Evidence map›Paper›PMID 36574388›Full record

ArticlePloS one2022

A novel open-source raspberry Pi-based behavioral testing in zebrafish.

Yunlin Li, Fengye Wu, Qinyan Wu, Wenya Liu, Guanghui Li, Benxing Yao, Ran Xiao, Yudie Hu, Junsong Wang

Abstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

9 authors.

Yunlin LiCenter for Molecular Metabolism, School of Environmental & Biological Engineering, Nanjing University of Science and Technology, Nanjing, China.
Fengye WuCenter for Molecular Metabolism, School of Environmental & Biological Engineering, Nanjing University of Science and Technology, Nanjing, China.
Qinyan WuCenter for Molecular Metabolism, School of Environmental & Biological Engineering, Nanjing University of Science and Technology, Nanjing, China.
Wenya LiuCenter for Molecular Metabolism, School of Environmental & Biological Engineering, Nanjing University of Science and Technology, Nanjing, China.
Guanghui LiCenter for Molecular Metabolism, School of Environmental & Biological Engineering, Nanjing University of Science and Technology, Nanjing, China.
Benxing YaoCenter for Molecular Metabolism, School of Environmental & Biological Engineering, Nanjing University of Science and Technology, Nanjing, China.
Ran XiaoCenter for Molecular Metabolism, School of Environmental & Biological Engineering, Nanjing University of Science and Technology, Nanjing, China.
Yudie HuCenter for Molecular Metabolism, School of Environmental & Biological Engineering, Nanjing University of Science and Technology, Nanjing, China.
Junsong WangCenter for Molecular Metabolism, School of Environmental & Biological Engineering, Nanjing University of Science and Technology, Nanjing, China.ORCID 0000-0002-8935-4969

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The zebrafish (Danio rerio) is widely used as a promising high-throughput model organism in neurobehavioral research. The mobility of zebrafish can be dissected into multiple behavior endpoints to assess its neurobehavioral performance. However, such facilities on the market are expensive and clumsy to be used in laboratories. Here, we designed a low-cost, automatic zebrafish behavior assay apparatus, barely without unintentional human operational errors. The data acquisition part, composed of Raspberry Pi and HQ Camera, automatically performs video recording and data storage. Then, the data processing process is also on the Raspberry Pi. Water droplets and inner wall reflection of multi-well cell culture plates (used for placing zebrafish) will affect the accuracy of object recognition. And during the rapid movement of zebrafish, the probability of zebrafish tracking loss increased significantly. Thus, ROI region and related thresholds were set, and the Kalman filter algorithm was performed to estimate the best position of zebrafish in each frame. In addition, all functions of this device are realized by the custom-written behavior analysis algorithm, which makes the optimization of the setup more efficient. Furthermore, this setup was also used to analyze the behavioral changes of zebrafish under different concentrations of alcohol exposure to verify the reliability and accuracy. The alcohol exposure induced an inverted U-shape dose-dependent behavior change in zebrafish, which was consistent with previous studies, showcasing that the data obtained from the setup proposed in this study are accurate and reliable. Finally, the setup was comprehensively assessed by evaluating the accuracy of zebrafish detection (precision, recall, F-score), and predicting alcohol concentration by XGBoost. In conclusion, this study provides a simple, and low-cost package for the determination of multiple behavioral parameters of zebrafish with high accuracy, which could be easily adapted for various other fields.

Indexed as

Behavior, AnimalZebrafishAlgorithmsAnimalsHumansReproducibility of ResultsVideo Recording

Identifiers

PMID36574388
PMCPMC9794099

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LicenceCC BY
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Registered trials

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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.