<p><strong>Abstract.</strong> Analyzing optical remote sensing imagery depends heavily on their spatial resolution. At the same time, this data is adversely affected by fixed sensor parameters and environmental influences. Methods for increasing the quality of such data and concomitantly optimizing its information content are, thus, in high demand. In particular, single-image super-resolution (SISR) approaches aim to achieve this goal solely by observing the individual images.</p><p>We propose to adapt a generic deep residual neural network architecture for SISR to deal with the special properties of remote sensing satellite imagery, especially taking into account the different spatial resolutions of individual Sentinel-2 bands, i.e., ground sampling distances of 20&amp;thinsp;m and 10&amp;thinsp;m. As a result, this method is able to increase the perceived resolution of the 20&amp;thinsp;m channels and mesh all spectral bands. Experimental evaluation and ablation studies on large datasets have shown superior performance compared to the state-of-the-art and that the model is not bound by its capacity.</p>

Background: Data on ocular syphilis (OS) and its clinical presentation are currently insufficient. This study aimed to investigate the characteristics of a cohort with a high OS incidence at a university hospital in Germany, focusing on the clinical presentation of OS.Methods: This single-center cohort study retrospectively analyzed data on 90 patients with 109 episodes of syphilis between 2008 and 2018. Cases of OS were identified and additionally reevaluated through a study-specific secondary assessment by an ophthalmologist specializing in uveitis.

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