bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing UI layouts for deformable face-rig manipulation
Authors: Kim Joonho , Singh Karan
Published: July 2021

Complex deformable face-rigs have many independent parameters that control the shape of the object. A human face has upwards of 50 parameters (FACS Action Units), making conventional UI controls hard to find and operate. Animators address this problem by tediously hand-crafting in-situ layouts of UI controls that serve as visual deformation proxies, and facilitate rapid shape exploration. We propose the automatic creation of such in-situ UI control layouts. We distill the design choices made by animators into mathematical objectives that we optimize as the solution to an integer quadratic programming problem. Our evaluation is three-fold: we show the impact of our design principles on the resulting layouts; we show automated UI layouts for complex and diverse face rigs, comparable to animator handcrafted layouts; and we conduct a user study showing our UI layout to be an effective approach to face-rig manipulation, preferable to a baseline slider interface.
Scalable image-based indoor scene rendering with reflections
Authors: Xu Jiamin , Wu Xiuchao , Zhu Zihan , Huang Qixing , Yang Yin , Bao Hujun , Xu Weiwei
Published: July 2021

This paper proposes a novel scalable image-based rendering (IBR) pipeline for indoor scenes with reflections. We make substantial progress towards three sub-problems in IBR, namely, depth and reflection reconstruction, view selection for temporally coherent view-warping, and smooth rendering refinements. First, we introduce a global-mesh-guided alternating optimization algorithm that robustly extracts a two-layer geometric representation. The front and back layers encode the RGB-D reconstruction and the reflection reconstruction, respectively. This representation minimizes the image composition error under novel views, enabling accurate renderings of reflections. Second, we introduce a novel approach to select adjacent views and compute blending weights for smooth and temporal coherent renderings. The third contribution is a supersampling network with a motion vector rectification module that refines the rendering results to improve the final output's temporal coherence. These three contributions together lead to a novel system that produces highly realistic rendering results with various reflections. The rendering quality outperforms state-of-the-art IBR or neural rendering algorithms considerably.
An inverse method for the exploration of layered material appearance
Authors: Bati Mégane , Barla Pascal , Pacanowski Romain
Published: July 2021

Layered materials exhibit a wide range of appearance, due to the combined effects of absorption and scattering at and between interfaces. Yet most existing approaches let users set the physical parameters of all layers by hand, a process of trial and error. We introduce an inverse method that provides control over BRDF lobe properties of layered materials, while automatically retrieving compatible physical parameters. Our method permits to explore the space of layered material appearance: it lets users find configurations with nearly indistinguishable appearance, isolate grazing angle effects, and give control over properties such as the color, blur or haze of reflections.
Knitting 4D garments with elasticity controlled for body motion
Authors: Liu Zishun , Han Xingjian , Zhang Yuchen , Chen Xiangjia , Lai Yu-Kun , Doubrovski Eugeni L. , Whiting Emily , Wang Charlie C. L.
Published: July 2021

In this paper, we present a new computational pipeline for designing and fabricating 4D garments as knitwear that considers comfort during body movement. This is achieved by careful control of elasticity distribution to reduce uncomfortable pressure and unwanted sliding caused by body motion. We exploit the ability to knit patterns in different elastic levels by
single-jersey jacquard
(SJJ) with two yarns. We design the distribution of elasticity for a garment by physics-based computation, the optimized elasticity on the garment is then converted into instructions for a digital knitting machine by two algorithms proposed in this paper. Specifically, a graph-based algorithm is proposed to generate knittable stitch meshes that can accurately capture the 3D shape of a garment, and a tiling algorithm is employed to assign SJJ patterns on the stitch mesh to realize the designed distribution of elasticity. The effectiveness of our approach is verified on simulation results and on specimens physically fabricated by knitting machines.
Unsupervised learning for cuboid shape abstraction via joint segmentation from point clouds
Authors: Yang Kaizhi , Chen Xuejin
Published: July 2021

Representing complex 3D objects as simple geometric primitives, known as shape abstraction, is important for geometric modeling, structural analysis, and shape synthesis. In this paper, we propose an unsupervised shape abstraction method to map a point cloud into a compact cuboid representation. We jointly predict cuboid allocation as part segmentation and cuboid shapes and enforce the consistency between the segmentation and shape abstraction for self-learning. For the cuboid abstraction task, we transform the input point cloud into a set of parametric cuboids using a variational auto-encoder network. The segmentation network allocates each point into a cuboid considering the point-cuboid affinity. Without manual annotations of parts in point clouds, we design four novel losses to jointly supervise the two branches in terms of geometric similarity and cuboid compactness. We evaluate our method on multiple shape collections and demonstrate its superiority over existing shape abstraction methods. Moreover, based on our network architecture and learned representations, our approach supports various applications including structured shape generation, shape interpolation, and structural shape clustering.
A momentum-conserving implicit material point method for surface tension with contact angles and spatial gradients
Authors: Chen Jingyu , Kala Victoria , Marquez-Razon Alan , Gueidon Elias , Hyde David A. B. , Teran Joseph
Published: July 2021

