Visual-Preserving Mesh Repair.

Mesh repair is a long-standing challenge in computer graphics and related fields. Converting defective meshes into watertight manifold meshes can greatly benefit downstream applications such as geometric processing, simulation, fabrication, learning, and synthesis. In this work, by assuming the model is visually correct, we first introduce three visual measures for visibility, orientation, and openness, based on ray-tracing. We then present a novel mesh repair framework incorporating visual measures with several critical steps, i.e., open surface closing, face reorientation, and global optimization, to effectively repair meshes with defects (e.g., gaps, holes, self-intersections, degenerate elements, and inconsistent orientations) and preserve visual appearances. Our method reduces unnecessary mesh complexity without compromising geometric accuracy or visual quality while preserving input attributes such as UV coordinates for rendering. We evaluate our approach on hundreds of models randomly selected from ShapeNet and Thingi10K, demonstrating its effectiveness and robustness compared to existing approaches.

Neural-IMLS: Self-supervised Implicit Moving Least-Squares Network for Surface Reconstruction.

Surface reconstruction is a challenging task when input point clouds, especially real scans, are noisy and lack normals. Observing that the Multilayer Perceptron (MLP) and the implicit moving least-square function (IMLS) provide a dual representation of the underlying surface, we introduce Neural-IMLS, a novel approach that directly learns a noise-resistant signed distance function (SDF) from unoriented raw point clouds in a self-supervised manner. In particular, IMLS regularizes MLP by providing estimated SDFs near the surface and helps enhance its ability to represent geometric details and sharp features, while MLP regularizes IMLS by providing estimated normals. We prove that at convergence, our neural network produces a faithful SDF whose zero-level set approximates the underlying surface due to the mutual learning mechanism between the MLP and the IMLS. Extensive experiments on various benchmarks, including synthetic and real scans, show that Neural-IMLS can reconstruct faithful shapes even with noise and missing parts. The source code can be found at https://github.com/bearprin/Neural-IMLS.

Gene Repair of iPSC Line with GARS (G294R) Mutation of CMT2D Disease by CRISPR/Cas9.

OBJECTIVE: Charcot-Marie-Tooth disease (CMT) severely affects patient activity, and may cause disability. However, no clinical treatment is available to reverse the disease course. The combination of CRISPR/Cas9 and iPSCs may have therapeutic potential against nervous diseases, such as CMT. METHODS: In the present study, the skin fibroblasts of CMT type 2D (CMT2D) patients with the c.880G>A heterozygous nucleotide mutation in the GARS gene were reprogrammed into iPSCs using three plasmids (pCXLE-hSK, pCXLE-hUL and pCXLE-hOCT3/4-shp5-F). Then, CRISPR/Cas9 technology was used to repair the mutated gene sites at the iPSC level. RESULTS: An iPSC line derived from the GARS (G294R) family with fibular atrophy was successfully induced, and the mutated gene loci were repaired at the iPSC level using CRISPR/Cas9 technology. These findings lay the foundation for future research on drug screening and cell therapy. CONCLUSION: iPSCs can differentiate into different cell types, and originate from autologous cells. Therefore, they are promising for the development of autologous cell therapies for degenerative diseases. The combination of CRISPR/Cas9 and iPSCs may open a new avenue for the treatment of nervous diseases, such as CMT.

OctSurf: Efficient hierarchical voxel-based molecular surface representation for protein-ligand affinity prediction.

