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Trials of the Automated Particle Counter for laboratory rearing of mosquito larvae.

Mark Q BenedictPriscila BascuñánCatherine M HuntErica I AvilesRachel D RotenberryEllen M Dotson
Published in: PloS one (2020)
As a means of obtaining reproducible and accurate numbers of larvae for laboratory rearing, we tested a large-particle flow-cytometer type device called the 'Automated Particle Counter' (APC). The APC is a gravity-fed, self-contained unit that detects changes in light intensity caused by larvae passing the detector in a water stream and controls dispensing by stopping the flow when the desired number has been reached. We determined the accuracy (number dispensed compared to the target value) and precision (distribution of number dispensed) of dispensing at a variety of counting sensitivity thresholds and larva throughput rates (larvae per second) using < 1-day old Anopheles gambiae and Aedes aegypti larvae. All measures were made using an APC algorithm called the 'Smoothed Z-Score' which allows the user to define how many standard deviations (Z scores) from the baseline light intensity a particle's absorbance must exceed to register a count. We dispensed a target number of 100 An. gambiae larvae using Z scores from 2.5-8 and observed no difference among them in the numbers dispensed for scores from 2.5-6, however, scores of 7 and 8 under-counted (over-dispensed) larvae. Using a Z score ≤ 6, we determined the effect of throughput rate on the accuracy of the device to dispense An. gambiae larvae. For rates ≤ 98 larvae per second, the accuracy of dispensing a target of 100 larvae was - 2.29% ± 0.72 (95% CI of the mean) with a mode of 99 (49 of 348 samples). When using a Z score of 3.5 and rates ≤ 100 larvae per second, the accuracy of dispensing a target of 100 Ae. aegypti was - 2.43% ± 1.26 (95% CI of the mean) with a mode of 100 (6 of 42 samples). No effect on survival was observed on the number of An. gambiae first stage larvae that reached adulthood as a function of dispensing.
Keyphrases
  • aedes aegypti
  • zika virus
  • dengue virus
  • drosophila melanogaster
  • deep learning
  • high throughput
  • high resolution