Abstract:We present a new super-resolution technique, Re-scan Confocal Microscopy (RCM), based on standard confocal microscopy extended with an optical (re-scanning) unit that projects the image directly on a CCDcamera. This new microscope has improved lateral resolution and strongly improved sensitivity while maintaining the sectioning capability of a standard confocal microscope. This simple technology is typically useful for biological applications where the combination high-resolution and highsensitivity is required. Toomre, and J. Bewersdorf, "Video-rate nanoscopy using sCMOS camera-specific single-molecule localization algorithms," Nat. Methods 10(7), 653-658 (2013).

Annexins, found in most eukaryotic species, are cytosolic proteins that are able to bind negatively-charged phospholipids in a calcium-dependent manner. Annexin A4 (AnxA4) has been implicated in diverse cellular processes, including the regulation of exocytosis and ion-transport; however, its precise mechanistic role is not fully understood. AnxA4 has been shown to aggregate on lipid layers upon Ca(2+) binding in vitro, a characteristic that may be critical for its function. We have utilized advanced fluorescence microscopy to discern details on the mobility and self-assembly of AnxA4 after Ca(2+) influx at the plasma membrane in living cells. Total internal reflection microscopy in combination with Förster resonance energy transfer reveals that there is a delay between initial plasma membrane binding and the beginning of self-assembly and this process continues after the cytoplasmic pool has completely relocated. Number-and-brightness analysis suggests that the predominant membrane bound mobile form of the protein is trimeric. There also exists a pool of AnxA4 that forms highly immobile aggregates at the membrane. Fluorescence recovery after photobleaching suggests that the relative proportion of these two forms varies and is correlated with membrane morphology.

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Big data offers many opportunities for official statistics: for example increased resolution, better timeliness, and new statistical outputs. But there are also many challenges: uncontrolled changes in sources that threaten continuity, lack of identifiers that impedes linking to population frames, and data that refers only indirectly to phenomena of statistical interest. We discuss two approaches to deal with these challenges and opportunities. First, we may accept big data for what they are: an imperfect, yet timely, indicator of phenomena in society. These data exist and that's why they are interesting. Secondly, we may extend this approach by explicit modelling. New methods like machine-learning techniques can be considered alongside more traditional methods like Bayesian techniques. National statistical institutes have always been reluctant to use models, apart from specific cases like small-area estimates. Based on the experience at Statistics Netherlands we argue that NSIs should not be afraid to use models, provided that their use is documented and made transparent to users. Moreover, the primary purpose of an NSI is to describe society; we should refrain from making forecasts. The models used should therefore rely on actually observed data and they should be validated extensively.