VTechWorks staff will be away for the Independence Day holiday from July 4-7. We will respond to email inquiries on Monday, July 8. Thank you for your patience.
 

Computational tools for inversion and uncertainty estimation in respirometry

Simple item page
dc.contributor.authorCho, Taewonen
dc.contributor.authorPendar, Hodjaten
dc.contributor.authorChung, Julianneen
dc.contributor.departmentMathematicsen
dc.contributor.departmentBiomedical Engineering and Mechanicsen
dc.date.accessioned2021-08-25T12:00:34Zen
dc.date.available2021-08-25T12:00:34Zen
dc.date.issued2021-05-21en
dc.date.updated2021-08-25T12:00:30Zen
dc.description.abstractIn many physiological systems, real-time endogeneous and exogenous signals in living organisms provide critical information and interpretations of physiological functions; however, these signals or variables of interest are not directly accessible and must be estimated from noisy, measured signals. In this paper, we study an inverse problem of recovering gas exchange signals of animals placed in a flow-through respirometry chamber from measured gas concentrations. For large-scale experiments (e.g., long scans with high sampling rate) that have many uncertainties (e.g., noise in the observations or an unknown impulse response function), this is a computationally challenging inverse problem. We first describe various computational tools that can be used for respirometry reconstruction and uncertainty quantification when the impulse response function is known. Then, we address the more challenging problem where the impulse response function is not known or only partially known. We describe nonlinear optimization methods for reconstruction, where both the unknown model parameters and the unknown signal are reconstructed simultaneously. Numerical experiments show the benefits and potential impacts of these methods in respirometry.en
dc.description.versionPublished versionen
dc.format.extent27 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifierARTN e0251926 (Article number)en
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0251926en
dc.identifier.eissn1932-6203en
dc.identifier.issn1932-6203en
dc.identifier.issue5en
dc.identifier.orcidPendar, Hodjat [0000-0001-8853-0062]en
dc.identifier.otherPONE-D-20-38297 (PII)en
dc.identifier.pmid34019586en
dc.identifier.urihttp://hdl.handle.net/10919/104702en
dc.identifier.volume16en
dc.language.isoenen
dc.publisherPLoSen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000664632300040&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectPHYSIOLOGICAL SYSTEMSen
dc.subjectDECONVOLUTIONen
dc.subjectALGORITHMSen
dc.titleComputational tools for inversion and uncertainty estimation in respirometryen
dc.title.serialPLOS ONEen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
dcterms.dateAccepted2021-05-06en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Biomedical Engineering and Mechanicsen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Computational tools for inversion and uncertainty estimation in respirometry.pdf
Size:
3.8 MB
Format:
Adobe Portable Document Format
Description:
Published version
Download

Collections

All Faculty Deposits
Scholarly Works, Biomedical Engineering and Mechanics
Scholarly Works, Mathematics