-->PCOMPBIOL-D-26-00134

PumpKin: A machine-learning pipeline for automatically tracking localized kinematics in freely moving organisms

PLOS Computational Biology

Dear Dr. Lu,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter

We look forward to receiving your revised manuscript.

Kind regards,

Thomas Zdyrski

Guest Editor

PLOS Computational Biology

Feng Fu

Section Editor

PLOS Computational Biology

Additional Editor Comments :

Dear Authors,

We recommend a major revision incorporating the reviewers' suggestions. Please send a revised copy after updating the manuscript.

Thank you kindly

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Reviewers' comments:

Reviewer's Responses to Questions

Reviewer #1: The manuscript presents a computational pipeline, named PumpKin, to track pumping rate in C. elegans. The work could be a potentially useful tool for C. elegans studies. However, I have the following comments regarding the manuscript:

1. The title, abstract, introduction, and discussion prominently claim generalizations of PumpKin to "freely moving organisms" broadly, yet the entire validation is performed on a single organism, a single behavior, and a single imaging modality. The claim of generalization is therefore speculative. I would recommend the authors either: (a) demonstrate PumpKin on at least one additional organism/behavior to substantiate the applicability to "freely moving organisms," or (b) revise their language throughout the manuscript.

2. The Butterworth filter cutoff frequency is derived from the expected pumping rate of the strain, meaning we must already have a reasonable estimate of the behavior we are trying to measure. The authors briefly acknowledge that the lawn-entry experiment uses separate parameters for pre- and post-lawn entry. However, it remains unclear how sensitive the results are to a different choice of cutoff frequency, and what happens in novel conditions where the expected rate is unknown. A parameter sensitivity analysis is important here and should be included.

3. Table 1 compares PumpKin against prior methods, but no quantitative comparison is made against any of them. PharaGlow in particular is directly relevant, as it also tracks pharyngeal pumping in freely moving worms. The claim that PumpKin represents an advance cannot be fully evaluated without benchmarking. If running PharaGlow on the same dataset is infeasible, this limitation should be explicitly stated and justified.

4. The reported R² values of 0.99 and 0.97 in Fig. 3B are not informative and arguably misleading given the structure of the underlying data. The condition-averaged pumping rates form two tight, well-separated clusters, with eat-2 conditions near 1–1.5 Hz and N2 conditions near 4–5 Hz. In this bimodal structure, a high R² is mathematically guaranteed by the large between-cluster separation (~3–4 Hz), which completely dominates the variance calculation regardless of how accurately PumpKin performs within each cluster.

5. Using only 5 worms per condition for the primary validation comparison limits the statistical power to detect differences between PumpKin and human annotators. This should be acknowledged as a limitation of the study.

6. The peak detector's sensitivity and specificity should be characterized, ideally as a function of the key tunable parameters. At present, no evaluation of how parameter choices affect false positive or false negative rates is provided.

Reviewer #2: This manuscript introduces PumpKin, a machine-learning pipeline designed to quantify localized, single-structure kinematics in freely moving animals. The authors combine sequential Faster R-CNN detection, motion compensation, sparse optical flow, Butterworth filtering, and biphasic peak detection to track C. elegans pharyngeal pumping without fixation or fluorescent labeling. Validation across eight experimental conditions (genotype, satiety, and food presence) demonstrates high agreement with expert manual annotations for both average pumping rate (R² ≥ 0.97) and instantaneous dynamics. The authors further use the pipeline to uncover a shared basal off-food pumping rate (~0.5 Hz) across strains and satiety states. While this work addresses a meaningful technical gap, I have several concerns,

1. The authors evaluate only the Faster R-CNN model, although other state-of-the-art detection architectures such as YOLO and Real-Time Detection Transformer (RT-DETR) are available. It would strengthen the manuscript to include comparisons with these alternative detectors and report their performance relative to the chosen baseline. Such benchmarking would help clarify whether the reported improvements are specific to Faster R-CNN or generalizable across detection frameworks.

2. Although the manuscript describes several tunable components (e.g., Butterworth cutoff frequency, peak distance threshold, and peak height threshold; Methods, pp. 14–15), the sensitivity of the method to these parameters remains unclear. Specifically: 1) How sensitive is performance to variations in these parameters? 2) Were the parameters fixed across all experimental conditions? 3) How much user intervention is required when adapting the pipeline to a new organism, behavior, or different imaging conditions?

