Performance of deep learning restoration methods for the extraction of particle dynamics in noisy microscopy image sequences

Particle tracking in living systems requires low light exposure and short exposure times to avoid phototoxicity and photobleaching and to fully capture particle motion with high-speed imaging. Low-excitation light comes at the expense of tracking accuracy. Image restoration methods based on deep learning dramatically improve the signal-to-noise ratio in low-exposure data sets, qualitatively improving the images. However, it is not clear whether images generated by these methods yield accurate quantitative measurements such as diffusion parameters in (single) particle tracking experiments. Here, we evaluate the performance of two popular deep learning denoising software packages for particle tracking, using synthetic data sets and movies of diffusing chromatin as biological examples. With synthetic data, both supervised and unsupervised deep learning restored particle motions with high accuracy in two-dimensional data sets, whereas artifacts were introduced by the denoisers in three-dimensional data sets. Experimentally, we found that, while both supervised and unsupervised approaches improved tracking results compared with the original noisy images, supervised learning generally outperformed the unsupervised approach. We find that nicer-looking image sequences are not synonymous with more precise tracking results and highlight that deep learning algorithms can produce deceiving artifacts with extremely noisy images. Finally, we address the challenge of selecting parameters to train convolutional neural networks by implementing a frugal Bayesian optimizer that rapidly explores multidimensional parameter spaces, identifying networks yielding optimal particle tracking accuracy. Our study provides quantitative outcome measures of image restoration using deep learning. We anticipate broad application of this approach to critically evaluate artificial intelligence solutions for quantitative microscopy

Thermal tests of birefringent plates in molecular adhesion for spatial ultra-violet polarimetry

International audienceHigh-resolution spectropolarimetry is a technique used to study many astronomical objects including stellar magnetic fields. It has mainly been used on ground for optical and, more recently, infrared (IR) observations. Space mission projects including ultra-violet (UV) high-resolution spectropolarimetry, such as Pollux onboard LUVOIR proposed to NASA, are being studied in Europe under CNES leadership. Bringing a spectropolarimeter into space means that the instrument should be prepared for space environment including temperatures. The UV polarimeter we are considering is composed by a rotating modulator and an analyzer. Both components are made of magnesium fluoride (MgF<SUB>2</SUB>). The modulator is a rotating block of waveplates in molecular adhesion, each plate having its own fast axis. The analyzer is a Wollaston prism, also made with molecular adhesion. MgF<SUB>2 </SUB>being birefringent, the plates and prism are anisotropic and will dilate and retract due to thermal changes differently along their fast and slow axes. Each plate having its own fast axis, the thermal changes will create stress at the interfaces, i.e. at the molecular adhesion between the plates. This study focuses on the most critical part: the plates of the modulator. To demonstrate the resistance of the modulator and increase its technological readiness level (TRL), an optical bench including interferometry has been set at the Paris Observatory. It allows us to observe in real time the state of the molecular adhesion between plates as they are submitted to thermal changes in a vacuum chamber. Additional samples have been tested in a thermal vacuum chamber at CNES. This article describes the modulator using molecular adhesion, the test experiments, and the conclusion of this thermal study. Although molecular adhesion broke in 2 samples during thermal cycling, most samples survived which provides encouraging results for this technique

Targeted Glomerular Angiopoietin-1 Therapy for Early Diabetic Kidney Disease

Vascular growth factors play an important role in maintaining the structure and integrity of the glomerular filtration barrier. In healthy adult glomeruli, the proendothelial survival factors vascular endothelial growth factor-A (VEGF-A) and angiopoietin-1 are constitutively expressed in glomerular podocyte epithelia. We demonstrate that this milieu of vascular growth factors is altered in streptozotocin-induced type 1 diabetic mice, with decreased angiopoietin-1 levels, VEGF-A upregulation, decreased soluble VEGF receptor-1 (VEGFR1), and increased VEGFR2 phosphorylation. This was accompanied by marked albuminuria, nephromegaly, hyperfiltration, glomerular ultrastructural alterations, and aberrant angiogenesis. We subsequently hypothesized that restoration of angiopoietin-1 expression within glomeruli might ameliorate manifestations of early diabetic glomerulopathy. Podocyte-specific inducible repletion of angiopoietin-1 in diabetic mice caused a 70% reduction of albuminuria and prevented diabetes-induced glomerular endothelial cell proliferation; hyperfiltration and renal morphology were unchanged. Furthermore, angiopoietin-1 repletion in diabetic mice increased Tie-2 phosphorylation, elevated soluble VEGFR1, and was paralleled by a decrease in VEGFR2 phosphorylation and increased endothelial nitric oxide synthase Ser(1177) phosphorylation. Diabetes-induced nephrin phosphorylation was also reduced in mice with angiopoietin-1 repletion. In conclusion, targeted angiopoietin-1 therapy shows promise as a renoprotective tool in the early stages of diabetic kidney disease