Evidence map›Paper›PMID 28152031›Full record

ArticlePloS one2017

Collective behavior quantification on human odor effects against female Aedes aegypti mosquitoes-Open source development.

Abdul Halim Poh, Mahmoud Moghavvemi, Cherng Shii Leong, Yee Ling Lau, Alireza Safdari Ghandari, Alexlee Apau, Faisal Rafiq Mahamd Adikan

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
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  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Abdul Halim PohDepartment of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.ORCID http://orcid.org/0000-0002-7599-7971
Mahmoud MoghavvemiDepartment of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.ORCID http://orcid.org/0000-0001-6447-4203
Cherng Shii LeongDepartment of Parasitology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
Yee Ling LauDepartment of Parasitology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
Alireza Safdari GhandariDepartment of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.
Alexlee ApauDepartment of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.
Faisal Rafiq Mahamd AdikanDepartment of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Classifying and quantifying mosquito activity includes a plethora of categories, ranging from measuring flight speeds, repellency, feeding rates, and specific behaviors such as home entry, swooping and resting, among others. Entomologists have been progressing more toward using machine vision for efficiency for this endeavor. Digital methods have been used to study the behavior of insects in labs, for instance via three-dimensional tracking with specialized cameras to observe the reaction of mosquitoes towards human odor, heat and CO2, although virtually none was reported for several important fields, such as repellency studies which have a significant need for a proper response quantification. However, tracking mosquitoes individually is a challenge and only limited number of specimens can be studied. Although tracking large numbers of individual insects is hailed as one of the characteristics of an ideal automated image-based tracking system especially in 3D, it also is a costly method, often requiring specialized hardware and limited access to the algorithms used for mapping the specimens. The method proposed contributes towards (a) unlimited open source use, (b) a low-cost setup, (c) complete guide for any entomologist to adapt in terms of hardware and software, (d) simple to use, and (e) a lightweight data output for collective behavior analysis of mosquitoes. The setup is demonstrated by testing a simple response of mosquitoes in the presence of human odor versus control, one session with continuous human presence as a stimuli and the other with periodic presence. A group of female Aedes aegypti (Linnaeus) mosquitoes are released into a white-background chamber with a transparent acrylic panel on one side. The video feed of the mosquitoes are processed using filtered contours in a threshold-adjustable video. The mosquitoes in the chamber are mapped on the raster where the coordinates of each mosquito are recorded with the corresponding timestamp. The average distance of the blobs within the frames against time forms a spectra where behavioral patterns can be observed directly, whether any collective effect is observed. With this method, 3D tracking will not be required and a more straightforward data output can be obtained.

Indexed as

Spatial BehaviorAedesAlgorithmsAnimalsAutomationFemaleHumansOdorantsSoftware

Identifiers

PMID28152031
PMCPMC5289636

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.