Structure Reconstruction Method Research Articles

3D model topology algorithm based on virtual reality visual features

This study proposes a virtual reconstruction method for fuzzy 3D images based on visual communication effects to improve recognition ability. This method includes constructing a grid distribution model for 3D images, recombining spatial information features using visual feature extraction, and detecting edges using contour feature extraction. A three-dimensional image visual communication model was established, and adaptive pixel reconstruction was achieved through fuzzy structure reconstruction method. Reconstruct the texture structure and grayscale histogram of 3D images to achieve high-quality virtual reconstruction. The simulation results verified the effectiveness of the method, and the designed simulation platform verified the accuracy and completeness of the method, demonstrating the improvement of existing systems and the potential for advancing the field of visual communication.

(Invited) Structure Reconstruction Strategy for High-Energy Lithium Ion Batteries without Capacity Sacrifice

Lithium nickel oxides (LiNiO2) is considered to be a promising high-energy-density cathode material in lithium-ion batteries (LIBs)1-3. However, its inherent Li off-stoichiometry composition and structural deterioration under various phase transition usually bring about low initial capacity and rapid capacity decay4,5. To stimulate the high capacity and extend the lifespan for LiNiO2 in LIBs, the structure reconstruction method is applied to modify the cathode materials by Mo regulating (Mo-LiNiO2). The cross section high-resolution transmission electron microscope and energy dispersive spectroscopy (TEM-EDS) images show that the concentration of Mo is gradually decreased from the surface to the bulk center. Correspondingly, the concentrated Ni2+ ions in the surface of Mo-LiNiO2 build up the rock-salt phase layer with 5-20 nm in the surface, which plays the pinner effect based on in situ X-ray diffraction (XRD) patterns to enable excellent mechanical and structural reversibility for the layered Mo-LiNiO2 upon cycling. Finally, electrochemical results show that the modified cathode materials (Mo-LiNiO2) has a high capacity (~235 mAh g–1 at 0.1C) and the stable cycle performance (~94% for 90 cycles at 1C) as shown in Figure 1. The Mo-LiNiO2 cathode can provide high capacity and cycle stability with low material cost, which is suitable for the long service life of electric vehicles, further realizing a commercially viable cathode materials for LIBs.

A 3D Chromosome Structure Reconstruction method with High Resolution Hi-C Data using Nonlinear Dimensionality Reduction and Divide-and-conquer strategy.

Chromosomes are fundamental components of genetic material, and their structural characteristics play an essential role in the regulation of gene expression. The advent of high-resolution Hi-C data has enabled scientists to explore the three-dimensional structure of chromosomes. However, most of the currently available methods for reconstructing chromosome structures are unable to achieve high resolutions, such as 5 Kilobase (KB). In this study, we present NeRV-3D, an innovative method that utilizes a nonlinear dimensionality reduction visualization algorithm to reconstruct 3D chromosome structures at low resolutions. Additionally, we introduce NeRV-3D-DC, which employs a divide-and-conquer technique to reconstruct and visualize 3D chromosome structures at high resolutions. Our results demonstrate that both NeRV-3D and NeRV-3D-DC outperform existing methods in terms of 3D visualization effects and evaluation metrics on simulated and actual Hi-C datasets. The implementation of NeRV-3D-DC can be found at https://github.com/ghaiyan/NeRV-3D-DC.

DENSS-multiple: A structure reconstruction method using contrast variation of small-angle neutron scattering based on the DENSS algorithm

The 3D structure of biomacromolecules, such as protein and DNA/RNA, provide keys to understanding their biological functions. Among many structural biology techniques, small-angle scattering techniques with ab initio methods have been widely used to reveal biomolecular structures in relevant solution conditions. Recently, a method called DENsity from Solution Scattering (DENSS) was developed to reconstruct the scattering density directly from biological small-angle X-ray and neutron scattering data instead of using a dummy atom modeling approach. Here, a method named DENSS-Multiple was developed to work simultaneously on multiple datasets from small-angle neutron scattering (SANS) contrast variation data. The easily manipulable neutron contrast has been widely exploited to study the structure and function of biological macromolecules and their complexes in solution. This new method provides a single structural result that includes all the information represented by different contrasts from SANS. The results from DENSS-Multiple generally have better resolution than those from DENSS, and more subtle features are represented by density variations from different phases of a structure. DENSS-Multiple was tested on various examples, including simulated and experimental data. These results, along with DENSS-Multiple's applications and limitations, are discussed herein.