We present a novel Material Point Method (MPM) discretization of surface tension forces that arise from spatially varying surface energies. These variations typically arise from surface energy dependence on temperature and/or concentration. Furthermore, since the surface energy is an interfacial property depending on the types of materials on either side of an interface, spatial variation is required for modeling the contact angle at the triple junction between a liquid, solid and surrounding air. Our discretization is based on the surface energy itself, rather than on the associated traction condition most commonly used for discretization with particle methods. Our energy based approach automatically captures surface gradients without the explicit need to resolve them as in traction condition based approaches. We include an implicit discretization of thermomechanical material coupling with a novel particle-based enforcement of Robin boundary conditions associated with convective heating. Lastly, we design a particle resampling approach needed to achieve perfect conservation of linear and angular momentum with Affine-Particle-In-Cell (APIC) [Jiang et al. 2015]. We show that our approach enables implicit time stepping for complex behaviors like the Marangoni effect and hydrophobicity/hydrophilicity. We demonstrate the robustness and utility of our method by simulating materials that exhibit highly diverse degrees of surface tension and thermomechanical effects, such as water, wine and wax.
Learning contact corrections for handle-based subspace dynamics
Authors: Romero Cristian , Casas Dan , Pérez Jesús , Otaduy Miguel
Published: July 2021

This paper introduces a novel subspace method for the simulation of dynamic deformations. The method augments existing linear handle-based subspace formulations with nonlinear learning-based corrections parameterized by the same subspace. Together, they produce a compact nonlinear model that combines the fast dynamics and overall contact-based interaction of subspace methods, with the highly detailed deformations of learning-based methods. We propose a formulation of the model with nonlinear corrections applied on the local undeformed setting, and decoupling internal and external contact-driven corrections. We define a simple mapping of these corrections to the global setting, an efficient implementation for dynamic simulation, and a training pipeline to generate examples that efficiently cover the interaction space. Altogether, the method achieves unprecedented combination of speed and contact-driven deformation detail.
Physical validation of simulators in computer graphics
Authors: Romero Victor , Ly Mickaël , Rasheed Abdullah Haroon , Charrondière Raphaël , Lazarus Arnaud , Neukirch Sébastien , Bertails-Descoubes Florence
Published: July 2021

We introduce a selected set of protocols inspired from the Soft Matter Physics community in order to validate Computer Graphics simulators of slender elastic structures possibly subject to dry frictional contact. Although these simulators were primarily intended for feature film animation and visual effects, they are more and more used as virtual design tools for predicting the shape and deformation of real objects; hence the need for a careful, quantitative validation. Our tests, experimentally verified, are designed to evaluate carefully the predictability of these simulators on various aspects, such as bending elasticity, bend-twist coupling, and frictional contact. We have passed a number of popular codes of Computer Graphics through our benchmarks by defining a rigorous, consistent, and as fair as possible methodology. Our results show that while some popular simulators for plates/shells and frictional contact fail even on the simplest scenarios, more recent ones, as well as well-known codes for rods, generally perform well and sometimes even better than some reference commercial tools of Mechanical Engineering. To make our validation protocols easily applicable to any simulator, we provide an extensive description of our methodology, and we shall distribute all the necessary model data to be compared against.
ChoreoMaster
Authors: Chen Kang , Tan Zhipeng , Lei Jin , Zhang Song-Hai , Guo Yuan-Chen , Zhang Weidong , Hu Shi-Min
Published: July 2021

Despite strong demand in the game and film industry, automatically synthesizing high-quality dance motions remains a challenging task. In this paper, we present ChoreoMaster, a production-ready music-driven dance motion synthesis system. Given a piece of music, ChoreoMaster can automatically generate a high-quality dance motion sequence to accompany the input music in terms of style, rhythm and structure. To achieve this goal, we introduce a novel choreography-oriented choreomusical embedding framework, which successfully constructs a unified choreomusical embedding space for both style and rhythm relationships between music and dance phrases. The learned choreomusical embedding is then incorporated into a novel choreography-oriented graph-based motion synthesis framework, which can robustly and efficiently generate high-quality dance motions following various choreographic rules. Moreover, as a production-ready system, ChoreoMaster is sufficiently controllable and comprehensive for users to produce desired results. Experimental results demonstrate that dance motions generated by ChoreoMaster are accepted by professional artists.
Unconventional patterns on surfaces
Authors: Meekes Merel , Vaxman Amir
Published: July 2021

We present a unified method to meshing surfaces with unconventional patterns, both periodic and aperiodic. These patterns, which have so far been studied on the plane, are patterns comprising a small number of tiles, that do not necessarily exhibit translational periodicity. Our method generalizes the de Bruijn multigrid method to the discrete setting, and thus reduces the problem to the computation of
N
-Directional fields on triangle meshes. We work with all cases of directional symmetries that have been little studied, including odd and high
N.
We address the properties of such patterns on surfaces and the challenges in their construction, including order-preservation, seamlessness, duality, and singularities. We show how our method allows for the design of original and unconventional meshes that can be applied to architectural, industrial, and recreational design.
Computational design of weingarten surfaces
Authors: Pellis Davide , Kilian Martin , Pottmann Helmut , Pauly Mark
Published: July 2021

In this paper we study Weingarten surfaces and explore their potential for fabrication-aware design in freeform architecture. Weingarten surfaces are characterized by a functional relation between their principal curvatures that implicitly defines approximate local congruences on the surface. These symmetries can be exploited to simplify surface paneling of double-curved architectural skins through mold re-use.

