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A novel approach of age-grading of mosquitoes using SERS and machine learning models

Award Number: R01AI180243
ORGANIZATION: NATIONAL INSTITUTE OF ALLERGY & INFECTIOUS DISEASES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 12/15/2023
PERIOD OF PERFORMANCE END DATE: 10/31/2028

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A novel approach of age-grading of mosquitoes using SERS and machine learning models - Project Summary Mosquito-borne pathogens, including malaria, Zika, dengue, and chikungunya continue to be a major public health concern globally. As only older mosquitoes are infectious and represent a risk to human health, scientists have sought to age-grade mosquitoes based on this understanding; however, no reliable, cost effective and practical methods exist to age mosquitoes despite the tremendous epidemiological value of this approach. The overall objective of this R01 is to establish a novel approach to age-grade mosquitoes Aedes aegypti in the field. The approach we took is based on surface-enhanced Raman spectroscopy (SERS) to analyze the biomolecules from mosquito water extract that are bound with silver nanoparticles (AgNPs) and then the SERS spectra are used in modern machine learning models to age-grade the mosquitoes. Our central hypothesis is that AgNPs interact with specific biomolecules enabling SERS to generate unique and predictable spectral information for establishing modern machine learning models to determine the age of mosquitoes. Our prior work demonstrates the feasibility of SERS and Artificial Neuron Networks (ANNs) to determine the age of both lab (error <1 day) and field-collected (error < 2 days) mosquitoes Ae. aegypti. In the proposed work, we will establish robust lab and field-deployable protocols to produce reliable and repeatable SERS data of mosquito water extract. Then, we will manipulate the lab and field conditions to determine the impact of biotic (food and infection status) and abiotic (temperature) to SERS characteristics. Robust and accurate machine learning model based on modern ANNs and Domain Adoption (DA) strategies will be established and validated for age-grading mosquitoes in the field. In addition, we will explore the Multi-task Learning (MTL) strategies to simultaneously determine the age and infection status. Our long-term goal is to establish a rapid, cost-effective, and field-deployable system that enables real-time analysis and data sharing to facilitate epidemiological studies, risk assessment, vector control intervention monitoring and evaluation.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $370,265 )
2026	2026	UNIVERSITY OF MASSACHUSETTS	101 COMMONWEALTH AVE	AMHERST	MA	01003	HAMPSHIRE	USA	Allergy and Infectious Diseases Research	000	3	12/8/2025	NON-COMPETING CONTINUATION	$370,265
														Subtotal = $370,265

Issue Date FY: 2025 ( Subtotal = $364,013 )
2025	2025	UNIVERSITY OF MASSACHUSETTS	101 COMMONWEALTH AVE	AMHERST	MA	01003	HAMPSHIRE	USA	Allergy and Infectious Diseases Research	000	2	11/5/2024	NON-COMPETING CONTINUATION	$364,013
														Subtotal = $364,013

Issue Date FY: 2024 ( Subtotal = $407,940 )
2024	2024	UNIVERSITY OF MASSACHUSETTS	101 COMMONWEALTH AVE	AMHERST	MA	01003	HAMPSHIRE	USA	Allergy and Infectious Diseases Research	001	1	9/3/2024	NEW	$40,794
2024	2024	UNIVERSITY OF MASSACHUSETTS	COMMONWEALTH AVE	AMHERST	MA	01003	HAMPSHIRE	USA	Allergy and Infectious Diseases Research	000	1	12/15/2023	NEW	$367,146
														Subtotal = $407,940

Grand Total All Awards = $1,142,218

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A novel approach of age-grading of mosquitoes using SERS and machine learning models

Award Number: R01AI180243

ORGANIZATION: NATIONAL INSTITUTE OF ALLERGY & INFECTIOUS DISEASES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 12/15/2023

PERIOD OF PERFORMANCE END DATE: 10/31/2028

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