Fluorescent polydopamine nanoparticles as a probe for zebrafish sensory hair cells targeted in vivo imaging

Abstract Fluorescent polydopamine nanoparticles (FPNPs) are prepared via the ethylenediamine (EDA)-induced degradation of as-prepared non-fluorescent polydopamine (PDA) and used for targeted bioimaging. The reductive treatment of PDA in the presence of EDA yields fluorescent precipitates, inspiring us to seek various biological approaches to preparing FPNPs with excellent optical and biocompatible properties. Moreover, we firstly found that FPNPs selectively label neuromast hair cells in the lateral line of zebrafish, their applications as a reliable fluorescent indicator to investigate the neuromast hair cells, to in turn determine the viability of hair cells, was demonstrated. FPNPs also provided a minimal toxicity enable to assay the number of functional hair cells per neuromast in live animals as development proceeds. Upon combined incubation with TO-PRO-3, a well-established hair cell marker, all hair cells that were rapidly labeled with FPNPs were observed to be also completely labeled with the TO-PRO-3, labeling hair cells in neuromasts positioned in the supraorbital, otic and occipital lateral line as well as in posterior lateral line of living zebrafish larvae. Their potential efficacy for biological applications was demonstrated by their excellent optical and biocompatible properties, offering new opportunities in cancer research, real-time monitoring of stem cell transplantation and other cell-based therapies

Overview and State-of-the-Art of Uncertainty Visualization

International audienceThe goal of visualization is to effectively and accurately communicate data. Visualization research has often overlooked the errors and uncertainty which accompany the scientific process and describe key characteristics used to fully understand the data. The lack of these representations can be attributed, in part, to the inherent difficulty in defining, characterizing, and controlling this uncertainty, and in part, to the difficulty in including additional visual metaphors in a well designed, potent display. However, the exclusion of this information cripples the use of visualization as a decision making tool due to the fact that the display is no longer a true representation of the data. This systematic omission of uncertainty commands fundamental research within the visualization community to address, integrate, and expect uncertainty information. In this chapter, we outline sources and models of uncertainty, give an overview of the state-of-the-art, provide general guidelines, outline small exemplary applications, and finally, discuss open problems in uncertainty visualization