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An integrated neural network analysis and video microscopy platform for fully automated particle tracking

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R41GM130202-01
Agency Tracking Number: R41GM130202
Amount: $224,894.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: 400
Solicitation Number: PA17-303
Solicitation Year: 2017
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-09-13
Award End Date (Contract End Date): 2019-09-12
Small Business Information
Carrboro, NC 27510-2504
United States
DUNS: 080335601
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 (607) 279-1880
Business Contact
Phone: (248) 982-7470
Research Institution
United States

 Domestic Nonprofit Research Organization

Project Summary AbstractParticle trackingPTis a biophysical tool for elucidating molecular interactionstransport phenomena
of diverse speciesand rheological properties of complex materialsPT experiments involve first obtaining high
resolution videos that capture time resolved increments of particlesfollowed by extraction of traces of entities
of interest from videos in the form of spatial locations over timea process we refer to as path conversionFinallyquantitative analysis of the traces will yield diffusivitiesviscoelasticityetcLung diseasessuch as cystic fibrosis and COPDare characterized by a highly viscoelastic mucus
layer that is incapable of being cleared by mucociliary clearanceNot surprisinglythe viscoelasticity of mucus
often directly reflects disease progressionA variety of mucolytics are being investigatedbut due to the
variable composition and properties of mucus between patientseffective mucolytics treatment will likely be
different between individualstoo little inappropriate mucolytics will not be effective in restoring mucus
clearancewhereas too much may result in bronchorrheaAlthough microbeads based rheology has been
performed on a variety of mucus specimens in basic researchthe capacity for high throughput
characterization of rheological properties of biological specimens in a clinical setting is currently not availableThis limitation can be attributed to inefficiencies of path conversioncurrent PT software requires extensive
human supervision intervention to achieve accurate path conversionnot only resulting in poor reproducibility
and throughput but also restricting its use to only expert labsOur vision is to make PT as objective and easy
to use as a simple plate reader that can be readily utilized by cliniciansdiagnosticsdisease progressiontherapy effectivenesspharmapreclinical clinical drug screeningand research professionalsTowards this
goalwe have created a neural network trackerNNTthat automatically determines the location of all particles
in each frame with zero user inputi eno parameter for users to changeand retains the identity of all
particles from frame to frameThe innovation is that NNT can robustlyreproduciblyand accurately track a
wide range ofDD videos with virtually no need for human interventionachieving unparalleled time savingsWe have already successfully deployed NNT over the Google cloudwhich offers exceptional scalabilityNeverthelessfor time sensitive applicationssuch as an automated PT rheometerthe transfer of large video
data files is likely prohibitiveThereforein this Phase I STTRwe seek to enable real time NNT based PT
analysis on the local machine while video microscopy data is being acquired by the microscopeand allow data
from PT analysis to drive the operation of the microscopeIn Aimwe will integrate our NNT with a single
objective fluorescence microscope system called MonoptesAimwill evaluate the performance of our NNTMonoptes systemIf successfulour technology would form the basis of a fully automated PT system capable
of measuring rheological properties of fluids materials or distribution of particle sizes in awell plate format Project Narrative
Particle tracking is a powerful biophysical tool in life and physical sciencesbut unfortunatelyits application
has been strongly limited by inefficiencies in accurately extracting particle traces from raw moviesUnlike
conventional particle tracking methodswe have combined artificial intelligence and machine learning to create
a software that can consistently provide superior and truly automated tracking performance compared to
current alternativesIn this proposalwe will integrate this latest advance with sophisticated instrumentation to
develop a microscope system capable of fully automated particle tracking microscopy in awell plate formatIf successfulthe instrument will likely be utilized by cliniciansdiagnosticsdisease progressiontherapy
effectivenesspharmapreclinical clinical drug screening of patientsand research professionals

* Information listed above is at the time of submission. *

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