Measurements of the swimming speeds of motile microorganisms using object tracking and their correlation with water pollution and rheology levels.
Ashaa Preyadharishini ShunmugamGowtham SubramanianJavier G FernandezPublished in: Scientific reports (2021)
Self-propelled microscopic organisms are ubiquitous in water. Such organisms' motility depends on hydrodynamic and physical factors related to the rheology of the surrounding media and biological factors depending on the organisms' state and well-being. Here we demonstrate that the swimming speed of Paramecium aurelia, a unicellular protozoan, globally found in fresh, brackish, and salt waters, can be used as a measurable frugal indicator of the presence of pollutants in water. This study establishes a significant and consistent relationship between Paramecia's swimming speed and the presence of five different organic and inorganic contaminants at varying concentrations centered around drinking water thresholds. The large size and ubiquity of the targeted microorganism, the avoidance of reagents or specialized tools for the measurement, and the simple data collection based on an object tracking algorithm enable the automatization of the assessment and real-time results using globally available technology.
Keyphrases
- drinking water
- health risk assessment
- heavy metals
- gram negative
- working memory
- health risk
- risk assessment
- mental health
- physical activity
- machine learning
- deep learning
- particulate matter
- multidrug resistant
- escherichia coli
- human health
- water soluble
- biofilm formation
- big data
- drug delivery
- cystic fibrosis
- staphylococcus aureus