scholarly journals VESPER: global and local cryo-EM map alignment using local density vectors

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Xusi Han ◽  
Genki Terashi ◽  
Charles Christoffer ◽  
Siyang Chen ◽  
Daisuke Kihara
Keyword(s):  
Structural Information ◽  
Local Density ◽  
Molecular Structures ◽  
Local Alignment ◽  
Biological Macromolecules ◽  
The Public ◽  
Density Maps ◽  
Map Alignment ◽  
Local Gradient ◽  
Global And Local

AbstractAn increasing number of density maps of biological macromolecules have been determined by cryo-electron microscopy (cryo-EM) and stored in the public database, EMDB. To interpret the structural information contained in EM density maps, alignment of maps is an essential step for structure modeling, comparison of maps, and for database search. Here, we developed VESPER, which captures the similarity of underlying molecular structures embedded in density maps by taking local gradient directions into consideration. Compared to existing methods, VESPER achieved substantially more accurate global and local alignment of maps as well as database retrieval.

scholarly journals Vesper: Global and Local Cryo-Em Map Alignment and Database Search using Local Density Vectors

2021 ◽  
Vol 120 (3) ◽  
pp. 84a
Author(s):  
Genki Terashi ◽  
Xusi Han ◽  
Charles Christoffer ◽  
Siyang Chen ◽  
Daisuke Kihara
Keyword(s):  
Local Density ◽  
Database Search ◽  
Map Alignment ◽  
Global And Local

scholarly journals Super-Resolution Cryo-EM Maps With 3D Deep Generative Networks

2021 ◽  
Author(s):  
Sai Raghavendra Maddhuri Venkata Subramaniya ◽  
Genki Terashi ◽  
Daisuke Kihara
Keyword(s):  
Three Dimensional ◽  
Super Resolution ◽  
Molecular Structures ◽  
Biological Macromolecules ◽  
Generative Adversarial Network ◽  
Test Dataset ◽  
Border Line ◽  
Adversarial Network ◽  
Cryo Electron Microscopy ◽  
Density Maps

AbstractAn increasing number of biological macromolecules have been solved with cryo-electron microscopy (cryo-EM). Over the past few years, the resolutions of density maps determined by cryo-EM have largely improved in general. However, there are still many cases where the resolution is not high enough to model molecular structures with standard computational tools. If the resolution obtained is near the empirical border line (3-4 Å), a small improvement of resolution will significantly facilitate structure modeling. Here, we report SuperEM, a novel deep learning-based method that uses a three-dimensional generative adversarial network for generating an improved-resolution EM map from an experimental EM map. SuperEM is designed to work with EM maps in the resolution range of 3 Å to 6 Å and has shown an average resolution improvement of 1.0 Å on a test dataset of 36 experimental maps. The generated super-resolution maps are shown to result in better structure modelling of proteins.

scholarly journals Crossmodality Person Reidentification Based on Global and Local Alignment

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Qiong Lou ◽  
Junfeng Li ◽  
Yaguan Qian ◽  
Anlin Sun ◽  
Fang Lu
Keyword(s):  
Computer Vision ◽  
State Of The Art ◽  
Structural Information ◽  
Local Alignment ◽  
Structure Information ◽  
Fine Grained ◽  
Rgb Images ◽  
Alignment Strategy ◽  
Global And Local ◽  
Feature Alignment

RGB-infrared (RGB-IR) person reidentification is a challenge problem in computer vision due to the large crossmodality difference between RGB and IR images. Most traditional methods only carry out feature alignment, which ignores the uniqueness of modality differences and is difficult to eliminate the huge differences between RGB and IR. In this paper, a novel AGF network is proposed for RGB-IR re-ID task, which is based on the idea of global and local alignment. The AGF network distinguishes pedestrians in different modalities globally by combining pixel alignment and feature alignment and highlights more structure information of person locally by weighting channels with SE-ResNet-50, which has achieved ideal results. It consists of three modules, including alignGAN module ( A ), crossmodality paired-images generation module ( G ), and feature alignment module ( F ). First, at pixel level, the RGB images are converted into IR images through the pixel alignment strategy to directly reduce the crossmodality difference between RGB and IR images. Second, at feature level, crossmodality paired images are generated by exchanging the modality-specific features of RGB and IR images to perform global set-level and fine-grained instance-level alignment. Finally, the SE-ResNet-50 network is used to replace the commonly used ResNet-50 network. By automatically learning the importance of different channel features, it strengthens the ability of the network to extract more fine-grained structural information of person crossmodalities. Extensive experimental results conducted on SYSU-MM01 dataset demonstrate that the proposed method favorably outperforms state-of-the-art methods. In addition, we evaluate the performance of the proposed method on a stronger baseline, and the evaluation results show that a RGB-IR re-ID method will show better performance on a stronger baseline.

