Online monitoring of methane transfer rates unveils nitrogen fixation dynamics in Methylococcus capsulatus.

This study explores methane utilization by the methanotrophic microorganism Methylococcus capsulatus (Bath) for biomass production, presenting a promising approach to mitigate methane emissions and foster the development sustainable biomaterials. Traditional screening methods for gas cultivations involve either serum flasks without online monitoring or costly, low-throughput fermenters. To address these limitations, the Respiration Activity MOnitoring System was augmented with methane sensors for real-time methane transfer rate (MTR) monitoring in shake flasks.

Leveraging Self-Supervised Vision Transformers for Segmentation-based Transfer Function Design.

In volume rendering, transfer functions are used to classify structures of interest, and to assign optical properties such as color and opacity. They are commonly defined as 1D or 2D functions that map simple features to these optical properties. As the process of designing a transfer function is typically tedious and unintuitive, several approaches have been proposed for their interactive specification.

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.

Finding Nano-Ötzi: Cryo-Electron Tomography Visualization Guided by Learned Segmentation.

Cryo-electron tomography (cryo-ET) is a new 3D imaging technique with unprecedented potential for resolving submicron structural details. Existing volume visualization methods, however, are not able to reveal details of interest due to low signal-to-noise ratio. In order to design more powerful transfer functions, we propose leveraging soft segmentation as an explicit component of visualization for noisy volumes.

Deep Volumetric Ambient Occlusion.

We present a novel deep learning based technique for volumetric ambient occlusion in the context of direct volume rendering. Our proposed Deep Volumetric Ambient Occlusion (DVAO) approach can predict per-voxel ambient occlusion in volumetric data sets, while considering global information provided through the transfer function. The proposed neural network only needs to be executed upon change of this global information, and thus supports real-time volume interaction.