3. The manuscript states that training frames were selected using DeepLabCut k-means clustering and that models were fine-tuned for 50 epochs (Methods, p. 13). However, important training details are missing, including: the number of annotated frames used per model, the train/validation/test split strategy, whether evaluation videos were fully held out from the training dataset.

4. In Figure 4, the number of manual human annotations appears limited. It is unclear why the uncertainty (grey shaded region) is very small in some cases but much larger in others. Since the model’s reported performance is evaluated relative to this uncertainty band, the variability across conditions requires further explanation. Additionally, in panel D, there appears to be a grey uncertainty region despite only one manual estimate being shown. The authors should clarify how this uncertainty was computed in that case.

5. The introduction appropriately discusses the limitations of single-keypoint pose tracking due to drift. However, the manuscript would be strengthened by including direct benchmarking against pose estimation tools such as DeepLabCut or SLEAP trained specifically for grinder localization. The authors should make more quantitative comparisons with peer models as listed in Table 1 to deminstrate its advances.

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Reviewer #1: None

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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PCOMPBIOL-D-26-00134R1

PumpKin: A machine-learning pipeline for automatically tracking localized kinematics in freely moving C. elegans

PLOS Computational Biology

Dear Dr. Lu,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Aug 15 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at ploscompbiol@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pcompbiol/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

* A letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. This file does not need to include responses to formatting updates and technical items listed in the 'Journal Requirements' section below.

* A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

* An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

As the corresponding author, your ORCID iD is verified in the submission system and will appear in the published article. PLOS supports the use of ORCID, and we encourage all coauthors to register for an ORCID iD and use it as well. Please encourage your coauthors to verify their ORCID iD within the submission system before final acceptance, as unverified ORCID iDs will not appear in the published article. Only the individual author can complete the verification step; PLOS staff cannot verify ORCID iDs on behalf of authors.

We look forward to receiving your revised manuscript.

Kind regards,

Thomas Zdyrski

Guest Editor

PLOS Computational Biology

Feng Fu

Section Editor

PLOS Computational Biology

Additional Editor Comments (if provided):

Thank you for implementing the changes recommended by the reviewers. I have a few, minor recommendations:

1. Given the matching color scheme between supplemental figures 4 and 5, I assume figure 5's subfigure columns correspond to cutoff frequency, minimum valid peak height, and maximum valid peak height, respectively. If that is correct, would you please add that information to either the figure or caption of supplemental figure 5? If not, please clarify what the three columns represent.

2. On line 106, there is an upside-down exclamation mark, which should presumably be a less than or less-than-or-equals sign

3. In figure 5's caption, I believe the phrase "(B) the average run length" should be "(B) the average bout length" to match the figure's axis label

4. Please ensure that the supplemental file is submitted without highlighted changes, as it will not be copy-edited

Thank you

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2) Please amend your detailed Financial Disclosure statement. This is published with the article. It must therefore be completed in full sentences and contain the exact wording you wish to be published.

1) State what role the funders took in the study. If the funders had no role in your study, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

2) If any authors received a salary from any of your funders, please state which authors and which funders..

Reviewers' comments:

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Figure resubmission:

-->While revising your submission, we strongly recommend that you use PLOS’s NAAS tool (https://ngplosjournals.pagemajik.ai/artanalysis) to test your figure files. NAAS can convert your figure files to the TIFF file type and meet basic requirements (such as print size, resolution), or provide you with a report on issues that do not meet our requirements and that NAAS cannot fix.-->-->

After uploading your figures to PLOS’s NAAS tool - https://ngplosjournals.pagemajik.ai/artanalysis, NAAS will process the files provided and display the results in the "Uploaded Files" section of the page as the processing is complete. If the uploaded figures meet our requirements (or NAAS is able to fix the files to meet our requirements), the figure will be marked as "fixed" above. If NAAS is unable to fix the files, a red "failed" label will appear above. When NAAS has confirmed that the figure files meet our requirements, please download the file via the download option, and include these NAAS processed figure files when submitting your revised manuscript.-->

Reproducibility:

To enhance the reproducibility of your results, we recommend that authors of applicable studies deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Dear Dr. Lu,

We are pleased to inform you that your manuscript 'PumpKin: A machine-learning pipeline for automatically tracking localized kinematics in freely moving C. elegans' has been provisionally accepted for publication in PLOS Computational Biology.

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Best regards,

Thomas Zdyrski

Guest Editor

PLOS Computational Biology

Feng Fu

Section Editor

PLOS Computational Biology

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