Voxel-based 3D convolutional neural networks (CNNs) have been applied to predict protein-ligand binding affinity. However, the memory usage and computation cost of these voxel-based approaches increase cubically with respect to spatial resolution and sometimes make volumetric CNNs intractable at higher resolutions. Therefore, it is necessary to develop memory-efficient alternatives that can accelerate the convolutional operation on 3D volumetric representations of the protein-ligand interaction. In this study, we implement a novel volumetric representation, OctSurf, to characterize the 3D molecular surface of protein binding pockets and bound ligands. The OctSurf surface representation is built based on the octree data structure, which has been widely used in computer graphics to efficiently represent and store 3D object data. Vanilla 3D-CNN approaches often divide the 3D space of objects into equal-sized voxels. In contrast, OctSurf recursively partitions the 3D space containing the protein-ligand pocket into eight subspaces called octants. Only those octants containing van der Waals surface points of protein or ligand atoms undergo the recursive subdivision process until they reach the predefined octree depth, whereas unoccupied octants are kept intact to reduce the memory cost. Resulting non-empty leaf octants approximate molecular surfaces of the protein pocket and bound ligands. These surface octants, along with their chemical and geometric features, are used as the input to 3D-CNNs. Two kinds of CNN architectures, VGG and ResNet, are applied to the OctSurf representation to predict binding affinity. The OctSurf representation consumes much less memory than the conventional voxel representation at the same resolution. By restricting the convolution operation to only octants of the smallest size, our method also alleviates the overall computational overhead of CNN. A series of experiments are performed to demonstrate the disk storage and computational efficiency of the proposed learning method. Our code is available at the following GitHub repository: https://github.uconn.edu/mldrugdiscovery/OctSurf.

Cardanol-derived cationic surfactants enabling the superior antibacterial activity of single-walled carbon nanotubes.

Single-walled carbon nanotubes (SWCNTs) are potential antibacterial material, and their antibacterial activity in aqueous solutions depends on efficient surfactants to create strong interactions between well-dispersed SWCNTs and bacterial cells. Here, we designed and synthesized a new family of cationic surfactants by introducing different positively charged hydrophilic heads, i.e. -(CH2)6N+(CH3)3Br-, -(CH2)2N+(CH3)3Br- and -(CH2)2N+PyridineBr-, to cardanol obtained from cashew nut shell liquid. These surfactants can efficiently disperse SWCNTs in aqueous solutions because benzene rings and olefin chains in cardanol enable their strong π-stacking on SWCNTs. A much higher fraction of SWCNTs can be dispersed individually compared to the commonly used surfactant, dodecylbenzene-sulfonate sodium (SDBS). SWCNTs dispersed in the cardanol-derived surfactants demonstrate significantly improved antibacterial activity. At the concentration of 0.5 wt%, their minimum inhibitory concentration is 0.33 and 0.02 µg ml-1 against E. coli and S. aureus, respectively, which is only 0.8%-1.5% of that of SDBS-dispersed SWCNTs. The strong antibacterial activity can be attributed to both better dispersion of SWCNTs and positive charges introduced by hydrophilic heads, which are attracted to negatively charged bacterial cell surfaces. These cardanol-derived surfactants are promising as sustainable surfactants for enabling various SWCNT applications.

Gapless Spin Excitations in the Field-Induced Quantum Spin Liquid Phase of α-RuCl_{3}.

α-RuCl_{3} is a leading candidate material for the observation of physics related to the Kitaev quantum spin liquid (QSL). By combined susceptibility, specific-heat, and nuclear-magnetic-resonance measurements, we demonstrate that α-RuCl_{3} undergoes a quantum phase transition to a QSL in a magnetic field of 7.5 T applied in the ab plane. We show further that this high-field QSL phase has gapless spin excitations over a field range up to 16 T. This highly unconventional result, unknown in either Heisenberg or Kitaev magnets, offers insight essential to establishing the physics of α-RuCl_{3}.

Structural and Magnetic Phase Transitions near Optimal Superconductivity in BaFe2(As(1-x)Px)2.

We use nuclear magnetic resonance (NMR), high-resolution x-ray, and neutron scattering studies to study structural and magnetic phase transitions in phosphorus-doped BaFe2(As(1-x)P(x)2. Previous transport, NMR, specific heat, and magnetic penetration depth measurements have provided compelling evidence for the presence of a quantum critical point (QCP) near optimal superconductivity at x=0.3. However, we show that the tetragonal-to-orthorhombic structural (T{s}) and paramagnetic to antiferromagnetic (AF, TN) transitions in BaFe2(As(1-x)Px)2 are always coupled and approach T{N}≈T{s}≥T{c} (≈29 K) for x=0.29 before vanishing abruptly for x≥0.3. These results suggest that AF order in BaFe_{2}(As(1-x)Px)2 disappears in a weakly first-order fashion near optimal superconductivity, much like the electron-doped iron pnictides with an avoided QCP.