Vibrio coralliilyticus, a prominent pathogenic bacteria, is known to cause tissue damage in the coral Pocillopora damicornis and is attracted towards the coral via chemotaxis. However, the potential of V. coralliilyticus to infect most of the other coral hosts via chemotaxis is unknown. In this study, we used capillary assays to quantify the chemotactic response of V. coralliilyticus to the mucus of four tank-cultivated coral species (Cataphyllia jardine, Mussidae sp., Nemenzophyllia turbida, and Euphyllia ancora), and mucus from three wild coral species (Acropora sp., Porites sp., and Montipora sp.). The bacteria showed a positive chemotactic response to each coral mucus tested, with the highest response recorded to the mucus of Acropora sp. and the lowest response to the mucus of Montipora sp. A microfluidic chip was then used to assess the chemotactic preference of V. coralliilyticus to the mucus of the tank cultivated corals. Here too, the bacterium showed positive response, but with a slightly different ranking order. The strong chemotactic response of V. coralliilyticus towards the mucus tested could indicate a broader host range of V. coralliilyticus, and by extension, indicate a threat to weakened coral reefs worldwide.
Microplastic (MP) contamination on land has been estimated to be 32 times higher than in the oceans, and yet there is a distinct lack of research on soil MPs compared to marine MPs. Beaches are bridges between land and ocean and present equally understudied sites of microplastic pollution. Visible-near-infrared (vis-NIR) has been applied successfully for the measurement of reflectance and prediction of low-density polyethylene (LDPE), polyethylene terephthalate (PET), and polyvinyl chloride (PVC) concentrations in soil. The rapidity and precision associated with this method make vis-NIR promising. The present study explores PCA regression and machine learning approaches for developing learning models. First, using a spectroradiometer, the spectral reflectance data was measured from treated beach sediment spiked with virgin microplastic pellets [LDPE, PET, and acrylonitrile butadiene styrene (ABS)]. Using the recorded spectral data, predictive models were developed for each microplastic using both the approaches. Both approaches generated models of good accuracy with R2 values greater than 0.7, root mean squared error (RMSE) values less than 3 and mean absolute error (MAE)