Behavioral Responses in Zebrafish (Danio rerio) Exposed to Heavy Metal and Insecticide Induced Stress

Background: Changes in fish behavior can help identify accidental chemical pollution. Heavy metals and pesticides are two of the most found pollutants to investigate the different behavioral responses of fish to these two types of pollutants exposure. Methods: Real-time computer imaging was utilized to record parameters of fish behaviors, including swimming speed, turning frequency, depth and distance between fish. Deltamethrin and cadmium were 0.015 ppm and 3.5 ppm, respectively. It was conducted for a total period of 180 min. Fish behaviors were recorded with dechlorinated water during the first 60 mins, then deltamethrin and cadmium was introduced to observe behavioral responses of zebrafish during the next 120 mins. Result: As a result of increased swimming activity, the first response of zebrafish is avoidance followed by a changed distribution in the test chamber. The duration of hyperactivity during deltamethrin exposure was lasted 35 minutes larger than Cd exposure and the average swimming depth showed totally different trends with increased from 140 mm to 226 mm during deltamethrin exposure but decreased from 161 to 84 mm during cadmium exposure. It is proved that these different responses do exist under in the two chemicals studied and this may contribute to the development of biological early warning system to separate accidental chemical pollution types.

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A New on-Line Multi-Factor Monitoring System of Water Accidental Pollution Events Based on the Behavioral Responses of Aquatic Organisms

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.317-319.1058 ◽

2011 ◽

Vol 317-319 ◽

pp. 1058-1061 ◽

Cited By ~ 3

Author(s):

Zong Ming Ren ◽

Yang Zeng ◽

Xiu E Fu

Keyword(s):

Monitoring System ◽

Behavioral Responses ◽

Warning System ◽

Aquatic Organisms ◽

Negative Effects ◽

Accidental Pollution ◽

Biological Early Warning System ◽

Chemical Factors ◽

On Line ◽

Pollution Events

In building a new On-line Multi-factor Monitoring System (OMMS) of Water Accidental Pollution Events (WAPE) based on the behavioral responses (BR) of aquatic organisms, the effects of physi-chemical factors (PF) such as temperature (T), turbidity (TUB) and pH on BR of Daphnia magna and medaka (Oryzias latipes) were discussed with Biological Early Warning System (BEWS). The results suggested that when T was lower than 5°C, BR of D. magna and medaka would be inhibited in 5 hours. If TUB was more than 50NTU, the behavior strength (BS) of D. magna would decrease to about 0.3 after 10h exposure, the effects on medaka would be much weaker in 24h than D. magna. If pH is lower than 5.5 or higher than 9, negative effects on BR of both aquatic organisms would happen in 15h. Therefore, a new OMMS was built for the purpose of environmental management of WAPE, which included BEWS and PF Monitoring System (PFMS).

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Improvement of Biological Early Warning System Based on Medaka (Oryzias latipes) Behavioral Responses to Physiochemical Factors

Journal of Biobased Materials and Bioenergy ◽

10.1166/jbmb.2012.1290 ◽

2012 ◽

Vol 6 (6) ◽

pp. 678-681 ◽

Cited By ~ 3

Author(s):

Zongming Ren ◽

Naizhou Su ◽

Mingsheng Miao ◽

Rongshu Fu ◽

Gaosheng Zhang

Keyword(s):

Early Warning ◽

Early Warning System ◽

Behavioral Responses ◽

Warning System ◽

Biological Early Warning System

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Changing trends of mental and behavioral responses and associations during the COVID-19 epidemic in China: a panel study

Health Education Research ◽

10.1093/her/cyab012 ◽

2021 ◽

Vol 36 (2) ◽

pp. 151-158

Author(s):

Xiaozhao Yousef Yang ◽

Sihui Peng ◽

Tingzhong Yang ◽

Randall R Cottrell

Keyword(s):

Self Efficacy ◽

Perceived Risk ◽

Mental Stress ◽

Panel Study ◽

Behavioral Responses ◽

Test Methods ◽

Baseline Survey ◽

Emotional Status ◽

Total Period ◽

Changing Trends

Abstract This prospective observational study examined changing trends in mental and behavioral responses, and their association with perceived risk, severity, self-efficacy and isolation status during the Chinese COVID-19 epidemic. There were five waves of interviews. Descriptive statistics and non-parametric test methods were used for data analysis. Participants numbered 150 for the linkable baseline survey and 102 completed all 5 survey waves and were included in the analysis. Mental stress, emotional status and lifestyle manifested a statistically significant downwards trend across the total period of this panel study. The number of reported new confirmed patients perceived high risk and perceived severity were positively associated with mental stress, emotional status and lifestyle. Self-efficacy was negatively associated with each type of mental and behavioral response. The more time people were confined to their home, the more serious the emotional and lifestyle problems. Dose-response relationships were noted between the number of reported new confirmed patients and mental stress, emotional status and lifestyle during the five observation points. This study yielded new information about mental and behavioral responses among Chinese people during the COVID-19 epidemic. Policy changes and health education are essential for minimizing the adverse health effects of these responses.

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Aquatic Toxic Analysis by Monitoring Fish Behavior Using Computer Vision: A Recent Progress

Journal of Toxicology ◽

10.1155/2018/2591924 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 7

Author(s):

Chunlei Xia ◽

Longwen Fu ◽

Zuoyi Liu ◽

Hui Liu ◽

Lingxin Chen ◽

...

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Aquatic Toxicity ◽

Warning System ◽

Video Tracking ◽

Behavioral Data ◽

3D Space ◽

Biological Early Warning System ◽

Abnormal Pattern ◽

2D And 3D

Video tracking based biological early warning system achieved a great progress with advanced computer vision and machine learning methods. Ability of video tracking of multiple biological organisms has been largely improved in recent years. Video based behavioral monitoring has become a common tool for acquiring quantified behavioral data for aquatic risk assessment. Investigation of behavioral responses under chemical and environmental stress has been boosted by rapidly developed machine learning and artificial intelligence. In this paper, we introduce the fundamental of video tracking and present the pioneer works in precise tracking of a group of individuals in 2D and 3D space. Technical and practical issues suffered in video tracking are explained. Subsequently, the toxic analysis based on fish behavioral data is summarized. Frequently used computational methods and machine learning are explained with their applications in aquatic toxicity detection and abnormal pattern analysis. Finally, advantages of recent developed deep learning approach in toxic prediction are presented.

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