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Single molecule localisation microscopy (SMLM) generates data in the form of Cartesian coordinates of localised fluorophores. Cluster analysis is an attractive route for extracting biologically meaningful information from such data and has been widely applied. Despite the range of developed cluster analysis algorithms, there exists no consensus framework for the evaluation of their performance. Here, we use a systematic approach based on two metrics, the Adjusted Rand Index (ARI) and Intersection over Union (IoU), to score the success of clustering algorithms in diverse simulated clustering scenarios mimicking experimental data. We demonstrate the framework using three analysis algorithms: DBSCAN, ToMATo and KDE, show how to deduce optimal analysis parameters and how they are affected by fluorophore multiple blinking. We propose that these standard conditions and metrics become the basis for future analysis algorithm development and evaluation..

There is an increasing trend in the food industry on the Listeria monocytogenes biofilm formation and inhibition. This is attributed to its easy survival on contact surfaces, resistance to disinfectants or antibiotics and growth under the stringent condition used for food processing and preservation thereby leading to food contamination products by direct or indirect exposure. Though, there is a lack of conclusive evidences about the mechanism of biofilm formation, in this review, the concept of biofilm formation and various chemical, physical, and green technology approaches to prevent or control the biofilm formed is discussed. State-of-the-art approaches ranging from the application of natural to synthetic molecules with high effectiveness and non-toxicity targeted at the different steps of biofilm formation could positively influence the biofilm inhibition in the future.