüllems K, Kölling J, Bednarz H, et al.: Detection and visualization of communities in mass spectrometry imaging data. BMC Bioinform. 2019 Jun 4; 20(1): 303. PubMed Abstract | Publisher Full Text

[6] 6. Kaddi CD, Parry RM, Wang MD: Multivariate hypergeometric similarity measure. IEEE/ACM Trans. Comput. Biol. Bioinform. 2013 Dec; 10(6): 1505–1516. PubMed Abstract | Publisher Full Text

[7] 7. Alexandrov T, Kobarg JH: Efficient spatial segmentation of large imaging mass spectrometry datasets with spatially aware clustering. Bioinformatics. 2011 Jul 1; 27(13): i230–i238. PubMed Abstract | Publisher Full Text

[8] 8. Wüllems K, Zurowietz A, Zurowietz M, et al.: Fast visual exploration of mass spectrometry images with interactive dynamic spectral similarity pseudocoloring. Sci. Rep. 2021 Feb 25; 11(1): 4606. PubMed Abstract | Publisher Full Text

[9] 9. Race AM, Palmer AD, Dexter A, et al.: Spectralanalysis: software for the masses. Anal. Chem. 2016 Oct 4; 88(19): 9451–9458. PubMed Abstract | Publisher Full Text

[10] 10. Bokhart MT, Nazari M, Garrard KP, et al.: MSiReader v1.0: Evolving Open-Source Mass Spectrometry Imaging Software for Targeted and Untargeted Analyses. J. Am. Soc. Mass Spectrom. 2018 Jan; 29(1): 8–16. PubMed Abstract | Publisher Full Text

[11] 11. Klinkert I, Chughtai K, Ellis SR, et al.: Methods for full resolution data exploration and visualization for large 2D and 3D mass spectrometry imaging datasets. Int. J. Mass Spectrom. 2014 Apr; 362: 40–47. Publisher Full Text

[12] 12. Ràfols P, Torres S, Ramírez N, et al.: rMSI: an R package for MS imaging data handling and visualization. Bioinformatics. 2017 Aug 1; 33(15): 2427–2428. PubMed Abstract | Publisher Full Text

[13] 13. Zhvansky ES, Ivanov DG, Sorokin AA, et al.: Interactive Estimation of Heterogeneity from Mass Spectrometry Imaging. Anal. Chem. 2021 Mar 2; 93(8): 3706–3709. PubMed Abstract | Publisher Full Text

[14] 14. Oetjen J, Veselkov K, Watrous J, et al.: Supporting materials for “Benchmark datasets for 3D MALDI- and DESI-Imaging Mass Spectrometry.”. GigaScience Database. 2015; 4: 20. PubMed Abstract | Publisher Full Text

The software for interactive evaluation of mass spectrometric imaging heterogeneity

Abstract

Keywords

Introduction

Methods

Implementation

Figure 1. On the left panel, you can see the input fields for setting the parameters.

Operation

Use cases

Conclusion

Data availability

Software availability

Author contributions

Acknowledgments

References

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