It is undeniable that plastics are ubiquitous and a threat to global ecosystems. Plastic waste is transformed into microplastics (MPs) through physical and chemical disruption processes within the aquatic environment. MPs are detected in almost every environment due to their worldwide transportability through ocean currents or wind, which allows them to reach even the most remote regions of our planet. MPs colonized by biofilm-forming microbial communities are known as the ‘‘plastisphere”. The revelation that this unique substrate can aid microbial dispersal has piqued interest in the ground of microbial ecology. MPs have synergetic effects on the development, transportation, persistence, and ecology of microorganisms. This review summarizes the studies of plastisphere in recent years and the microbial community assemblage (viz. autotrophs, heterotrophs, predators, and pathogens). We also discussed plastic-microbe interactions and the potential sources of plastic degrading microorganisms. Finally, it also focuses on current technologies used to characterize those microbial inhabitants and recommendations for further research.

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Accuracy of the well-known k-nearest neighbor (kNN) classifier heavily depends on the choice of k. The problem of estimating a suitable k for any test point becomes difficult due to several factors like the local distribution of training points around that test point, presence of outliers in the dataset, and, dimensionality of the feature space. In this paper, we propose a dynamic k estimation algorithm based on the neighbor density function of a test point and class variance as well as certainty factor information of the training points. Performance of the kNN algorithm with the proposed choice of k is evaluated on UCI benchmark datasets without employing any dimensionality reduction and/or explicit learning method(s). Experimental results clearly demonstrate the supremacy of the proposed method over the existing kNN algorithm with k = 1, 3, 5 and [√ ] (N denotes the number of training points) for different distance measures)

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