Movement Patterning of Daphnia magna Treated with Copper Based on Self-Organizing Map

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.

Evaluation of the sensitivity spectrum of a video tracking system with zebrafish (Danio rerio) exposed to five different toxicants

The aim of this study was to develop a biological early warning system for the detection of aquatic toxicity and test it with five toxicants with distinct chemical nature. This was done in order to verify the spectrum of sensitivities of the proposed system, as well as the potential identification capability of the tested contaminants, using only the analysis of zebrafish's behavior. Six experimental conditions were tested: negative control and five toxicants (bleach, lindane, tributyltin, mercury, and formaldehyde). The exposure time was 45 min, and the concentrations used corresponded to 9% of LC₅₀'s-96 h for the tested compounds, to ensure ecologically relevant results. A total of 108 fish were used, with each individual experimental condition being tested 18 times. A statistical model of diagnosis was used, combining self-organizing map and correspondence analysis. The values of sensitivity, specificity, accuracy, false positive, false negative, positive predictive value (PPV), and negative predictive value (NPV) were calculated. The objectives of the work were accomplished and the system showed a good overall diagnostic performance with 79% in accuracy, 77% in sensitivity, and 88% in specificity. The lowest result of the predictive values was 78% (lindane and mercury), in the case of the NPV, and 86% (bleach and lindane), in the case of the PPV. The best result of the predictive values was 100% (bleach and tributyltin), for the NPV, and 89% (tributyltin), for the PPV. Regarding the five tested toxicants, the system was able to correctly identify the agent responsible for the contamination in 40% of the positive diagnoses.

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.