Aseismic Design of Building Structure and Strengthening and Reconstruction Method

Aseismic Design of Building Structure and Strengthening and Reconstruction Method

Paper] A 2.5D Surface Structure Reconstruction Method using an Imaging System with Linear Sensor

Paper] A 2.5D Surface Structure Reconstruction Method using an Imaging System with Linear Sensor

GSDB: a database of 3D chromosome and genome structures reconstructed from Hi-C data

Advances in the study of chromosome conformation capture technologies, such as Hi-C technique - capable of capturing chromosomal interactions in a genome-wide scale - have led to the development of three-dimensional chromosome and genome structure reconstruction methods from Hi-C data. The three dimensional genome structure is important because it plays a role in a variety of important biological activities such as DNA replication, gene regulation, genome interaction, and gene expression. In recent years, numerous Hi-C datasets have been generated, and likewise, a number of genome structure construction algorithms have been developed.In this work, we outline the construction of a novel Genome Structure Database (GSDB) to create a comprehensive repository that contains 3D structures for Hi-C datasets constructed by a variety of 3D structure reconstruction tools. The GSDB contains over 50,000 structures from 12 state-of-the-art Hi-C data structure prediction algorithms for 32 Hi-C datasets.GSDB functions as a centralized collection of genome structures which will enable the exploration of the dynamic architectures of chromosomes and genomes for biomedical research. GSDB is accessible at http://sysbio.rnet.missouri.edu/3dgenome/GSDB

Linear Correction and Matching Method for 3D Line Structure Reconstruction

The 3D reconstruction technique using the straight-line segments as features has high precision and low computational cost. The method is especially suitable for large-scale urban datasets. However, the line matching step in the existing method has a mismatching problem. The two main reasons for this problem are the line detection result is not located at the true edge of the image and there is no consistency check of the matching pair. In order to solve this problem, a linear correction and matching method for 3D reconstruction of target line structure is proposed in this paper. Firstly, the edge features of the image are extracted to obtain a binarized edge map. Then, the extended gradient map is calculated using the edge map and the gradient to establish the gradient gravitational map. Secondly, the straight-line detection method is used to extract all the linear features used for the 3D reconstruction image, and the linear position is corrected by the gradient gravitational map. Finally, the point feature matching result is used to calculate the polar line, and the line matching results of the adjacent three images are used to determine the final partial check feature area. Then, random sampling is used to obtain the feature similarity check line matching result in the small neighborhood. The aforementioned steps can eliminate the mismatched lines. The experimental results demonstrate that the 3D model obtained using the proposed method has higher integrity and accuracy than the existing methods.

SDARE: A stacked denoising autoencoder method for game dynamics network structure reconstruction

Complex network is a general model to represent the interactions within technological, social, information, and biological interaction. Often, the direct detection of the interaction relationship is costly. Thus, network structure reconstruction, the inverse problem in complex networked systems, is of utmost importance for understanding many complex systems with unknown interaction structures. In addition, the data collected from real network system is often contaminated by noise, which makes the network structure inference task much more challenging. In this paper, we develop a new framework for the game dynamics network structure reconstruction based on deep learning method. In contrast to the compressive sensing methods that employ computationally complex convex/greedy algorithms to solve the network reconstruction task, we introduce a deep learning framework that can learn a structured representation from nodes data and efficiently reconstruct the game dynamics network structure with few observation data. Specifically, we propose the denoising autoencoders (DAEs) as the unsupervised feature learner to capture statistical dependencies between different nodes. Compared to the compressive sensing based method, the proposed method is a global network structure inference method, which can not only get the state-of-art performance, but also obtain the structure of network directly. Besides, the proposed method is robust to noise in the observation data. Moreover, the proposed method is also effective for the network which is not exactly sparse. Accordingly, the proposed method can extend to a wide scope of network reconstruction task in practice.

