Pattern recognition of the movement tracks of medaka (Oryzias latipes) in response to sub-lethal treatments of an insecticide by using artificial neural networks

Analyzing the Response Behavior of Lumbriculus variegatus (Oligochaeta: Lumbriculidae) to Different Concentrations of Copper Sulfate Based on Line Body Shape Detection and a Recurrent Self-Organizing Map

Point detection (e.g., the centroid of the body) of species has been conducted in numerous studies. However, line detection (i.e., the line body shape) of elongated species has rarely been investigated under stressful conditions. We analyzed the line movements of an Oligochaeta Lumbriculus variegatus in response to treatments with a toxic chemical, copper sulfate, at low concentrations (0.01 mg/L and 0.1 mg/L). The automatic line-tracking system was devised to identify the movement of body segments (body length) and the movements of segments (i.e., the speed and angles between segments) were recorded before and after treatment. Total body length was shortened from 31.22 (±5.18) mm to 20.91 (±4.65) mm after the 0.1 mg/L treatment. The Shannon entropy index decreased from 0.44 (±0.1) to 0.28 (±0.08) after treatment. On the other hand, the body and movement segments did not significantly change after the 0.01 mg/L treatment. Sequential movements of test organisms were further analyzed with a recurrent self-organizing map (RSOM) to determine the pattern of time-series line movements. The RSOM made it feasible to classify sequential behaviors of indicator organisms and identify various continuous body movements under stressful conditions.

Escherichia coli's water load affects zebrafish (Danio rerio) behavior

Traditional physico-chemical sensors are becoming an obsolete tool for environmental quality assessment. Biomonitoring techniques, such as biological early warning systems present the advantage of being sensitivity, fast, non-invasive and ecologically relevant. In this work, we applied a video tracking system, developed with zebrafish (Danio rerio), to detect microbiological contamination in water. Using the fishs' behavior response, the system was able to detect the presence of a non-pathogenic environmental strain of Escherichia coli, at three different levels of contamination: 600, 1800 and 5000 CFU/100 mL (colony forming units/100 mL). Data was collected during 50 min of exposure and analyzed with the artificial neural networks Self-organizing Map and Multi-layer Perceptron. The behavior of exposed fish was more erratic, with pronounced and rapid changes on movement direction and with significant less exploratory activity. The accuracy, sensitivity and specificity values regarding the detection capability (distinction between presence or absence of contamination) ranged from 89 to 100%. Regarding the classification capability (distinction between experimental conditions), the values ranged from 67 to 89%. This research may be a valuable contribution to improve water monitoring and management strategies, by taking as reference the effects on biosensors, without a biased anthropocentric perspective.

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

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.

Analysis of zebrafish (Danio rerio) behavior in response to bacterial infection using a self-organizing map

BACKGROUND

Animal behavioral responses have been recently established as a suitable tool for detecting contaminants in the environment for risk assessment in situ. In this study, we observed movement behavior of zebrafish (Danio rerio) before and after infection with Edwardsiella tarda CK41 for 3 days until death.

METHODS

Infection status of zebrafish was confirmed through PCR and colonization assay as time progressed and lesion development in the tails of zebrafish was also examined. Movement behaviors in response to bacterial infection were patterned by self-organizing map (SOM) based on movement parameters, including speed (mm/s), acceleration (mm/s (2) ), stop duration (t), stop number (n), locomotory rate (mm/s), turning rate (rad/s), and meander (rad/mm).

RESULTS

According to SOM result, clusters were identified firstly according to time and secondly according to infection. Two movement patterns were observed in the early period of infection: one group with minimum turning rate and meander (i.e., stiff movement) and the other group with maximum strop number. Late infection was characterized by long stop duration.

CONCLUSION

SOM was suitable for extracting complex behavioral data and thus can serve as a referencing system for diagnosing disease development in order to reveal the mechanism of the infection process.

