Improving STED microscopy with SUPPOSe: enhancing resolution from a single-image.

Here we apply the SUPPOSe algorithm on images acquired using Stimulated Emission Depletion (STED) microscopy with the aim of improving the resolution limit achieved. We processed images of the nuclear pore complex (NPC) from cell lines in which the Nup96 nucleoporin was endogenously labeled. This reference protein forms a ring whose diameter is ∼107 nm with 8 corners ∼42 nm apart from each other.

Combining deep learning with SUPPOSe and compressed sensing for SNR-enhanced localization of overlapping emitters.

We present gSUPPOSe, a novel, to the best of our knowledge, gradient-based implementation of the SUPPOSe algorithm that we have developed for the localization of single emitters. We study the performance of gSUPPOSe and compressed sensing STORM (CS-STORM) on simulations of single-molecule localization microscopy (SMLM) images at different fluorophore densities and in a wide range of signal-to-noise ratio conditions. We also study the combination of these methods with prior image denoising by means of a deep convolutional network.

Device for real-time monitoring of oil-in-water and suspended solids based on thermal lens spectrometry and light scattering.

A novel system suitable for simultaneous monitoring of both oil-in-water and suspended solids based on thermal lens spectroscopy and forward light scattering is presented. The technique measures the concentration of dissolved hydrocarbons and simultaneously detects single oil droplets and suspended particles separately. The device was tested with injection water samples from an on-field water treatment plant, and hydrocarbon concentrations were measured with a precision better than 5% in the range of up to 100 ppm, reaching resolutions as low as 0.