We present an optimization approach to find a Weingarten surface that is close to a given input design. Leveraging insights from differential geometry, our method aligns curvature isolines of the surface in order to contract the curvature diagram from a 2D region into a 1D curve. The unknown functional curvature relation then emerges as the result of the optimization. We show how a robust and efficient numerical shape approximation method can be implemented using a guided projection approach on a high-order B-spline representation. This algorithm is applied in several design studies to illustrate how Weingarten surfaces define a versatile shape space for fabrication-aware exploration in freeform architecture. Our optimization algorithm provides the first practical tool to compute general Weingarten surfaces with arbitrary curvature relation, thus enabling new investigations into a rich, but as of yet largely unexplored class of surfaces.
Bistable auxetic surface structures
Authors: Chen Tian , Panetta Julian , Schnaubelt Max , Pauly Mark
Published: July 2021

We present
Bistable Auxetic Surface Structures
, a novel deployable material system based on optimized bistable auxetic cells. Such a structure can be flat-fabricated from elastic sheet material, then deployed towards a desired double-curved target shape by activating the bistable mechanism of its component cells. A unique feature is that the deployed model is by design in a stable state. This facilitates deployment without the need of complex external supports or boundary constraints.

We introduce a computational solution for the inverse design of our Bistable Auxetic Surface Structures. Our algorithm first precomputes a library of bistable auxetic cells to cover a range of in-plane expansion / contraction ratios, while maximizing the bistability and stiffness of the cell to ensure robust deployment. We then use metric distortion analysis of the target surface to compute the planar fabrication state as a composition of cells that best matches the desired deployment deformation. As each cell expands or contracts during deployment, metric frustration forces the surface towards its target equilibrium state. We validate our method with several physical prototypes.
Reliable feature-line driven quad-remeshing
Authors: Pietroni Nico , Nuvoli Stefano , Alderighi Thomas , Cignoni Paolo , Tarini Marco
Published: July 2021

We present a new algorithm for the semi-regular quadrangulation of an input surface, driven by its line features, such as sharp creases. We define a perfectly feature-aligned cross-field and a coarse layout of polygonal-shaped patches where we strictly ensure that all the feature-lines are represented as patch boundaries. To be able to consistently do so, we allow non-quadrilateral patches and T-junctions in the layout; the key is the ability to constrain the layout so that it still admits a globally consistent, T-junction-free, and pure-quad internal tessellation of its patches. This requires the insertion of additional irregular-vertices inside patches, but the regularity of the final-mesh is safeguarded by optimizing for both their number and for their reciprocal alignment. In total, our method guarantees the reproduction of feature-lines by construction, while still producing good quality, isometric, pure-quad, conforming meshes, making it an ideal candidate for CAD models. Moreover, the method is fully automatic, requiring no user intervention, and remarkably reliable, requiring little assumptions on the input mesh, as we demonstrate by batch processing the entire Thingi10K repository, with less than 0.5% of the attempted cases failing to produce a usable mesh.
WRAPD
Authors: Brown George E. , Narain Rahul
Published: July 2021

Local-global solvers such as ADMM for elastic simulation and geometry optimization struggle to resolve large rotations such as bending and twisting modes, and large distortions in the presence of barrier energies. We propose two improvements to address these challenges. First, we introduce a novel local-global splitting based on the polar decomposition that separates the geometric nonlinearity of rotations from the material nonlinearity of the deformation energy. The resulting ADMM-based algorithm is a combination of an L-BFGS solve in the global step and proximal updates of element stretches in the local step. We also introduce a novel method for dynamic reweighting that is used to adjust element weights at runtime for improved convergence. With both improved rotation handling and element weighting, our algorithm is considerably faster than state-of-the-art approaches for quasi-static simulations. It is also much faster at making early progress in parameterization problems, making it valuable as an initializer to jump-start second-order algorithms.
Modeling Temporal Patterns with Dilated Convolutions for Time-Series Forecasting
Authors: Li Yangfan , Li Kenli , Chen Cen , Zhou Xu , Zeng Zeng , Li Keqin
Published: July 2021

Time-series forecasting is an important problem across a wide range of domains. Designing accurate and prompt forecasting algorithms is a non-trivial task, as temporal data that arise in real applications often involve both non-linear dynamics and linear dependencies, and always have some mixtures of sequential and periodic patterns, such as daily, weekly repetitions, and so on. At this point, however, most recent deep models often use Recurrent Neural Networks (RNNs) to capture these temporal patterns, which is hard to parallelize and not fast enough for real-world applications especially when a huge amount of user requests are coming. Recently, CNNs have demonstrated significant advantages for sequence modeling tasks over the de-facto RNNs, while providing high computational efficiency due to the inherent parallelism. In this work, we propose HyDCNN, a novel hybrid framework based on fully Dilated CNN for time-series forecasting tasks. The core component in HyDCNN is a proposed hybrid module, in which our proposed position-aware dilated CNNs are utilized to capture the sequential non-linear dynamics and an autoregressive model is leveraged to capture the sequential linear dependencies. To further capture the periodic temporal patterns, a novel hop scheme is introduced in the hybrid module. HyDCNN is then composed of multiple hybrid modules to capture the sequential and periodic patterns. Each of these hybrid modules targets on either the sequential pattern or one kind of periodic patterns. Extensive experiments on five real-world datasets have shown that the proposed HyDCNN is better compared with state-of-the-art baselines and is at least 200% better than RNN baselines. The datasets and source code will be published in Github to facilitate more future work.
A cache consolidation design of MLC STT-RAM for energy efficiency enhancement on cyber-physical systems
Authors: Liang Yu-Pei , Chen Shuo-Han , Chang Yuan-Hao , Liu Yun-Fei , Wei Hsin-Wen , Shih Wei-Kuan
Published: July 2021

