{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T18:04:08Z","timestamp":1784916248056,"version":"3.55.0"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1014489","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000}}],"reference-count":42,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DGE-2039655"],"award-info":[{"award-number":["DGE-2039655"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1648035"],"award-info":[{"award-number":["1648035"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1764406"],"award-info":[{"award-number":["1764406"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["NIH R01AG082039"],"award-info":[{"award-number":["NIH R01AG082039"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    One of the many goals of neuroscience is to understand how the brain encodes and transforms sensory information into behavior. These animal behaviors can be studied at the level of multi-limb poses or through the focused analysis of individual body parts. Techniques for tracking animal pose, such as DeepLabCut and SLEAP, enable detailed studies of large-scale multi-limb behaviors but show reduced accuracy when used for single-keypoint tracking, where insufficient spatial context leads to increased drift and instability in tracking (Arent I, Schmidt FP, Botsch M et al. Marker-less motion capture of insect locomotion with deep neural networks pre-trained on synthetic videos. Frontiers in Behavioral Neuroscience. Vol. 15. 2021. Tang G, Han Y, Sun X, et al. Anti-drift pose tracker (ADPT), a transformer-based network for robust animal pose estimation cross-species. eLife. Vol. 13. 2025). More general techniques, such as Faster Region-based Convolutional Neural Network (Faster R-CNN) and You Only Look Once (YOLO), have also been used to track location-based behaviors such as center-of-mass position and velocity. However, behaviors localized to a single body structure, such as the pharyngeal pumping (i.e., feeding) in the microscopic roundworm\n                    <jats:italic>Caenorhabditis elegans<\/jats:italic>\n                    (\n                    <jats:italic>C. elegans<\/jats:italic>\n                    ), are particularly sensitive to noise from moving non-target body parts. This limitation cannot be resolved by simply adding more training data, as doing so often leads to overfitting rather than improved robustness, and instead requires additional processing beyond existing object tracking packages. To address these challenges, we present a fast, automated method that reliably measures pumping in freely moving\n                    <jats:italic>C. elegans<\/jats:italic>\n                    by combining a state-of-the-art object detector (Faster R-CNN) with a tunable noise filter in a technique we call PumpKin. To validate its performance, we demonstrate both its speed (average of 0.4 seconds\/frame) and its robust estimation capabilities through application to eight different experimental conditions that encompass both satiety and genetically-driven changes to feeding. PumpKin accurately estimates average pumping rates under eight different experimental conditions, which are positively correlated with the estimates of two expert annotators. Furthermore, PumpKin provides reliable estimates of the instantaneous pumping rate dynamics, achieving an average overlap that exceeds the human\u2013human agreement measured via leave-one-out analysis. Applying PumpKin to conditions differing in satiety revealed a shared basal pumping rate of 0.5 Hz across all worm groups recorded off food, regardless of genetic background or satiety state. Together, these findings highlight PumpKin\u2019s ability to accurately isolate and estimate the motion of a single body part during locomotion. Although we present results specific to\n                    <jats:italic>C. elegans<\/jats:italic>\n                    , we anticipate that PumpKin will generalize to behaviors localized to a single body structure in other systems.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1014489","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T17:55:41Z","timestamp":1784310941000},"page":"e1014489","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["PumpKin: A machine-learning pipeline for automatically tracking localized kinematics in freely moving C. elegans"],"prefix":"10.1371","volume":"22","author":[{"given":"Erin","family":"Shappell","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debra","family":"Buggs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennah","family":"Walcott","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6881-660X","authenticated-orcid":true,"given":"Hang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,17]]},"reference":[{"issue":"6","key":"pcbi.1014489.ref001","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1002\/mds.870120629","article-title":"Analysis of blink rate patterns in normal subjects","volume":"12","author":"AR Bentivoglio","year":"1997","journal-title":"Mov Disord"},{"issue":"1","key":"pcbi.1014489.ref002","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/S1542-0124(11)70007-6","article-title":"Spontaneous eyeblink activity","volume":"9","author":"AAV Cruz","year":"2011","journal-title":"Ocul Surf"},{"issue":"2","key":"pcbi.1014489.ref003","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.optom.2019.09.002","article-title":"The clinical and experimental significance of blinking behavior","volume":"13","author":"CW McMonnies","year":"2020","journal-title":"J Optom"},{"issue":"99","key":"pcbi.1014489.ref004","doi-asserted-by":"crossref","first-page":"20140672","DOI":"10.1098\/rsif.2014.0672","article-title":"Mapping the stereotyped behaviour of freely moving fruit flies","volume":"11","author":"GJ Berman","year":"2014","journal-title":"J R Soc Interface"},{"key":"pcbi.1014489.ref005","unstructured":"McIntosh D, Marques TP, Albu AB, Rountree R, Leo FD. Movement Tracks for the Automatic Detection of Fish Behavior in Videos. 2020. 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