Monocular Depth Decomposition of Semi-Transparent Volume Renderings.

Neural networks have shown great success in extracting geometric information from color images. Especially, monocular depth estimation networks are increasingly reliable in real-world scenes. In this work we investigate the applicability of such monocular depth estimation networks to semi-transparent volume rendered images. As depth is notoriously difficult to define in a volumetric scene without clearly defined surfaces, we consider different depth computations that have emerged in practice, and compare state-of-the-art monocular depth estimation approaches for these different interpretations during an evaluation considering different degrees of opacity in the renderings. Additionally, we investigate how these networks can be extended to further obtain color and opacity information, in order to create a layered representation of the scene based on a single color image. This layered representation consists of spatially separated semi-transparent intervals that composite to the original input rendering. In our experiments we show that existing approaches to monocular depth estimation can be adapted to perform well on semi-transparent volume renderings, which has several applications in the area of scientific visualization, like re-composition with additional objects and labels or additional shading.

From peptides to their alternating ester-urea analogues: synthesis and influence of hydrogen bonding motif and stereochemistry on aggregation.

Peptide-mimicking scaffolds with an incorporated ester-urea motif, replacing two adjacent amide residues, were synthesized and their aggregation behavior was studied in dependence of hydrogen bonding sites as well as backbone stereochemistry. Two oligomer series containing either 50% or 100% ester-urea units and either all-(l) or (d)-alt-(l) backbone configuration were prepared via ester and amide couplings, using a divergent/convergent exponential growth strategy. Their aggregation behavior in organic solution was investigated by means of concentration-dependent NMR spectroscopy and compared to the parent peptide series. Interestingly, the naturally occurring peptide scaffold exhibits the largest tendency to associate in combination with the strongest difference in aggregation behavior between all-(l) and (d)-alt-(l) backbone stereochemistry. With increasing incorporation of the ester-urea motif the aggregation strength decreases and become much less dependent on the backbone configuration. The obtained structure-aggregation relationships reveal the importance of the commensurability and multivalency of hydrogen bonding sites as well as conformational restriction for peptide association and should hence aid the design of peptide mimics, such as beta-sheet breakers or gelators.