Owing to the energy-constraint nature of cyber-physical systems (CPS), energy efficiency has become a primary design consideration for CPS. On CPS, owing to the high leakage power issue of SRAM, the major portion of its energy consumption comes from static random-access memory (SRAM)-based processors. Recently, with the emerging and rapidly evolving nonvolatile Spin-Transfer Torque RAM (STT-RAM), STT-RAM is expected to replace SRAM within processors for enhancing the energy efficiency with its near-zero leakage power features. The advances in Magnetic Tunneling Junction (MTJ) technology also realize the multi-level cell (MLC) STT-RAM to pack more cells with the same die area for achieving the memory density. However, the write disturbance issue of MLC STT-RAM prevents STT-RAM from properly resolving the energy efficiency of CPS. Although studies have been proposed to alleviate this issue, previous strategies could induce additional management overhead due to the use of counters or lead to frequent swap operations. Such an observation motivates us to propose an effective and simple strategy to combine direct and split cache mapping designs to enhance the energy efficiency of MLC STT-RAM. A series of experiments have been conducted on an open-source emulator with encouraging results.
Effect of Electronic Conductivity on the Polarization Behavior of Li[Li1/3Ti5/3]O4 Electrodes
Authors: Ariyoshi Kingo , Ino Takaya , Yamada Yusuke
Published: July 2021

Along with structural factors such as thickness and porosity, the conductive additive in lithium insertion electrodes in lithium-ion batteries influences the rate capability. The addition of a conductive additive improves the electronic conductivity of the electrodes, necessitating the optimization of such additives. However, the relationship of conductive additives to the electronic conductivity and rate capability of an electrode cannot be explained using classical electrochemical theory. The effect of the electronic conductivity of electrodes on polarization was investigated using Li[Li1/3Ti5/3]O4−pellet electrodes, whose electronic conductivity can be modified using a graphite conductive additive during an oxidation/reduction reaction. Consequently, the polarization behavior of the Li[Li1/3Ti5/3]O4−pellet electrodes without the conductive additive is asymmetric during the reduction and oxidation reactions, while it is symmetrical in the electrodes with the conductive additive. Furthermore, the polarization voltage is significantly lower in the electrodes with the conductive additive. Herein, we propose a rational explanation for the observed relationship between the electronic conductivity and polarization of electrodes using the formation and disappearance of electron conduction paths. This explanation would help us understand the enhancement of the rate capabilities of electrodes with poor electronic conductivity using conductive additives by the construction of the electron conduction paths.
[Retracted] Evaluation of Mechanical Properties of Materials Based on Genetic Algorithm Optimizing BP Neural Network
Authors: Liu Tianzeng , Zou Guangping
Published: July 2021

In the 21st century, with the increasingly urgent requirements for lightweight in the fields of aviation, aerospace, and electronics, especially automobiles, many properties of magnesium alloy materials, especially the low‐density performance characteristics, have been widely valued. In order to better use magnesium metal materials, it is very important to evaluate its mechanical properties. This article is based on 196 sets of mechanical performance experimental results and related data of AZ31 and AZ91 2 magnesium alloys. Based on data analysis and sorting, take deformation temperature, deformation rate, deformation coefficient, solid solution temperature, and solid solution time as input and take ultimate tensile strength (UTS), yield strength (YS), and elongation (ELO) as output. The 5‐8‐1 three‐layer BP neural network forecast model optimized by the genetic algorithm is used for data training. The training results show that the prediction model can accurately predict the tensile strength, yield strength, and elongation. Compared with the general BP neural network prediction model, the BP neural network based on the genetic algorithm has small discreteness and high fitness: the average error of UTS and YS of AZ31 magnesium alloy is reduced to 0.88% and 3.3%, respectively. The most obvious is that the elongation of AZ31 ELO is reduced, and the error is reduced to 8.1%.
Intelligent Segmentation Algorithm for Diagnosis of Meniere’s Disease in the Inner Auditory Canal Using MRI Images with Three-Dimensional Level Set
Authors: Liu Ting , Xu Ying , An Yujuan , Ge Hongzhou
Published: July 2021

