Functional evaluation of Asp76, 84, 102 and 150 in human arsenic(III) methyltransferase (hAS3MT) interacting with S-adenosylmethionine

AbstractWe prepared eight mutants (D76P, D76N, D84P, D84N, D102P, D102N, D150P and D150N) to investigate the functions of residues Asp76, 84, 102 and 150 in human arsenic(III) methyltransferase (hAS3MT) interacting with the S-adenosylmethionine (SAM)-binding. The affinity of all the mutants for SAM were weakened. All the mutants except for D150N completely lost their methylation activities. Residues Asp76, 84, 102 and 150 greatly influenced hAS3MT catalytic activity via affecting SAM-binding or methyl transfer. Asp76 and 84 were located in the SAM-binding pocket, and Asp102 significantly affected SAM-binding via forming hydrogen bonds with SAM

Bethe states of the trigonometric SU(3) spin chain with generic open boundaries

By combining the algebraic Bethe ansatz and the off-diagonal Bethe ansatz, we investigate the trigonometric SU(3) model with generic open boundaries. The eigenvalues of the transfer matrix are given in terms of an inhomogeneous T-Q relation, and the corresponding eigenstates are expressed in terms of nested Bethe-type eigenstates which have well-defined homogeneous limit. This exact solution provides a basis for further analyzing the thermodynamic properties and correlation functions of the anisotropic models associated with higher rank algebras.Comment: 17 pages, 3 tables. arXiv admin note: text overlap with arXiv:1705.0947

Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination

Although unsupervised feature learning has demonstrated its advantages to reducing the workload of data labeling and network design in many fields, existing unsupervised 3D learning methods still cannot offer a generic network for various shape analysis tasks with competitive performance to supervised methods. In this paper, we propose an unsupervised method for learning a generic and efficient shape encoding network for different shape analysis tasks. The key idea of our method is to jointly encode and learn shape and point features from unlabeled 3D point clouds. For this purpose, we adapt HR-Net to octree-based convolutional neural networks for jointly encoding shape and point features with fused multiresolution subnetworks and design a simple-yet-efficient Multiresolution Instance Discrimination (MID) loss for jointly learning the shape and point features. Our network takes a 3D point cloud as input and output both shape and point features. After training, the network is concatenated with simple task-specific back-end layers and fine-tuned for different shape analysis tasks. We evaluate the efficacy and generality of our method and validate our network and loss design with a set of shape analysis tasks, including shape classification, semantic shape segmentation, as well as shape registration tasks. With simple back-ends, our network demonstrates the best performance among all unsupervised methods and achieves competitive performance to supervised methods, especially in tasks with a small labeled dataset. For fine-grained shape segmentation, our method even surpasses existing supervised methods by a large margin.Comment: Accepted by AAAI 2021. Code: https://github.com/microsoft/O-CNN/blob/master/docs/unsupervised.m

Thermodynamic limit and twisted boundary energy of the XXZ spin chain with antiperiodic boundary condition

We investigate the thermodynamic limit of the inhomogeneous T-Q relation of the antiferromagnetic XXZ spin chain with antiperiodic boundary condition. It is shown that the contribution of the inhomogeneous term at the ground state can be neglected when the system-size N tends to infinity, which enables us to reduce the inhomogeneous Bethe ansatz equations (BAEs) to the homogeneous ones. Then the quantum numbers at the ground states are obtained, by which the system with arbitrary size can be studied. We also calculate the twisted boundary energy of the system.Comment: 18 pages, 7 figure

Analytical model for predicting residual stresses in abrasive waterjet peening

Abrasive waterjet peening (AWJP) is a new mechanical surface treatment where particles are delivered by a waterjet to induce plastic deformation and achieve surface strengthening effects on a workpiece. Although fatigue strength can be improved by inducing compressive residual stress, the prediction of residual stress distribution remains challenging because particle–workpiece interaction occurs with randomicity, superposition, and overlapping. In this paper, a theoretical model is proposed for predicting workpiece plastic deformation and compressive residual stress by analysing i) the non-uniform energy distribution of the AWJP beam caused by the non-uniformity of the abrasive size, spatial distribution, and impact velocity; ii) material hardening among multiple impacts by abrasive particles; and iii) overlapping traces induced by the changing position of the AWJP beam. The AWJP experiments were conducted in single-pass/multiple-pass/multiple-overlapping footprints with different pump pressures, traverse speeds, and jet centre distances of the adjacent traces to validate the model. The results showed good agreement with the predicted surface roughness and compressive residual stress. Compressive residual stress increased with the pump pressure, whereas the effect of pump pressure change rate decreased when the pump pressure was increased; further, residual stress is nearly constant with the variation in traverse speed and jet centre distance of the adjacent traces when it decreases to a certain value. These results can act as references for the control of residual stress and the prediction model can aid industrial manufacturing in AWJP parameter optimisation (e.g. pump pressure, traverse speed, surface roughness, compressive residual stress, and centre distance between two adjacent traces)

Modelling and experimental study of surface treatment in abrasive waterjet peening of Nickel-based superalloy: Inverse problem

Abrasive waterjet peening (AWJP) is a promising method of surface treatment for modifying mechanical properties of components by introducing compressive residual stress (CRS) to a workpiece surface. Many efforts have been paid so far to modelling and optimisation of the AWJP process, however, most of these studies focus on the forward problems, i.e. estimating the CRS of workpiece surface according to processing parameters. There are still significant challenges in implanting different CRS at target areas in workpiece surface, which is the foundation of implanting uniform distribution of CRS on free-form surface or workpiece with uneven initial stress state. In this paper, a novel temporally and spatially controlled method for AWJP has been proposed, where the distribution of CRS can be adjusted by the optimisation of the abrasive waterjet parameters. That is, to achieve the AWJP system configuration for specific CRS on a target area, an inverse problem of CRS distribution has been modelled and solved, where the pump pressure, traverse speed and centre distance were optimised together to reach a prescribed CRS distribution. The proposed method was validated through experiments of implanting uniform distribution and non-uniform distribution of CRS at target areas. The results revealed that the maximum error between target and experimental CRS was only 14.25% in 18 sets of experiments. In addition, microstructure analysis of the AWJP surface suggested that a relatively low pump pressure and traverse speed can be selected to induce grain refinement and strain hardening layer on the workpiece surface without cracks and heavy surface topography fluctuations