Measurement and Reduction of Radiation Damage in Frozen Hydrated Crystalline Specimens

1978 ◽  
Vol 36 (3) ◽  
pp. 70-77 ◽  
Author(s):  
R.M. Glaeser ◽  
S.B. Hayward
Keyword(s):  
Diffraction Pattern ◽  
Structural Information ◽  
Structural Disorder ◽  
Structural Features ◽  
Biological Macromolecules ◽  
Diffraction Intensity ◽  
Object Structure ◽  
Specimen Material ◽  
Unit Cells ◽  
Identical Unit

Highly ordered or crystalline biological macromolecules become severely damaged and structurally disordered after a brief electron exposure. Evidence that damage and structural disorder are occurring is clearly given by the fading and eventual disappearance of the specimen's electron diffraction pattern. The fading and disappearance of sharp diffraction spots implies a corresponding disappearance of periodic structural features in the specimen. By the same token, there is a oneto- one correspondence between the disappearance of the crystalline diffraction pattern and the disappearance of reproducible structural information that can be observed in the images of identical unit cells of the object structure. The electron exposures that result in a significant decrease in the diffraction intensity will depend somewhat upon the resolution (Bragg spacing) involved, and can vary considerably with the chemical makeup and composition of the specimen material.

scholarly journals Structure and Electrochemical Potential Simulation for the Cathode Material Li1+xV3O8

1997 ◽  
Vol 496 ◽  
Author(s):  
R. Benedek ◽  
M. M. Thackeray ◽  
L. H. Yang
Keyword(s):  
Density Functional ◽  
Structural Information ◽  
Local Density ◽  
Tetrahedral Site ◽  
Low Energy ◽  
Electrochemical Potential ◽  
X Ray Diffraction ◽  
X Ray ◽  
Local Density Functional Theory ◽  
Two Phases

ABSTRACTThe structure and electrochemical potential of monoclinic Li1+xV3O8 were calculated within the local-density-functional-theory framework by use of plane-wave-pseudopotential methods. Special attention was given to the compositions 1+x=1.2 and 1+x=4, for which x-ray diffraction structure refinements are available. The calculated low-energy configuration for 1+x=4 is consistent with the three Li sites identified in x-ray diffraction measurements and predicts the position of the unobserved Li. The location of the tetrahedrally coordinated Li in the calculated low-energy configuration for 1+x=1.5 is consistent with the structure measured by x-ray diffraction for Li1.2V3O8. Calculations were also performed for the two monoclinic phases at intermediate Li compositions, for which no structural information is available. Calculations at these compositions are based on hypothetical Li configurations suggested by the ordering of vacancy energies for Li4V3O8 and tetrahedral site energies in Li1.5V3O8. The internal energy curves for the two phases- cross near 1+x=3. Predicted electrochemical potential curves agree well with experiment.

scholarly journals Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures

Biomolecules ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1788
Author(s):  
Vy T. Duong ◽  
Elizabeth M. Diessner ◽  
Gianmarc Grazioli ◽  
Rachel W. Martin ◽  
Carter T. Butts
Keyword(s):  
Protein Structure ◽  
Intrinsically Disordered Proteins ◽  
Dynamic Models ◽  
Structural Information ◽  
Coarse Graining ◽  
Molecular Structures ◽  
Disordered Proteins ◽  
Intrinsically Disordered ◽  
Scalable Network ◽  
Atomic Coordinates

Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of Aβ1–40, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs.