This paper aimed to explore segmentation effects of the magnetic resonance imaging (MRI) images of the inner auditory canal of patients with Meniere’s disease under the intelligent segmentation method of the inner ear based on three-dimensional (3D) level set (IS3DLS). The statistical shape model and the level set segmentation algorithm were combined to propose the IS3DLS. First, the shape training samples of the inner ear model were determined, and the results were manually segmented to further obtain region of interest (ROI) of the inner ear. The IS3DLS was employed to accurately segment MRI images of the inner auditory canal of patients with Meniere’s disease. The segmentation performance of IS3DLS was compared with the expert manual segmentation method and the region growth level set-based segmentation algorithm. Results showed that Matthews correlation coefficient (MCC), Dice similarity coefficient (DSC), false positive rate (FPR), and false negative rate (FNR) of this algorithm were 0.9599, 0.9594, 0.0325, and 0.03655, respectively. Therefore, the IS3DLS could achieve good segmentation effect in MRI images of the inner auditory canal of patients with Meniere’s disease, which was helpful for diagnosis and subsequent treatment of Meniere’s disease.
Image Mosaic Algorithm-Based Analysis of Effects of Low-Dose X-Rays on Blood Immune Cells
Authors: Fu Xiaohua , Miao Fei , Guo Jianbin , He Weijuan , Deng Yuying , Wang Yushan , Zeng Xiaxiang , Wang Wenbing , Liu Yunfeng
Published: July 2021

This work aimed to explore the effects of image mosaic algorithm- (IMA-) based low-dose X-rays on deoxyribonucleic acid (DNA) damage of peripheral blood lymphocytes in cardiovascular patients. 84 patients with interventional cardiovascular surgeries were selected as the research objects and divided into coronary intervention surgery (A) group, coronary angiography (B) group, radiofrequency ablation (C) group, and permanent cardiac pacemaker implantation (D) group. They were treated with IMA-based low-dose X-rays, DNA damage indicators of lymphocytes were detected, and the relationship was analyzed between those indicators and radiation dose. Results showed that the cumulative dose (CD) and dose area product (DAP) of group A were the lowest, and those of group D were obviously higher than those of the other three groups (

P
<
0.01

). Besides, the results also showed that the values of CD and DAP of patients from group A and group D were 0.38 ± 0.08 (Gy) and 0.38 ± 0.08 (Gy/cm2) and 1.37 ± 0.21 (Gy) and 1.37 ± 0.21(Gy/cm2), respectively. The comet tail DNA content (TDNA) and tail moment (TM) of each group after surgery increased dramatically compared with before surgery (

P
<
0.01

), and the TDNA and TM of group D were greatly higher than those of the other three groups (

P
<
0.01

). Therefore, TDNA and TM were markedly positively correlated with DAP and CD (

P
<
0.01

). IMA-based X-ray radiotherapy caused reliable DNA damage to peripheral blood lymphocytes of cardiovascular patients, and the greater the radiation dose, the more serious the DNA damage.
A Single Target Grasp Detection Network Based on Convolutional Neural Network
Authors: Zhang Longzhi , Wu Dongmei
Published: July 2021

Grasp detection based on convolutional neural network has gained some achievements. However, overfitting of multilayer convolutional neural network still exists and leads to poor detection precision. To acquire high detection accuracy, a single target grasp detection network that generalizes the fitting of angle and position, based on the convolution neural network, is put forward here. The proposed network regards the image as input and grasping parameters including angle and position as output, with the detection manner of end‐to‐end. Particularly, preprocessing dataset is to achieve the full coverage to input of model and transfer learning is to avoid overfitting of network. Importantly, a series of experimental results indicate that, for single object grasping, our network has good detection results and high accuracy, which proves that the proposed network has strong generalization in direction and category.
Methionine Protects Mammary Cells against Oxidative Stress through Producing S‐Adenosylmethionine to Maintain mTORC1 Signaling Activity
Authors: Zhong Heju , Yuan Peiqiang , Li Yunxia , Batonon-Alavo Dolores , Deschamps Caroline , Feng Bin , Zhang Xiaoling , Che Lianqiang , Lin Yan , Xu Shengyu , Li Jian , Zhuo Yong , Tian Gang , Tang Jiayong , Jiang Xuemei , Huang Lingjie , Wu Caimei , Wu De , Fang Zhengfeng
Published: July 2021

The mechanistic target of rapamycin complex 1 (mTORC1) signaling plays pivotal roles in cell growth and diseases. However, it remains mechanistically unclear about how to maintain mTORC1 activity during mammary glands development. Here we showed that mammary glands suffered from aggravated oxidative stress as pregnancy advanced and was accompanied by an increase in H2O2 levels, while the consumption for methionine and S‐adenosylmethionine (SAM) rather than S‐adenosylhomocysteine (SAH) were promoted in vivo. Likewise, H2O2 promoted SAM synthesis and reduced SAM utilization for methylation depending on H2O2 levels and treatment time in vitro. H2O2 inhibited phosphorylation of S6 kinase Thr 389 (p‐S6K1 (T389)), 4E‐BP1 Thr 37/46 and ULK1 Ser 757, the downstream of mTORC1, in mammary epithelial cells. However, methionine and SAM were shown to activate mTORC1 under H2O2‐exposed condition. Moreover, this effect was not disabled by SGI‐1027 which inhibits SAM transmethylation. In conclusion, methionine appeared to protect mammary cells against oxidative stress through producing SAM to maintain mTORC1 signaling activity.
Random Dynamic Response Characteristics of Pipeline Subjected to Blasting Cylindrical SH Waves
Authors: Lu Shiwei , Zhang Heng , Ji Ling
Published: July 2021

