DeepShaRM: Multi-View Shape and Reflectance Map Recovery Under Unknown Lighting

Geometry reconstruction of textureless, non-Lambertian objects under unknown natural illumination (i.e., in the wild) remains challenging as correspondences cannot be established and the reflectance cannot be expressed in simple analytical forms. We derive a novel multi-view method, DeepShaRM, that achieves state-of-the-art accuracy on this challenging task. Unlike past methods that formulate this as inverse-rendering, i.e., estimation of reflectance, illumination, and geometry from images, our key idea is to realize that reflectance and illumination need not be disentangled and instead estimated as a compound reflectance map. We introduce a novel deep reflectance map estimation network that recovers the camera-view reflectance maps from the surface normals of the current geometry estimate and the input multi-view images. The network also explicitly estimates per-pixel confidence scores to handle global light transport effects. A deep shape-from-shading network then updates the geometry estimate expressed with a signed distance function using the recovered reflectance maps. By alternating between these two, and, most important, by bypassing the ill-posed problem of reflectance and illumination decomposition, the method accurately recovers object geometry in these challenging settings. Extensive experiments on both synthetic and real-world data clearly demonstrate its state-of-the-art accuracy.Comment: 3DV 202

Hemodynamic Analysis of a Microanastomosis Using Computational Fluid Dynamics

[Background] Technical issues in free flap transfer, such as the selection of recipient vessels and the positioning and method of anastomosis of the vascular pedicle, have been the subject of vigorous debate. Recent developments in computational fluid dynamics (CFD) have enabled the analysis of blood flow within microvessels. In this study, CFD was used to analyze hemodynamics in a microanastomosis. [Methods] In the fluid calculation process, the fluid domain modelizes microvessels with anastomosis. The inlet flow conditions were measured as venous waveform, and the fluid is simulated as blood. Streamlines (SL), wall shear stress (WSS), and oscillatory shear index (OSI) at the anastomosis were visualized and analyzed for observing effects from the flow field. [Results] Some flow disruption was evident as the SL passed over the sutures. The maximum recorded WSS was 13.37 Pa where the peak of a suture was exposed in the lumen. The local maximum value of the OSI was 0.182, recorded at the base of the anastomosis on the outflow side. [Conclusion] In the ideal anastomosis, the SL is disrupted as little as possible by the sutures. The WSS indicated that thrombus formation is unlikely to occur at suture peaks, but more likely to occur at the base of sutures, where the OSI is high. Tight suture knots are important in microanastomosis

A proximity biotinylation-based approach to identify protein-E3 ligase interactions induced by PROTACs and molecular glues

Proteolysis-targeting chimaeras (PROTACs) as well as molecular glues such as immunomodulatory drugs (IMiDs) and indisulam are drugs that induce interactions between substrate proteins and an E3 ubiquitin ligases for targeted protein degradation. Here, we develop a workflow based on proximity-dependent biotinylation by AirID to identify drug-induced neo-substrates of the E3 ligase cereblon (CRBN). Using AirID-CRBN, we detect IMiD-dependent biotinylation of CRBN neo-substrates in vitro and identify biotinylated peptides of well-known neo-substrates by mass spectrometry with high specificity and selectivity. Additional analyses reveal ZMYM2 and ZMYM2-FGFR1 fusion protein—responsible for the 8p11 syndrome involved in acute myeloid leukaemia—as CRBN neo-substrates. Furthermore, AirID-DCAF15 and AirID-CRBN biotinylate neo-substrates targeted by indisulam and PROTACs, respectively, suggesting that this approach has the potential to serve as a general strategy for characterizing drug-inducible protein–protein interactions in cells