Recognizing Human Actions in Basketball Video Sequences on the Basis of Global and Local Pairwise Representation

2018 ◽  
pp. 2083-2101
Author(s):  
Masaki Takahashi ◽  
Masahide Naemura ◽  
Mahito Fujii ◽  
James J. Little
Keyword(s):  
Feature Representation ◽  
Video Sequences ◽  
Human Actions ◽  
The Public ◽  
Motion Features ◽  
Spatio Temporal ◽  
Representation Method ◽  
Global And Local ◽  
The Relationship ◽  
Local Representations

A feature-representation method for recognizing actions in sports videos on the basis of the relationship between human actions and camera motions is proposed. The method involves the following steps: First, keypoint trajectories are extracted as motion features in spatio-temporal sub-regions called “spatio-temporal multiscale bags” (STMBs). Global representations and local representations from one sub-region in the STMBs are then combined to create a “glocal pairwise representation” (GPR). The GPR considers the co-occurrence of camera motions and human actions. Finally, two-stage SVM classifiers are trained with STMB-based GPRs, and specified human actions in video sequences are identified. An experimental evaluation of the recognition accuracy of the proposed method (by using the public OSUPEL basketball video dataset and broadcast videos) demonstrated that the method can robustly detect specific human actions in both public and broadcast basketball video sequences.

Bioinformatics-Inspired Algorithms for 2D-Image Analysis-Application to Synthetic and Medical Images Part I

2012 ◽  
Vol 1 (1) ◽  
pp. 14-38
Author(s):  
Perambur S. Neelakanta ◽  
Deepti Pappusetty
Keyword(s):  
Image Analysis ◽  
Medical Images ◽  
Synthetic Image ◽  
Local Alignment ◽  
Brain Scan ◽  
Analysis Application ◽  
Binary Correlation ◽  
Global And Local ◽  
The Brain ◽  
2D Images

To ascertain specific features in bio-/medical-images, a new avenue of using the so-called Needleman-Wunsch (NW) and Smith-Waterman (SW) algorithms (of bioinformatics) is indicated. In general, NW/SW algorithms are adopted in genomic science to obtain optimal (global and local) alignment of two linear sequences (like DNA nucleotide bases) to determine the similarity features between them and such 1D-sequence algorithms are presently extended to compare 2D-images via binary correlation. The efficacy of the proposed method is tested with synthetic images and a brain scan image. Thus, the way of finding the location of a distinct part in a synthetic image and that of a tumour in the brain scan image is demonstrated.

Uncovering community structure in networks via hybrid clustering using cascading failure dynamics and topological metric functions

2019 ◽  
Vol 33 (29) ◽  
pp. 1950352
Author(s):  
Bo Yang ◽  
Tao Huang ◽  
Xu Li
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Nearest Neighbor ◽  
Structural Information ◽  
Point Of View ◽  
Cascading Failures ◽  
Hybrid Clustering ◽  
Mesoscale Structure ◽  
Metric Functions ◽  
Global And Local

Many networks have community structure — groups of nodes within which connections are dense but between which they are sparser. While there exists a range of algorithms for community detection in networks, most of them try to discover this important mesoscale structure from a topological point of view solely. Here we develop a hybrid clustering approach for uncovering the community structure in a network using a combination of information on local topology of the network and on the dynamics of the cascading failures. The originality of the proposed approach is that we introduce a novel fusion of the dynamic behaviors of the cascading failures and topological metric functions in the [Formula: see text]th-nearest neighbor density scheme, which integrates both the global and local structural information of a given network for community detection. The experimental results on both artificial random and real-world benchmark networks indicate the effectiveness and reliability of our approach.

scholarly journals Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps

Molecules ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 82 ◽  
Author(s):  
Eman Alnabati ◽  
Daisuke Kihara
Keyword(s):  
Electron Microscopy ◽  
De Novo ◽  
Molecular Structures ◽  
Structure Modeling ◽  
Rapid Progress ◽  
Macromolecular Complexes ◽  
Modeling Methods ◽  
Cryo Electron Microscopy ◽  
Density Maps

Cryo-electron microscopy (cryo-EM) has now become a widely used technique for structure determination of macromolecular complexes. For modeling molecular structures from density maps of different resolutions, many algorithms have been developed. These algorithms can be categorized into rigid fitting, flexible fitting, and de novo modeling methods. It is also observed that machine learning (ML) techniques have been increasingly applied following the rapid progress of the ML field. Here, we review these different categories of macromolecule structure modeling methods and discuss their advances over time.

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