It is essential to investigate the influence of blasting vibrations on pipelines, and the dynamic response is the crux in the safety issues. At present, the blasting seismic wave is usually regarded as a plane wave. However, there is little research about the dynamic response characteristics of underground structures subjected to nonplane waves. The analytical solution to dynamic stress concentration factor (DSCF) of pipelines subjected to cylindrical SH wave was derived. Besides, the randomness of the shear modulus of soil was considered, and the statistical analysis of DSCF was carried out by the Monte Carlo simulation method. Results show that the variability of the shear modulus of soil has a significant influence on the probability distribution of DSCF. The larger the variation coefficient of the shear modulus is, the more obvious the skewness of DSCF is. The influence of low‐frequency wave on pipeline increases with the reducing normalized distance r∗, while the influence of high‐frequency wave reduces and the variation amplitude of DSCF increases. Compared with the DSCF of pipe subjected to a plane wave, a lower dominant frequency or larger normalized distance for the cylindrical SH wave will generate a more similar statistical characteristic of DSCF.
PERK-Dependent Activation of the JAK2/STAT3 Pathway Contributes to High Glucose-Induced Extracellular Matrix Deposition in Renal Tubular Epithelial Cells
Authors: Bao Yan , Liang Wei , Ye Yingchun , Yi Bo
Published: July 2021

Background. Although the deposition of extracellular matrix (ECM) is critical leading to tubular damage in diabetic kidney disease (DKD), the mechanism still remains unclear. The purpose of this study was to demonstrate a role for protein kinase R-like endoplasmic reticulum kinase (PERK) (a protein located in the endoplasmic reticulum membrane) in this pathologic process. Methods. NRK-52E cells were grown in the media containing different concentrations of glucose or thapsigargin for different durations. Cells were subsequently incubated with or without AG490, a selective inhibitor of Janus kinase 2 (JAK2) or GSK2606414 (a selective PERK inhibitor). We evaluated the production of TGF-β1, fibronectin, and collagen I proteins by ELISA. The levels of 78 kD-glucose-regulated protein (GRP78) and PERK, as well as the phosphorylation statues of PERK and JAK2/signal transducer and activator of transcription (STAT3), were determined by western blotting analysis. Results. We showed that the increased phosphorylation of JAK2 and STAT3 was accompanied by overexpression of TGF-β1 and ECM deposition in high glucose medium. Disruption of the JAK2/STAT3 pathway with AG490 significantly prevents the high glucose-induced increase in TGF-β1, fibronectin, and collagen I. High glucose induced the overproduction of GRP78 and phosphorylation of PERK, which indicated that endoplasmic reticulum stress (ERS) was triggered in NRK-52E cells cultured under high glucose condition. Inhibition of PERK phosphorylation with GSK2606414, however, blocked the effect of JAK2/STAT3 on the production of TGF-β1 and ECM components in NRK-52E cells. Conclusion. Our data indicated that the ECM accumulation induced by high glucose arouse via the PERK-dependent JAK2/STAT3-signaling pathway in renal tubular epithelial cells.
Forecast of E‐Commerce Transactions Trend Using Integration of Enhanced Whale Optimization Algorithm and Support Vector Machine
Authors: Zhang Suqi , Lin Hsiung-Cheng , Wang Xinxin
Published: July 2021

E‐commerce has become a crucial business model through the Internet around the world. Therefore, its transaction trend forecast can provide important information for the market planning and development in advance. For this purpose, the integrated model of enhanced whale optimization algorithm (EWOA) with support vector machine (SVM) is proposed for forecast of E‐commerce transaction trend in this study. First, the global optimization ability of the whale optimization algorithm (WOA) is enhanced by the search updating strategy. Second, multiple factors that may affect the E‐commerce transaction trend are analyzed and determined using the gray correlation mechanism. Third, the EWOA algorithm is employed to optimize the SVM random parameters. Finally, the EWOA‐SVM model is established for forecasting E‐commerce transaction trend. Two representative cases tests confirm that the EWOA‐SVM model is superior to other existing methods in terms of fast convergence speed and high prediction accuracy.
Solving Bisymmetric Solution of a Class of Matrix Equations Based on Linear Saturated System Model Neural Network
Authors: Zhang Feng
Published: July 2021

In order to solve the complicated process and low efficiency and low accuracy of solving a class of matrix equations, this paper introduces the linear saturated system model neural network architecture to solve the bisymmetric solution of a class of matrix equations. Firstly, a class of matrix equations is constructed to determine the key problems of solving the equations. Secondly, the linear saturated system model neural network structure is constructed to determine the characteristic parameters in the process of bisymmetric solution. Then, the matrix equations is solved by using backpropagation neural network topology. Finally, the class normalization is realized by using the objective function of bisymmetric solution, and the bisymmetric solution of a class of matrix equations is realized. In order to verify the solving effect of the method in this paper, three indexes (accuracy, correction accuracy, and solving time) are designed in the experiment. The experimental results show that the proposed method can effectively reduce the solving time, can improve the accuracy and correction effect of the bisymmetric solution, and has high practicability.
Nonlinear Vibration Analysis for Stiffened Cylindrical Shells Subjected to Electromagnetic Environment
Authors: Li Xue-Qin , Bai Guang-Chen , Song Lu-Kai , Zhang Wei
Published: July 2021