Video-tracking of zebrafish (Danio rerio) as a biological early warning system using two distinct artificial neural networks: Probabilistic neural network (PNN) and self-organizing map (SOM)

Biological early warning systems (BEWS) are becoming very important tools in ecotoxicological studies because they can detect changes in the behavior of organisms exposed to toxic substances. In this work, a video tracking system was fully developed to detect the presence of commercial bleach (NaOCl) in water in three different concentrations (0.0005%; 0.0010% and 0.0020% (v/v)) during one hour of exposure. Zebrafish was selected as the test organism because it is widely used in many different areas and studies. Two distinct statistical models were developed, using probabilistic neural network (PNN) and correspondence analysis associated with self-organizing map (SOM-CA). The diagnosis was based only in the analysis of a few behavioral components of the fish, namely: mean angular velocity, mean linear velocity, spatial dispersion, mean value of the X coordinate and mean value of the Y coordinate. Both models showed good results in their diagnosis capabilities. However, the overall performance (accuracy) was always superior in the PNN model. The worst result was with the SOM-CA model, at the lowest concentration (0.0005% v/v), achieving only 65% of correct diagnosis. The best result was with the PNN model, at the highest concentration (0.0020% v/v), achieving 94% of correct diagnosis.

Biological early warning system based on the responses of aquatic organisms to disturbances: a review

Aquatic ecosystems are subject to a number of anthropogenic disturbances, including environmental toxicants. The efficient monitoring of water resources is fundamental for effective management of water quality and aquatic ecosystems. Spot sampling and continuous water quality monitoring based on physicochemical factors are conducted to assess water quality. However, not all contaminants or synergistic and antagonistic toxic effects can be determined by solely analyzing the physicochemical factors. Thus, various biotests have been developed using long-term and automatic observation studies based on the ability of the aquatic organisms to continuously sense a wide range of pollutants. In addition, a biological early warning system (BEWS) has been developed based on the response behaviors of organisms to continuously detect a wide range of pollutants for effective water quality monitoring and management. However, large amounts of data exhibiting non-linearity and individual behavioral variation are continuously accumulated over long-term and continuous behavioral monitoring studies. Thus, appropriate mathematical and computational data analyses are necessary to manage and interpret such large datasets. Here, we review the development and application of BEWS by using various groups of organisms and the computational methods used to process the behavioral monitoring data.

Water toxicity assessment and spatial pollution patterns identification in a Mediterranean River Basin District. Tools for water management and risk analysis

In compliance with the requirements of the EU Water Framework Directive, monitoring of the ecological and chemical status of Catalan river basins (NE Spain) is carried out by the Catalan Water Agency. The large amount of data collected and the complex relationships among the environmental variables monitored often mislead data interpretation in terms of toxic impact, especially considering that even pollutants at very low concentrations might contribute to the total toxicity. The total dataset of chemical monitoring carried out between 2007 and 2008 (232 sampling stations and 60 pollutants) has been analyzed using sequential advanced modeling techniques. Data on concentrations of contaminants in water were pre-treated in order to calculate the bioavailable fraction, depending on substance properties and local environmental conditions. The resulting values were used to predict the potential impact of toxic substances in complex mixtures on aquatic biota and to identify hot spots. Exposure assessment with Species Sensitivity Distribution (SSD) and mixture toxicity rules were used to compute the multi-substances Potentially Affected Fraction (msPAF). The combined toxicity of the pollutants analyzed in the Catalan surface waters might potentially impact more than 50% of the species in 10% of the sites. In order to understand and visualize the spatial distribution of the toxic risk, Self Organising Map (SOM), based on the Kohonen's Artificial Neural Network (ANN) algorithm, was applied on the output data of these models. Principal Component Analysis (PCA) was performed on top of Neural Network results in order to identify main influential variables which account for the pollution trends. Finally, predicted toxic impacts on biota have been linked and correlated to field data on biological quality indexes using macroinvertebrate and diatom communities (IBMWP and IPS). The methodology presented could represent a suitable tool for water managers in environmental risk assessment and management.

