Local Structure and Global Structure of a Light Sensor Protein Investigated by the Crystallographic Analysis and Solution Scattering Experiments

Identifying the key genes related to tumors from gene expression data with a large number of features is important for the accurate classification of tumors and to make special treatment decisions. In recent years, unsupervised feature selection algorithms have attracted considerable attention in the field of gene selection as they can find the most discriminating subsets of genes, namely the potential information in biological data. Recent research also shows that maintaining the important structure of data is necessary for gene selection. However, most current feature selection methods merely capture the local structure of the original data while ignoring the importance of the global structure of the original data. We believe that the global structure and local structure of the original data are equally important, and so the selected genes should maintain the essential structure of the original data as far as possible. In this paper, we propose a new, adaptive, unsupervised feature selection scheme which not only reconstructs high-dimensional data into a low-dimensional space with the constraint of feature distance invariance but also employs ℓ2,1-norm to enable a matrix with the ability to perform gene selection embedding into the local manifold structure-learning framework. Moreover, an effective algorithm is developed to solve the optimization problem based on the proposed scheme. Comparative experiments with some classical schemes on real tumor datasets demonstrate the effectiveness of the proposed method.

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Identification of Local Structure in 2-D and 3-D Atomic Systems through Crystallographic Analysis

Crystals ◽

10.3390/cryst10111008 ◽

2020 ◽

Vol 10 (11) ◽

pp. 1008

Author(s):

Pablo Miguel Ramos ◽

Miguel Herranz ◽

Katerina Foteinopoulou ◽

Nikos Ch. Karayiannis ◽

Manuel Laso

Keyword(s):

Local Structure ◽

Point Group ◽

Local Symmetry ◽

Crystallographic Analysis ◽

Three Dimensions ◽

Group Symmetry ◽

Spherical Particles ◽

Specific Reference ◽

Face Centered Cubic ◽

Face Centered

In the present work, we revise and extend the Characteristic Crystallographic Element (CCE) norm, an algorithm used to simultaneously detect radial and orientational similarity of computer-generated structures with respect to specific reference crystals and local symmetries. Based on the identification of point group symmetry elements, the CCE descriptor is able to gauge local structure with high precision and finely distinguish between competing morphologies. As test cases we use computer-generated monomeric and polymer systems of spherical particles interacting with the hard-sphere and square-well attractive potentials. We demonstrate that the CCE norm is able to detect and differentiate, between others, among: hexagonal close packed (HCP), face centered cubic (FCC), hexagonal (HEX) and body centered cubic (BCC) crystals as well as non-crystallographic fivefold (FIV) local symmetry in bulk 3-D systems; triangular (TRI), square (SQU) and honeycomb (HON) crystals, as well as pentagonal (PEN) local symmetry in thin films of one-layer thickness (2-D systems). The descriptor is general and can be applied to identify the symmetry elements of any point group for arbitrary atomic or particulate system in two or three dimensions, in the bulk or under confinement.

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Anomalous pressure effects on the photoreaction of a light-sensor protein from Synechocystis, PixD (Slr1694), and the compressibility change of its intermediates

Physical Chemistry Chemical Physics ◽

10.1039/c6cp05091c ◽

2016 ◽

Vol 18 (37) ◽

pp. 25915-25925 ◽

Cited By ~ 2

Author(s):

Tsubasa Nakajima ◽

Kunisato Kuroi ◽

Yusuke Nakasone ◽

Koji Okajima ◽

Masahiko Ikeuchi ◽

...

Keyword(s):

High Pressure ◽

Pressure Effect ◽

Pressure Effects ◽

Light Sensor ◽

Sensor Protein

High pressure enhances the dissociation yield of SyPixD, and this pressure effect was explained by pressure dependent fluctuation of intermediates.

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Optimizing complex networks controllability by local structure information

International Journal of Modern Physics C ◽

10.1142/s0129183116501151 ◽

2016 ◽

Vol 27 (10) ◽

pp. 1650115

Author(s):

Houyi Yan ◽

Lvlin Hou ◽

Yunxiang Ling ◽

Guohua Wu

Keyword(s):

Numerical Simulation ◽

Complex Networks ◽

Network Structure ◽

Local Structure ◽

Global Structure ◽

Directed Network ◽

Structure Information ◽

Different Types ◽

Extensive Numerical Simulation ◽

Network Controllability

Research in network controllability has mostly been focused on the effects of the network structure on its controllability, and some methods have been proposed to optimize the network controllability. However, they are all based on global structure information of networks. We propose two different types of methods to optimize controllability of a directed network by local structure information. Extensive numerical simulation on many modeled networks demonstrates that this method is effective. Since the whole topologies of many real networks are not visible and we only get some local structure information, this strategy is potentially more practical.

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