The nonlinear vibration behaviors of stiffened cylindrical shells under electromagnetic excitations, transverse excitations, and in‐plane excitations are studied for the first time in this paper. Given the first‐order shear deformation theory and Hamilton principle, the nonlinear partial differential governing equations of motion are derived with considering the von Karman geometric nonlinearity. By employing the Galerkin discretization procedure, the partial differential equations are diverted to a set of coupled nonlinear ordinary differential equations of motion. Based on the case of 1 : 2 internal resonance and principal resonance‐1/2 subharmonic parametric resonance, the multiscale method of perturbation analysis is employed to precisely acquire the four‐dimensional nonlinear averaged equations. From the resonant response analysis and nonlinear dynamic simulation, we discovered that the unstable regions of stiffened cylindrical shells can be narrowed by decreasing the external excitation or increasing the magnetic intensity, and their working frequency range can be expanded by reducing the in‐plane excitation. Moreover, the different nonlinear dynamic responses of the stiffened cylindrical shell are acquired by controlling stiffener number, stiffener size, and aspect ratio. The presented approach in this paper can provide an eﬃcient analytical framework for nonlinear dynamics theories of stiffened cylindrical shells and will shed light on complex structure design in vibration test engineering.
Permanent Flank Bulge after Flank Incision: Patient- and Physician-Reported Outcome
Authors: Kranz Jennifer , Grundl Sebastian , Wußow Friederike , Steffens Joachim , Anheuser Petra , Schneidewind Laila
Published: July 2021

Introduction: The aim of this study was to determine the incidence and risk factors for postoperative flank bulging and associated physiologic/psychologic consequences as well as to establish a clinical flank bulge classification system after open retroperitoneal surgery. Methods: In this retrospective study, a postal patient survey was sent to 240 patients who underwent open retroperitoneal surgery between 2007 and 2017. Patients, who reported a flank bulge, were invited for further evaluation, which included a clinical examination and standardized photo documentation. Results: Forty-three of 120 patients (35.8%) reported a flank bulging after retroperitoneal surgery. During clinical examination, a flank bulge could be confirmed in 25 patients, whereas in 18 patients, no bulging could be detected, leading to a corrected rate of flank bulge-positive patients of 20.8%. The corresponding relaxation values ranged from 1 to 1.44 and correlated with the clinical degree of bulging. A body mass index of ≥25 was identified as a risk factor. No correlation was found regarding age, gender, surgery side, access to the retroperitoneum, surgical procedure, and pathology. Thirty-seven patients complained about chronic pain or suffered from the cosmetic impact of bulging. Thirteen of those patients had shown a flank bulge during clinical examination, resulting in a symptomatic bulge rate of 10.8% (13/120 patients). Conclusion: Chronic pain and postoperative flank bulging are 2 of the most common long-term complications after open retroperitoneal access. If an open retroperitoneal approach is required, particularly obese patients should be thoroughly informed about the risk of flank bulging and chronic pain.
Tumor-Suppressing STF cDNA 3 Overexpression Suppresses Renal Fibrosis by Alleviating Anoikis Resistance and Inhibiting the PI3K/Akt Pathway
Authors: Xiao Fei , Liu Xinghong , Chen Yan , Dai Huanzi
Published: July 2021

Background: Myofibroblast (MF) activation is the key event of irreversible renal interstitial fibrosis. Anoikis resistance is the hallmark of active MFs, which is conferred by continuous activation of the phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)/protein kinase B (Akt) pathway. Our previous study found that tumor-suppressing STF cDNA 3 (TSSC3) enhances the sensitivity of cells to anoikis via the PI3K/Akt pathway. Therefore, we hypothesized that TSSC3 might suppress renal interstitial fibrosis by inducing anoikis via the PI3K/Akt pathway. Methods: Cell anoikis was induced by the exogenous addition of RGD-containing peptides or by culturing cells in suspension. MFs were established by stimulating HK-2 renal tubular epithelial cells with transforming growth factor beta 1 (TGF-β1). Lentivirus vectors were to construct a TSSC3 overexpression cell model. The effects of TSSC3 on the anoikis, growth, migration, invasion, and contraction of MFs were determined using annexin V-fluorescein isothiocyanate assays, cell counting kit-8 assays, wound healing migration assays, matrigel invasion assays, and collagen-based contraction assays. Results: The results demonstrated that TGF-β1, simultaneous with the induction of MF differentiation, confers significant protection against anoikis-induced cell death, which could be partly reversed by treatment with the PI3K/Akt pathway inhibitor, LY294002. Moreover, overexpression of TSSC3 obviously impaired cell growth, cell migration, cell invasion, contraction, and anoikis resistance of MFs, and decreased the activity of the PI3K/Akt pathway and the production of extracellular matrix molecules, all of which could be attenuated by treatment with the PI3K/Akt pathway activator, 740Y-P. Taken together, this study suggested that TSSC3 attenuates the anoikis resistance and profibrogenic ability of TGF-β1-induced MF by regulating the PI3K-Akt pathway. Conclusion: These findings provide a biological basis for further exploration of the therapeutic significance of targeting MF via TSSC3 in renal interstitial fibrosis.
Eosinophilic Reaction at the Time of Catheter Insertion Predicts Survival in Patients Initiating Peritoneal Dialysis
Authors: Uchiyama Kiyotaka , Washida Naoki , Kusahana Ei , Nakayama Takashin , Morimoto Kohkichi , Itoh Hiroshi
Published: July 2021