Sensitivity of simulated light interception and tree transpiration to the level of detail of 3D tree reconstructions

The aim of this paper is to investigate the minimal level of detail of the tree geometry reconstruction required to enable an accurate estimate of the evaporative cooling effect of an individual tree.The Functional Structural Plant Modeling approach, which links the 3D tree structure to its functioning, is used to investigate the impact of the level of detail of a tree reconstruction on its simulated transpiration. Based on terrestrial laser scanning point cloud data of a nine-meter-high silver linden tree (Tilia tomentosa Moench), several methods of reconstruction of its crown were considered. They can be divided into three groups: (i) tree branching structure reconstructions where leafy shoots are reconstructed; (ii) envelope reconstructions such as 3D convex/concave envelopes; and (iii) voxel reconstructions where leaves are uniformly distributed within the given volume. Based on these methods, several mock-ups of resolved tree crowns from low to high level of details were created. The cooling performance of each tree mock-up was compared by simulating its transpiration rate with a validated 3D ecophysiological model based on the turbid medium approach.The resulting mock-ups differ in several characteristics related to tree metrics and light interception: volume, projected leaf area, leaf area index, leaf area density, leaf clumping and silhouette-to-total area ratio. An inter-comparison of the transpiration rates provided by these mock-ups shows that tree branching structure reconstruction methods perform better than envelope computation methods and that these differences are related to the light interception property of the reconstructed trees.This study provides guidelines to determine which structural characteristics of tree crowns must be measured and taken into account in order to carry out accurate estimates of the transpiration rates of individual trees.

Imprints of the large-scale structure on AGN formation and evolution

Black hole masses are found to correlate with several global properties of their host galaxies, suggesting that black holes and galaxies have an intertwined evolution and that active galactic nuclei (AGN) have a significant impact on galaxy evolution. Since the large-scale environment can also affect AGN, this work studies how their formation and properties depend on the environment. We have used a reconstructed three-dimensional high-resolution density field obtained from a Bayesian large-scale structure reconstruction method applied to the 2M++ galaxy sample. A web-type classification relying on the shear tensor is used to identify different structures on the cosmic web, defining voids, sheets, filaments, and clusters. We confirm that the environmental density affects the AGN formation and their properties. We found that the AGN abundance is equivalent to the galaxy abundance, indicating that active and inactive galaxies reside in similar dark matter halos. However, occurrence rates are different for each spectral type and accretion rate. These differences are consistent with the AGN evolutionary sequence suggested by previous authors, Seyferts and Transition objects transforming into low-ionization nuclear emission line regions (LINERs), the weaker counterpart of Seyferts. We conclude that AGN properties depend on the environmental density more than on the web-type. More powerful starbursts and younger stellar populations are found in high densities, where interactions and mergers are more likely. AGN hosts show smaller masses in clusters for Seyferts and Transition objects, which might be due to gas stripping. In voids, the AGN population is dominated by the most massive galaxy hosts.

Fractal nature structure reconstruction method in designing microstructure properties

In the process of predicting materials properties, it is necessary to predict microstructures. This is important for ceramic materials, for the purpose of miniaturization and for a higher level of integration. Although the method of material structure reconstruction is for the first time used on BaTiO3-ceramics by these authors, it can also be used for silicate, refractory and other ceramics, and, for any powder-based material. The reconstruction is based on the grain perimeter fractal analysis and the Richardson method. The main contribution of this paper is establishing the relationship between the electronic properties of BaTiO3-ceramics and its microstructural fractal nature. Similar relations can be used for any ceramics or even any materials. The samples consolidation includes both powder pressing (cold sintering phase) and sintering in a furnace (hot sintering phase). The fractal nature characterization has a very important role from the powder phase up to the final microstructure, through which structures it directly impacts electro-physical and other ceramics properties. Thereby, the new possibilities in the microstructure characterization applications are directly introduced to an engineering system, providing the new solutions related to computer aided support. This creates new industrial production and application possibilities, especially in the process of ceramics material consolidation, now based on microstructural properties, which is providing a basis for “fractal electronics’’ development.

Reconstructing minimal length tree branch systems from leaf positions

We present a method to infer a straight-lines tree branch system from a given set of leaf positions and average branching angles. Among an extensive set of possible branch systems constructed in the process, we choose the one featuring the shortest total length, following an optimality hypothesis by Leopold (1971). The approach is illustrated using empirical low-order skeletons from European beech. Our method further allows to assess, for a given species or individual tree, to what extent its branching pattern accords to Leopold's hypothesis, which we argue to be the case for beech. While yet facing issues of computational intensity for too many leaves, the method can furthermore be used to complement existing tree structure reconstruction methods that otherwise require a rudimentary skeleton as manual input.