Three-dimensional neurophenotyping of adult zebrafish behavior

The use of adult zebrafish (Danio rerio) in neurobehavioral research is rapidly expanding. The present large-scale study applied the newest video-tracking and data-mining technologies to further examine zebrafish anxiety-like phenotypes. Here, we generated temporal and spatial three-dimensional (3D) reconstructions of zebrafish locomotion, globally assessed behavioral profiles evoked by several anxiogenic and anxiolytic manipulations, mapped individual endpoints to 3D reconstructions, and performed cluster analysis to reconfirm behavioral correlates of high- and low-anxiety states. The application of 3D swim path reconstructions consolidates behavioral data (while increasing data density) and provides a novel way to examine and represent zebrafish behavior. It also enables rapid optimization of video tracking settings to improve quantification of automated parameters, and suggests that spatiotemporal organization of zebrafish swimming activity can be affected by various experimental manipulations in a manner predicted by their anxiolytic or anxiogenic nature. Our approach markedly enhances the power of zebrafish behavioral analyses, providing innovative framework for high-throughput 3D phenotyping of adult zebrafish behavior.

The application of entropy for detecting behavioral responses in Japanese medaka (Oryzias latipes) exposed to different toxicants

Growing concern over environmental pollution by chemicals that may directly or indirectly impact human and ecosystem health make it increasingly necessary to establish a biological early warning system to minimize these impacts. To achieve this, simple and reliable approaches and criteria for environmental risk assessment must be developed. We propose a new approach for detecting behavioral responses to toxicants in flowing water. We conducted a series of tests to determine toxic responses of Japanese medaka (Oryzias latipes) to lethal concentrations of potassium cyanide (KCN; 10 mg L(-1)) and phenol (25 mg L(-1)). Fish movements were tracked by three-dimensional (3D) biomonitoring under flow-through conditions. The 3D data were used to evaluate behavioral responses in terms of swimming speed, vertical position in the water, and entropies of these parameters. Fish exposed to KCN surfaced more frequently than unexposed fish, whereas fish exposed to phenol stayed near the bottom. Maximum swimming speed significantly increased after exposure to both test chemicals, whereas median swimming speed significantly decreased after exposure. Despite large variations in swimming behavior between individual fish, toxic behavioral responses could be identified by the significant decrease in the entropy of vertical position after 10-30 min of exposure to the test chemicals. We conclude that the use of entropy can contribute to the development of a biological early warning system for detecting toxicants at early stages of pollution. To use the present approach to detect lower concentrations of toxicants, further studies should be conducted using long-term monitoring of fish exposed to sublethal concentrations.

Computational characterization of behavioral response of medaka (Oryzias latipes) treated with diazinon

The behavior of indicator specimens in response to sub-lethal doses of toxic substances has been used to detect contamination in aquatic ecosystems. Changes in the movement behaviors of medaka (Oryzias latipes) were analyzed after being treated with diazinon at a concentration of 0.1 mg/l. The movement tracks of medaka were continuously recorded in two-dimension by a digital image processing system both before and after the treatments. Subsequently, two computational methods--two-dimensional fast Fourier transform (2D FFT) and self-organizing map (SOM), were implemented to extract information from the movement data. The differences in the shapes of the movement tracks before and after the treatments were clearly manifested through 2D FFT. The short-distance, irregular turnings in the movement tracks observed after the treatments in the time domain were characteristically transformed to circular or ellipsoidal patterns in the frequency domain. The amplitudes of 2D FFT were efficiently classified by SOM, demonstrating the effects of the different treatments. To evaluate the feasibility of information extraction by 2D FFT, SOM was similarly carried out on the parameters (speed, meander, stop duration, etc.) conventionally used for characterizing the movement tracks. 2D FFT was more efficient in information extraction from the movement data than the parameters. The 2D FFT and SOM were useful as computational methods for automatically detecting response behaviors of indicator specimens exposed to toxic chemicals in aquatic ecosystems.