Introduction: Despite excellent biocompatibility, insertion of silicone peritoneal dialysis (PD) catheters can induce minor foreign body reaction, affecting long-term patient outcomes. We evaluated the effect of eosinophilic reaction associated with PD catheter insertion on outcomes of patients initiating PD. Methods: Eosinophilic reaction to PD catheter insertion was defined as the ratio of peripheral blood eosinophil count at 1 week after insertion (E1W) to pre-insertion eosinophil count (E0), and the association of E1W/E0 with technique survival, peritonitis-free survival, and heart failure (HF)-related hospitalization-free survival was analyzed. Results: This retrospective cohort study included 116 patients (89 male patients) who underwent PD catheter insertion between January 2008 and June 2018 (61.3 ± 12.9 years). The follow-up duration was 46.2 (23.8–75.3) months. E1W was significantly higher than E0 (median, 333 vs. 234/μL, p < 0.001), with a median E1W/E0 of 1.54. The log-rank test showed that technique survival, peritonitis-free survival, and HF-related hospitalization-free survival were significantly better in patients with E1W/E0 < 1.54 than in those with E1W/E0 ≥ 1.54 (p = 0.002, <0.001, and <0.001, respectively). By the Cox regression analysis adjusted for age, sex, the Charlson comorbidity index, the estimated glomerular filtration rate, and the geriatric nutritional risk index, E1W/E0 remained a significant risk factor for technique failure, peritonitis, and hospitalization for HF (hazard ratio (HR) 1.68, p = 0.01; HR 2.19, p < 0.001; HR 2.15, p < 0.001, respectively). Conclusion: Eosinophilic reaction at the time of PD catheter insertion is a novel marker that may predict outcomes in patients initiating PD.
The Effects of 8-Week Combined Exercise Training on Inflammatory Markers in Women with Multiple Sclerosis
Authors: Tadayon Zadeh Fahime , Amini Hamid , Habibi Saeed , Shahedi Valiallah , Isanejad Amin , Akbarpour Mohsen
Published: July 2021

Purpose: The present study was designed to investigate the effects of 8-week combined endurance, resistance, and balance exercise training on IL-6, CRP, and IL-10 concentrations in women with multiple sclerosis. Methods: Thirty participants with multiple sclerosis (Expanded Disability Status Scale ≤6) were randomized into either an exercise and control groups. The exercise group performed 8-weeks of endurance, resistance, and balance exercise training. Serum concentrations of IL-6, CRP, and IL-10 were measured before and after the 8-week intervention. Moreover, anthropometric measures were determined at the onset of and after the intervention. For within- and between groups comparisons of all variables, t test (independent and dependent) was used (p < 0.05). Results: The results revealed that IL-6 and CRP levels significantly decreased after exercise training (from 6.8 ± 1.52 to 3.2 ± 0.96, p < 0.001 and from 2.76 ± 0.98 to 1.55 ± 0.44, p = <0.001; respectively). Also, exercise training significantly increased IL-10 in the exercise group (from 16.4 ± 2.74 to 23.2 ± 2.11, p < 0.001). There was a significant difference between the 2 groups in all markers in the after 8-week exercise (p < 0.05). Conclusions: One of the characteristics of MS disease is inflammation. Exercise training through physiological mechanisms and without aggravating the inflammatory pathology can be effective in functional and symptom reduction of patients with MS. In confirmation of this, the present study showed that 8 weeks of combined exercise training decreased pro-inflammatory markers (IL-6 and CRP) and increased anti-inflammatory cytokine (IL-10). Our findings suggested that an exercise training program can be an effective strategy for managing the immune system of women with MS at least by its significant effect on inflammatory markers.
A novel fast-releasing formulation containing curcumin and anti-microbial peptide-encapsulated transdermal hydrogels into microneedles to reduce inflammation in the therapy of neonatal sepsis
Authors: Qian Min , Ma Di , Zhao Xiaoqi , Guo Wanxu , Zhang Yunfeng
Published: July 2021

Neonatal sepsis is a leading cause of neonatal mortality worldwide. Typically, intramuscular injections of gentamycin and vancomycin have been the preferred treatment for sepsis. The use of these drug molecules has many complications because of some side effects, proper dosage calculations,
and harmless injection supply. In the present study, we developed new combinations of therapeutic drug molecules containing anti-inflammatory curcumin with bioactive antimicrobial peptides (AMP). We designed and developed a biocompatible and thermally responsive chitosan/polyacrylamide polymeric
microneedle array to deliver AMP/curcumin nanoparticles (Nano-Cur) transdermally. The prepared drug-loaded microneedles were investigated for their chemical and morphological structure, in vitro insertion, and transdermal drug release. This investigation established that AMP/Nano-Cur-loaded
microneedles have potential effects in sepsis treatment.