FRAXE-associated mental retardation protein (FMR2) is an RNA-binding protein with high affinity for G-quartet RNA forming structure

FRAXE is a form of mild to moderate mental retardation due to the silencing of the FMR2 gene. The cellular function of FMR2 protein is presently unknown. By analogy with its homologue AF4, FMR2 was supposed to have a role in transcriptional regulation, but robust evidences supporting this hypothesis are lacking. We observed that FMR2 co-localizes with the splicing factor SC35 in nuclear speckles, the nuclear regions where splicing factors are concentrated, assembled and modified. Similarly to what was reported for splicing factors, blocking splicing or transcription leads to the accumulation of FMR2 in enlarged, rounded speckles. FMR2 is also localized in the nucleolus when splicing is blocked. We show here that FMR2 is able to specifically bind the G-quartet-forming RNA structure with high affinity. Remarkably, in vivo, in the presence of FMR2, the ESE action of the G-quartet situated in mRNA of an alternatively spliced exon of a minigene or of the putative target FMR1 appears reduced. Interestingly, FMR1 is silenced in the fragile X syndrome, another form of mental retardation. All together, our findings strongly suggest that FMR2 is an RNA-binding protein, which might be involved in alternative splicing regulation through an interaction with G-quartet RNA structure

Advancing wavefront shaping with resonant metasurface

International audienceIn metasurface design, the use of look-up tables based on a local periodicity approximation have been tradi-tionally employed, but can result in sub-optimal designs due to lack of consideration of near-field couplingeffects, which are particularly important for resonant systems. This paper explores the benefits of taking intoaccount near-field coupling while optimizing non-local resonant metasurfaces to enhance their performance forwavefront shaping, including the state-of-the-art Huygens’s metasurface

Advancing wavefront shaping with resonant nonlocal metasurfaces: beyond the limitations of lookup tables

International audienceResonant metasurfaces are of paramount importance in addressing the growing demand for reduced thickness and complexity, while ensuring high optical efficiency. This becomes particularly crucial in overcoming fabrication challenges associated with high aspect ratio structures, thereby enabling seamless integration of metasurfaces with electronic components at an advanced level. However, traditional design approaches relying on lookup tables and local field approximations often fail to achieve optimal performance, especially for nonlocal resonant metasurfaces. In this study, we investigate the use of statistical learning optimization techniques for nonlocal resonant metasurfaces, with a specific emphasis on the role of near-field coupling in wavefront shaping beyond single unit cell simulations. Our study achieves significant advancements in the design of resonant metasurfaces. For transmission-based metasurfaces, a beam steering design outperforms the classical design by achieving an impressive efficiency of 80% compared to the previous 23%. Additionally, our optimized extended depth-of-focus (EDOF) metalens yields a remarkable five-fold increase in focal depth, a four-fold enhancement in focusing power compared to conventional designs and an optical resolution superior to 600 cycle/mm across the focus region. Moreover, our study demonstrates remarkable performance with a wavelength-selected beam steering metagrating in reflection, achieving exceptional efficiency surpassing 85%. This far outperforms classical gradient phase distribution approaches, emphasizing the immense potential for groundbreaking applications in the field of resonant metasurfaces

Advanced Optimization Technique for Robust and Multiobjective Metasurface designs

International audienceThis poster emphasizes the paramount importance of employing optimization techniques to effectively address nonlocal resonance phenomena, accomplish multiobjective optimization, and seamlessly integrate uncertain quantification methods