A support surface pore structure re-construction method to enhance the flux of TFC RO membrane

In this work, a support surface pore structure re-construction method was developed to enhance the flux of thin film composite (TFC) reverse osmosis (RO) membrane. With this method, an aqueous solution of a suitable organic solvent with a certain content, i.e., re-construction agent, was used to swell the support for a period of time and then the swelled support was immersed in water to complete the whole surface pore structure re-construction process. Herein, dimethylacetamide (DMAc) was employed as the organic solvent. It was shown that under proper conditions (i.e., the organic solvent content of 30% v/v, the re-constructing agent contacting time less than 10s and re-constructing agent contacting temperature of 25 ± 5°C), the support surface porosity and surface pore density increased a lot with the support surface pore size increasing slightly. The statistical results (obtained from scanning electron microscope images) showed that at the optimal condition, the support surface porosity and surface pore density increased by 92% and 89%, respectively. The results of RO membrane permselectivity obtained using 2000ppm NaCl solutions and 1.55MPa transmembrane pressure showed that the support surface pore structure re-construction method could improve the flux by 50.5% at most (an increase from 44.0 to 66.2Lm−2h−1) without sacrificing the salt rejection (all the membranes had a similar and high rejection of 99.38%). Besides, a support surface pore structure re-construction mechanism was proposed, which showed that the support surface pore structure was mainly determined by the diffusion and coalescence of re-construction agent molecules, and polymer chains mobility. This work provides a facile and effective method for preparing TFC RO membranes with high performance.

Chromosome3D: reconstructing three-dimensional chromosomal structures from Hi-C interaction frequency data using distance geometry simulated annealing.

BackgroundReconstructing three-dimensional structures of chromosomes is useful for visualizing their shapes in a cell and interpreting their function. In this work, we reconstruct chromosomal structures from Hi-C data by translating contact counts in Hi-C data into Euclidean distances between chromosomal regions and then satisfying these distances using a structure reconstruction method rigorously tested in the field of protein structure determination.ResultsWe first evaluate the robustness of the overall reconstruction algorithm on noisy simulated data at various levels of noise by comparing with some of the state-of-the-art reconstruction methods. Then, using simulated data, we validate that Spearman’s rank correlation coefficient between pairwise distances in the reconstructed chromosomal structures and the experimental chromosomal contact counts can be used to find optimum conversion rules for transforming interaction frequencies to wish distances. This strategy is then applied to real Hi-C data at chromosome level for optimal transformation of interaction frequencies to wish distances and for ranking and selecting structures. The chromosomal structures reconstructed from a real-world human Hi-C dataset by our method were validated by the known two-compartment feature of the human chromosome organization. We also show that our method is robust with respect to the change of the granularity of Hi-C data, and consistently produces similar structures at different chromosomal resolutions.ConclusionChromosome3D is a robust method of reconstructing chromosome three-dimensional models using distance restraints obtained from Hi-C interaction frequency data. It is available as a web application and as an open source tool at http://sysbio.rnet.missouri.edu/chromosome3d/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3210-4) contains supplementary material, which is available to authorized users.

Heat transfer characteristics of silica aerogel composite materials: Structure reconstruction and numerical modeling

