Transcriptional analysis of pqqD and study of the regulation of pyrroloquinoline quinone biosynthesis in Methylobacterium extorquens AM1

Methanol dehydrogenase, the enzyme that oxidizes methanol to formaldehyde in gram-negative methylotrophs, contains the prosthetic group pyrroloquinoline quinone (PQQ). To begin to analyze how the synthesis of PQQ is coordinated with the production of other methanol dehydrogenase components, the transcription of one of the key PQQ synthesis genes has been studied. This gene (pqqD) encodes a 29-amino- acid peptide that is thought to be the precursor for PQQ biosynthesis. A unique transcription start site was mapped to a guanidine nucleotide 95 bp upstream of the pqqD initiator codon. RNA blot analysis identified two transcripts, a major one of 240 bases encoding pqqD and a minor one of 1,300 bases encoding pqqD and the gene immediately downstream, pqqG. Both transcripts are present at similar levels in cells grown on methanol and on succinate, but the levels of PQQ are about fivefold higher in cells grown on methanol than in cells grown on succinate. These results suggest that PQQ production is regulated at a level different from the transcription of pqqD. The genes mxbM, mxbD, mxcQ, mxcE, and mxaB are required for transcription of the genes encoding the methanol dehydrogenase subunits and were assessed for their role in PQQ production. PQQ levels were measured in mutants defective in each of these regulatory genes and compared with levels of pqqD transcription, measured with a transcriptional fusion between the pqqD promoter and xylE. The results showed that only a subset of these regulatory genes (mxbM, mxbD, and mxaB) is required for transcription of pqqD, and only mxbM and mxbD mutants affected the final levels of PQQ significantly

Light Field Blind Motion Deblurring

We study the problem of deblurring light fields of general 3D scenes captured under 3D camera motion and present both theoretical and practical contributions. By analyzing the motion-blurred light field in the primal and Fourier domains, we develop intuition into the effects of camera motion on the light field, show the advantages of capturing a 4D light field instead of a conventional 2D image for motion deblurring, and derive simple methods of motion deblurring in certain cases. We then present an algorithm to blindly deblur light fields of general scenes without any estimation of scene geometry, and demonstrate that we can recover both the sharp light field and the 3D camera motion path of real and synthetically-blurred light fields.Comment: To be presented at CVPR 201

NeRFs: The Search for the Best 3D Representation

Neural Radiance Fields or NeRFs have become the representation of choice for problems in view synthesis or image-based rendering, as well as in many other applications across computer graphics and vision, and beyond. At their core, NeRFs describe a new representation of 3D scenes or 3D geometry. Instead of meshes, disparity maps, multiplane images or even voxel grids, they represent the scene as a continuous volume, with volumetric parameters like view-dependent radiance and volume density obtained by querying a neural network. The NeRF representation has now been widely used, with thousands of papers extending or building on it every year, multiple authors and websites providing overviews and surveys, and numerous industrial applications and startup companies. In this article, we briefly review the NeRF representation, and describe the three decades-long quest to find the best 3D representation for view synthesis and related problems, culminating in the NeRF papers. We then describe new developments in terms of NeRF representations and make some observations and insights regarding the future of 3D representations.Comment: Updated based on feedback in-person and via e-mail at SIGGRAPH 2023. In particular, I have added references and discussion of seminal SIGGRAPH image-based rendering papers, and better put the recent Kerbl et al. work in context, with more reference

Learning to Synthesize a 4D RGBD Light Field from a Single Image

We present a machine learning algorithm that takes as input a 2D RGB image and synthesizes a 4D RGBD light field (color and depth of the scene in each ray direction). For training, we introduce the largest public light field dataset, consisting of over 3300 plenoptic camera light fields of scenes containing flowers and plants. Our synthesis pipeline consists of a convolutional neural network (CNN) that estimates scene geometry, a stage that renders a Lambertian light field using that geometry, and a second CNN that predicts occluded rays and non-Lambertian effects. Our algorithm builds on recent view synthesis methods, but is unique in predicting RGBD for each light field ray and improving unsupervised single image depth estimation by enforcing consistency of ray depths that should intersect the same scene point. Please see our supplementary video at https://youtu.be/yLCvWoQLnmsComment: International Conference on Computer Vision (ICCV) 201