56 research outputs found

    Extracting Axial Depth and Trajectory Trend Using Astigmatism, Gaussian Fitting, and CNNs for Protein Tracking

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    Accurate analysis of vesicle trafficking in live cells is challenging for a number of reasons: varying appearance, complex protein movement patterns, and imaging conditions. To allow fast image acquisition, we study how employing an astigmatism can be utilized for obtaining additional information that could make tracking more robust. We present two approaches for measuring the z position of individual vesicles. Firstly, Gaussian curve fitting with CNN-based denoising is applied to infer the absolute depth around the focal plane of each localized protein. We demonstrate that adding denoising yields more accurate estimation of depth while preserving the overall structure of the localized proteins. Secondly, we investigate if we can predict using a custom CNN architecture the axial trajectory trend. We demonstrate that this method performs well on calibration beads data without the need for denoising. By incorporating the obtained depth information into a trajectory analysis, we demonstrate the potential improvement in vesicle tracking

    Single-Molecule Localization Microscopy Reconstruction Using Noise2Noise for Super-Resolution Imaging of Actin Filaments

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    Single-molecule localization microscopy (SMLM) is a super-resolution imaging technique developed to image structures smaller than the diffraction limit. This modality results in sparse and non-uniform sets of localized blinks that need to be reconstructed to obtain a super-resolution representation of a tissue. In this paper, we explore the use of the Noise2Noise (N2N) paradigm to reconstruct the SMLM images. Noise2Noise is an image denoising technique where a neural network is trained with only pairs of noisy realizations of the data instead of using pairs of noisy/clean images, as performed with Noise2Clean (N2C). Here we have adapted Noise2Noise to the 2D SMLM reconstruction problem, exploring different pair creation strategies (fixed and dynamic). The approach was applied to synthetic data and to real 2D SMLM data of actin filaments. This revealed that N2N can achieve reconstruction performances close to the Noise2Clean training strategy, without having access to the super-resolution images. This could open the way to further improvement in SMLM acquisition speed and reconstruction performance

    Analysis and modelling of tsunami-induced tilt for the 2007, M = 7.6, Tocopilla and the 2010, M = 8.8 Maule earthquakes, Chile, from long-base tiltmeter and broadband seismometer records

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    We present a detailed study of tsunami-induced tilt at in-land sites, to test the interest and feasibility of such analysis for tsunami detection and modelling. We studied tiltmeter and broadband seismometer records of northern Chile, detecting a clear s

    Imaging and modeling the ionospheric airglow response over Hawaii to the tsunami generated by the Tohoku earthquake of 11 March 2011

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    Although only centimeters in amplitude over the open ocean, tsunamis can generate appreciable wave amplitudes in the upper atmosphere, including the naturally occurring chemiluminescent airglow layers, due to the exponential decrease in density with altitude. Here, we present the first observation of the airglow tsunami signature, resulting from the 11 March 2011 Tohoku earthquake off the eastern coast of Japan. These images are taken using a wide-angle camera system located at the top of the Haleakala Volcano on Maui, Hawaii. They are correlated with GPS measurements of the total electron content from Hawaii GPS stations and the Jason-1 satellite. We find waves propagating in the airglow layer from the direction of the earthquake epicenter with a velocity that matches that of the ocean tsunami. The first ionospheric signature precedes the modeled ocean tsunami generated by the main shock by approximately one hour. These results demonstrate the utility of monitoring the Earth's airglow layers for tsunami detection and early warning

    3D super-resolution deep-tissue imaging in living mice.

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    Stimulated emission depletion (STED) microscopy enables the three-dimensional (3D) visualization of dynamic nanoscale structures in living cells, offering unique insights into their organization. However, 3D-STED imaging deep inside biological tissue is obstructed by optical aberrations and light scattering. We present a STED system that overcomes these challenges. Through the combination of two-photon excitation, adaptive optics, red-emitting organic dyes, and a long-working-distance water-immersion objective lens, our system achieves aberration-corrected 3D super-resolution imaging, which we demonstrate 164 µm deep in fixed mouse brain tissue and 76 µm deep in the brain of a living mouse

    Participation in Corporate Governance

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