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Abstract Many planets in the solar system and across the Galaxy have hydrogen-rich atmospheres overlying more heavy element-rich interiors with which they interact for billions of years. Atmosphere–interior interactions are thus crucial to understanding the formation and evolution of these bodies. However, this understanding is still lacking in part because the relevant pressure–temperature conditions are extreme. We conduct molecular dynamics simulations based on density functional theory to investigate how hydrogen and water interact over a wide range of pressure and temperature, encompassing the interiors of Neptune-sized and smaller planets. We determine the critical curve at which a single homogeneous phase exsolves into two separate hydrogen-rich and water-rich phases, finding good agreement with existing experimental data. We find that the temperature along the critical curve increases with increasing pressure and shows the influence of a change in fluid structure from molecular to atomic near 30 GPa and 3000 K, which may impact magnetic field generation. The internal temperatures of many exoplanets, including TOI-270 d and K2-18 b, may lie entirely above the critical curve: the envelope is expected to consist of a single homogeneous hydrogen–water fluid, which is much less susceptible to atmospheric loss as compared with a pure hydrogen envelope. As planets cool, they cross the critical curve, leading to rainout of water-rich fluid and an increase in internal luminosity. Compositions of the resulting outer, hydrogen-rich and inner, water-rich envelopes depend on age and instellation and are governed by thermodynamics. Rainout of water may be occurring in Uranus and Neptune at present. 

While machine learning approaches have shown remarkable performance in biomedical image analysis, most of these methods rely on high-quality and accurate imaging data. However, collecting such data requires intensive and careful manual effort. One of the major challenges in imaging the Shoot Apical Meristem (SAM) of Arabidopsis thaliana, is that the deeper slices in the z-stack suffer from different perpetual quality related problems like poor contrast and blurring. These quality related issues often lead to disposal of the painstakingly collected data with little to no control on quality while collecting the data. Therefore, it becomes necessary to employ and design techniques that can enhance the images to make it more suitable for further analysis. In this paper, we propose a data-driven Deep Quantized Latent Representation (DQLR) methodology for high-quality image reconstruction in the Shoot Apical Meristem (SAM) of Arabidopsis thaliana. Our proposed framework utilizes multiple consecutive slices in the z-stack to learn a low dimensional latent space, quantize it and subsequently perform reconstruction using the quantized representation to obtain sharper images. Experiments on a publicly available dataset validate our methodology showing promising results. Our code is available at github.com/agupt013/enhancedRec.git.