Electrochemical synthesis of peptide aldehydes via C‒N bond cleavage of cyclic amines.

Peptide aldehydes are crucial biomolecules essential to various biological systems, driving a continuous demand for efficient synthesis methods. Herein, we develop a metal-free, facile, and biocompatible strategy for direct electrochemical synthesis of unnatural peptide aldehydes. This electro-oxidative approach enabled a step- and atom-economical ring-opening via C‒N bond cleavage, allowing for homoproline-specific peptide diversification and expansion of substrate scope to include amides, esters, and cyclic amines of various sizes. The remarkable efficacy of the electro-synthetic protocol set the stage for the efficient modification and assembly of linear and macrocyclic peptides using a concise synthetic sequence with racemization-free conditions. Moreover, the combination of experiments and density functional theory (DFT) calculations indicates that different N-acyl groups play a decisive role in the reaction activity.

EditableNeRF: Editing Topologically Varying Neural Radiance Fields by Key Points.

Neural radiance fields (NeRF) achieve highly photo-realistic novel-view synthesis, but it's a challenging problem to edit the scenes modeled by NeRF-based methods, especially for dynamic scenes. We propose editable neural radiance fields that enable end-users to easily edit dynamic scenes and support topological changes. Input with an image sequence from a single camera, our network is trained automatically and models topologically varying dynamics using our picked-out surface key points. Then end-users can edit the scene by easily dragging the key points to desired new positions. To achieve this, we propose a scene analysis method to detect and initialize key points by considering the dynamics in the scene, and a weighted key points strategy to model topologically varying dynamics by joint key points and weights optimization. Our method supports intuitive multi-dimensional (up to 3D) editing and can generate novel scenes that are unseen in the input sequence. Experiments demonstrate that our method achieves high-quality editing on various dynamic scenes and outperforms the state-of-the-art.

A Self-Occlusion Aware Lighting Model for Real-Time Dynamic Reconstruction.

In real-time dynamic reconstruction, geometry and motion are the major focuses while appearance is not fully explored, leading to the low-quality appearance of the reconstructed surfaces. In this article, we propose a lightweight lighting model that considers spatially varying lighting conditions caused by self-occlusion. This model estimates per-vertex masks on top of a single Spherical Harmonic (SH) lighting to represent spatially varying lighting conditions without adding too much computation cost. The mask is estimated based on the local geometry of a vertex to model the self-occlusion effect, which is the major reason leading to the spatial variation of lighting. Furthermore, to use this model in dynamic reconstruction, we also improve the motion estimation quality by adding a real-time per-vertex displacement estimation step. Experiments demonstrate that both the reconstructed appearance and the motion are largely improved compared with the current state-of-the-art techniques.

Direct Electrooxidative Selenylation/Cyclization of Alkynes: Access to Functionalized Benzo[b]furans.

A mild, practical, metal and oxidant-free methodology for the synthesis of various C-3 selenylated benzo[b]furan derivatives was developed through the intramolecular cyclization of alkynes promoted with diselenides via electrooxidation. A wide range of selenium-substituted benzo[b]furan derivatives were obtained in good to excellent yields with high regioselectivity under constant current in an undivided cell equipped with carbon and platinum plates as the anode and cathode, respectively. Moreover, the convergent approach exhibited good functional group tolerance and could be easily scaled up with good efficiency, providing rapid access to a diverse range of selenylated benzo[b]furans derivatives from simple, readily available starting materials.

DTexFusion: Dynamic Texture Fusion Using a Consumer RGBD Sensor.

In addition to 3D geometry, accurate representation of texture is important when digitizing real objects in virtual worlds. Based on a single consumer RGBD sensor, accurate texture representation for static objects can be realized by fusing multi-frame information; however, extending the process to dynamic objects, which typically have time-varying textures, is difficult. Thus, to address this problem, we propose a compact keyframe-based representation that decouples a dynamic texture into a basic static texture and a set of multiplicative changing maps. With this representation, the proposed method first aligns textures recorded from multiple keyframes with the reconstructed dynamic geometry of the object. Errors in the alignment and geometry are then compensated in an innovative iterative linear optimization framework. With the reconstructed texture, we then employ a scheme to synthesize the dynamic object from arbitrary viewpoints. By considering temporal and local pose similarities jointly, dynamic textures in all keyframes are fused to guarantee high-quality image generation. Experimental results demonstrate that the proposed method handles various dynamic objects, including faces, bodies, cloth, and toys. In addition, qualitative and quantitative comparisons demonstrate that the proposed method outperforms state-of-the-art solutions.

Data-Driven 3D Neck Modeling and Animation.

In this article, we present a data-driven approach for modeling and animation of 3D necks. Our method is based on a new neck animation model that decomposes the neck animation into local deformation caused by larynx motion and global deformation driven by head poses, facial expressions, and speech. A skinning model is introduced for modeling local deformation and underlying larynx motions, while the global neck deformation caused by each factor is modeled by its corrective blendshape set, respectively. Based on this neck model, we introduce a regression method to drive the larynx motion and neck deformation from speech. Both the neck model and the speech regressor are learned from a dataset of 3D neck animation sequences captured from different identities. Our neck model significantly improves the realism of facial animation and allows users to easily create plausible neck animations from speech and facial expressions. We verify our neck model and demonstrate its advantages in 3D neck tracking and animation.

Tissue distribution of s-oleylpropanolamide in rats detected by liquid chromatography with tandem mass spectrometry.

This paper is to report the development of a rapid and sensitive method for the determination of s-oleylpropanolamide (OPA) in various tissues of rat (brain, heart, lung, liver, spleen, small intestine, kidney, adipose tissue and muscle), and to assess the applicability of the assay to tissue distribution. OPA was extracted by liquid-liquid extraction method with undecylenoylethanolamide as an internal standard. The concentrations of OPA were determined by LC-MS/MS after a single intragastric dose of 50 mg x kg(-1) at 4 time points (5 rats per group). With multiple reactions monitoring mode (MRM) the limit of quantification (LLOQ) was determined at 1 microg x L(-1). The calibration curve was linear from 1 to 2 x 104 microg x L(-1) (r > or = 0.999 0) for tissue homogenates. Validation parameters such as accuracy, precision and recovery were found to be within the acceptance criteria of the assay validation guidelines. The highest concentration was found in small intestine (the highest time point is 15 min) and heart (the highest time point is 90 min). The assay is rapid, sensitive and applicable to studying tissue distribution of OPA in rats.

Inhibition of TNFα-induced adhesion molecule expression by (Z)-(S)-9-octadecenamide, N-(2-hydroxyethyl,1-methyl).

Inflammation is a primary event in atherogenesis. Oleoylethanolamide (OEA), a naturally occurring fatty-acid ethanolamide, lowers lipid levels in liver and blood through activation of the nuclear receptor, peroxisome proliferator-activated receptor-alpha (PPARα). We designed and synthesized (Z)-(S)-9-octadecenamide, N-(2-hydroxyethyl, 1-methyl) (OPA), an OEA analog. The present study investigated the effect of OPA on the expression of adhesion molecules in human umbilical vein endothelial cells (HUVEC). OPA inhibited expression of vascular cell adhesion molecule-1 (VCAM-1) and intercellular adhesion molecule-1 (ICAM-1) stimulated by Tumor Necrosis Factor-α (TNF-α) via activation of PPARα. This inhibition of VCAM-1 and ICAM-1 expression decreased adhesion of monocyte-like cells to stimulated endothelial cells. These results demonstrate that OPA may have anti-inflammatory properties. Our results thus provide new insights into possible future therapeutic approaches to the treatment of atherosclerosis.