Silica aerogel is a kind of nanoporous superinsulation material. Usually some kinds of particles and fibers are added into the aerogel material to enhance its ability of restraining radiation heat transfer and improving its strength. These functional additives will combine with aerogel material to form the aerogel composite insulation material. In this paper, aiming at fully investigating the heat transfer characteristics of the composite insulation material, structure reconstruction study and numerical study were performed. The random-number-based algorithms were proposed to reconstruct the structure of particle type multiphase material and fibrous type multiphase material. Then based on the structure reconstruction study, numerical method was presented for the combined conduction and radiation heat transfer of aerogel composite insulation material. In the numerical model, discrete ordinate method was used to solve the radiative transfer equation and finite volume method was used to solve the energy equation. By using the numerical model as well as the structure reconstruction method, the insulation performance of the composite material was discussed in detail. The results showed that: (1) as the content of particle/fiber increased, the conductive thermal conductivity increased and the radiative thermal conductivity decreased, the total thermal conductivity of the material decreased firstly and then increased; (2) an optimum doping content of opacifier particles can be found with the variation of particle volume fraction which possess different values at different temperature levels; (3) particle clusters have significant effect on the heat transfer performance of the composite insulation materials. When particle volume fraction becomes to a high value, large deviation emerges between the theoretical method and numerical method due to the particle clusters in the randomly generated structures; (4) for the directionally distributed fibrous material, its conductive thermal conductivity decayed exponentially with the angle between heat flux direction and fiber orientation. A modified model was proposed for the directionally distributed fibrous material which can effectively predict its conductive thermal conductivity.

A Template-Based Protein Structure Reconstruction Method Using Deep Autoencoder Learning.

Protein structure prediction is an important problem in computational biology, and is widely applied to various biomedical problems such as protein function study, protein design, and drug design. In this work, we developed a novel deep learning approach based on a deeply stacked denoising autoencoder for protein structure reconstruction. We applied our approach to a template-based protein structure prediction using only the 3D structural coordinates of homologous template proteins as input. The templates were identified for a target protein by a PSI-BLAST search. 3DRobot (a program that automatically generates diverse and well-packed protein structure decoys) was used to generate initial decoy models for the target from the templates. A stacked denoising autoencoder was trained on the decoys to obtain a deep learning model for the target protein. The trained deep model was then used to reconstruct the final structural model for the target sequence. With target proteins that have highly similar template proteins as benchmarks, the GDT-TS score of the predicted structures is greater than 0.7, suggesting that the deep autoencoder is a promising method for protein structure reconstruction.

A multi-scale geometric flow method for molecular structure reconstruction

We have previously reported an L2-gradient flow (L2GF) method for cryo-electron tomography and single-particle reconstruction, which has a reasonably good performance. The aim of this paper is to further upgrade both the computational efficiency and accuracy of the L2GF method. In a finite-dimensional space spanned by the radial basis functions, a minimization problem combining a fourth-order geometric flow with an energy decreasing constraint is solved by a bi-gradient method. The bi-gradient method involves a free parameter As β increases from 0 to 1, the structures of the reconstructed function from coarse to fine are captured. The experimental results show that the proposed method yields more desirable results.

Pore‐scale study of dissolution‐induced changes in hydrologic properties of rocks with binary minerals

AbstractA pore‐scale numerical model for reactive transport processes based on the Lattice Boltzmann method is used to study the dissolution‐induced changes in hydrologic properties of a fractured medium and a porous medium. The solid phase of both media consists of two minerals, and a structure reconstruction method called quartet structure generation set is employed to generate the distributions of both minerals. Emphasis is put on the effects of undissolved minerals on the changes of permeability and porosity under different Peclet and Damkohler numbers. The simulation results show porous layers formed by the undissolved mineral remain behind the dissolution reaction front. Due to the large flow resistance in these porous layers, the permeability increases very slowly or even remains at a small value although the porosity increases by a large amount. Besides, due to the heterogeneous characteristic of the dissolution, the chemical, mechanical and hydraulic apertures are very different from each other. Further, simulations in complex porous structures demonstrate that the existence of the porous layers of the nonreactive mineral suppresses the wormholing phenomena observed in the dissolution of mono‐mineralic rocks.

Fault diagnosis of nonlinear process based on KCPLS reconstruction

In this paper, a new kernel concurrent projection to latent structure (KCPLS) reconstruction method for process monitoring is proposed. The main contributions of the proposed approach are as follows: (1) the KCPLS method provides a complete monitoring of faults that happen in the predictable output subspace and the unpredictable output-residual subspace; (2) after the fault reconstruction approach is proposed, the fault-relevant direction is determined; (3) the fault is effectively diagnosed compared to the conventional KPLS method. The proposed method is applied to penicillin fermentation process and is compared to the KPLS method. Experiment results show that the KCPLS can more accurately detect the fault compared to the KPLS method. In addition, the fault-relevant direction is identified more effectively by using the KCPLS reconstruction algorithm compared to the KPLS reconstruction approach.