Rapid age-grading and species identification of natural mosquitoes for malaria surveillance

1235 34 5109 3 25794 document 5109 Rapid_age-grading_and_species_identification_of_natural_mosquitoes.zip application/zip 6865d3eddafaaedf10c1670dc1c1699b MD5 211104715 2022-02-04 14:27:27 https://researchdata.gla.ac.uk/1235/1/Rapid_age-grading_and_species_identification_of_natural_mosquitoes.zip 1235 1 1 application/zip Mixed en public cc_by_4

Rapid_age-grading_and_species_identification_of_natural_mosquitoes.zip

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http://eprints.org/relation/isVersionOf https://researchdata.gla.ac.uk/id/document/5110 http://eprints.org/relation/isVolatileVersionOf https://researchdata.gla.ac.uk/id/document/5110 http://eprints.org/relation/isIndexCodesVersionOf https://researchdata.gla.ac.uk/id/document/5110 archive 5 disk0/00/00/12/35 2022-02-21 14:55:17 2025-06-11 10:50:54 2022-02-21 14:55:17 data_collection show Siria Doreen Sanou Roger Mitton Joshua 47456 Mwanga Emmanuel 61636 Niang Abdoulaye Saré Issiaka Johnson Paul 10389 0000-0001-6663-7520 Wynne Klaas 15668 0000-0002-5305-5940 Murray-Smith Roderick 11535 0000-0003-4228-7962 Ferguson Heather 6001 0000-0002-9625-5176 Gonzalez Jimenez Mario 31706 0000-0002-8853-0588 Babayan Simon 30070 0000-0002-4949-1117 Diabaté Abdoulaye Okumu Fredros 37197 Baldini Francesco 31072 0000-0002-5904-4070 glaresearchdata:2022-02-21-1235 Rapid age-grading and species identification of natural mosquitoes for malaria surveillance pub 20300000 30100000 30200000 The malaria parasite, which is transmitted by several Anopheles mosquito species, requires more time to reach its human-transmissible stage than the average lifespan of mosquito vectors. Monitoring the species-specific age structure of mosquito populations is critical to evaluating the impact of vector control interventions on malaria risk. We developed a rapid, cost-effective surveillance method based on deep learning of mid-infrared spectra of mosquito cuticle that simultaneously identifies the species and age class of three main malaria vectors in natural populations. Using spectra from over 40,000 ecologically and genetically diverse An. gambiae, An. arabiensis, and An. coluzzii females, we developed a deep transfer learning model that learned and predicted the age of new wild populations in Tanzania and Burkina Faso with minimal sampling effort. Additionally, the model was able to detect the impact of simulated control interventions on mosquito populations, measured as a shift in their age structures. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases. 2022-02-21 published University of Glasgow 10.5525/gla.researchdata.1235 Code Text University of Glasgow Ifakara Health Institute Institut de Recherches en Sciences de la Santé 174132 Development of a new tool for malaria mosquito surveillance to improve vector control Heather Ferguson Medical Research Council (MRC) MR/P025501/1 Institute of Biodiversity, Animal Health and Comparative Medicine 190647 Solvation dynamics and structure around proteins and peptides: collective network motions vs. weak interactions Klaas Wynne Engineering and Physical Sciences Research Council (EPSRC) EP/K034995/1 Chemistry 171830 EPSRC: Institutional Sponsorship 2015 - University of Glasgow Miles Padgett Engineering and Physical Sciences Research Council (EPSRC) EP/N508792/1 Computing Science 172399 Mapping and controlling nucleation Klaas Wynne Engineering and Physical Sciences Research Council (EPSRC) EP/N007417/1 Chemistry 303917 Delocalised phonon-like modes in organic and bio-molecules Klaas Wynne Leverhulme Trust (LEVERHUL) RPG-2018-350 Chemistry 304469 CONTROL Klaas Wynne European Commission (EC) 832703 Chemistry TRUE EN 2019-04 2021-11 https://doi.org/10.1101/2020.06.11.144253 preprint 2032-02-21 R file_transfer TRUE 18 15 nhs The University of Glasgow: Scottish National Blood Transfusion Service committee for governance of blood and tissue samples for non-therapeutic use and Donor Research: Reference No 18 15. The Ifakara Health Institute: the Ifakara Health Institute Institutional Review Board: Reference IHI/IRB/EXT/No: 005-2018, and the Medical Research Coordinating Committee at the National Institutes of Medical Research: Reference NIMR/HQ/R.8c/Vol.II/880. Institut de Recherche en Sciences de la Santé (IRSS): IRSS Ethical Committee: number A012-2017/CEIRES FALSE Rapid age-grading and species identification of natural mosquitoes for malaria surveillance http://eprints.gla.ac.uk/id/eprint/264593 Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra https://eprints.gla.ac.uk/id/eprint/288743