In Silico Investigation of Potential Applications of Gamma Carbonic Anhydrases as Catalysts of CO2 Biomineralization Processes: A Visit to the Thermophilic Bacteria Persephonella hydrogeniphila,Persephonella marina,Thermosulfidibacter takaii,and Thermus thermophilus

Carbonic anhydrases (CAs) have been identified as ideal catalysts for CO₂ sequestration. Here, we report the sequence and structural analyses as well as the molecular dynamics (MD) simulations of four γ-CAs from thermophilic bacteria. Three of these, Persephonella marina, Persephonella hydrogeniphila, and Thermosulfidibacter takaii originate from hydrothermal vents and one, Thermus thermophilus HB8, from hot springs. Protein sequences were retrieved and aligned with previously characterized γ-CAs, revealing differences in the catalytic pocket residues. Further analysis of the structures following homology modeling revealed a hydrophobic patch in the catalytic pocket, presumed important for CO₂ binding. Monitoring of proton shuttling residue His69 (P. marina γ-CA numbering) during MD simulations of P. hydrogeniphila and P. marina’s γ-CAs (γ-PhCA and γ-PmCA), showed a different behavior to that observed in the γ-CA of Escherichia coli, which periodically coordinates Zn²⁺. This work also involved the search for hotspot residues that contribute to interface stability. Some of these residues were further identified as key in protein communication via betweenness centrality metric of dynamic residue network analysis. T. takaii’s γ-CA showed marginally lower thermostability compared to the other three γ-CA proteins with an increase in conformations visited at high temperatures being observed. Hydrogen bond analysis revealed important interactions, some unique and others common in all γ-CAs, which contribute to interface formation and thermostability. The seemingly thermostable γ-CA from T. thermophilus strangely showed increased unsynchronized residue motions at 423 K. γ-PhCA and γ-PmCA were, however, preliminarily considered suitable as prospective thermostable CO₂ sequestration agents.     

Numerical solution of the kinematic dynamo problem for Beltrami flows in a sphere

The article discusses computational aspects of the kinematic problem of magnetic field generation by a Beltrami flow in a sphere. Galerkin's method is applied with a functional basis consisting of Laplace operator eigenfunctions. Dominant eigenvalues of the magnetic induction operator and associated magnetic eigenmodes are obtained numerically for a certain Beltrami flow for magnetic Reynolds numbers up to 100. The eigenvalue problem is solved by a highly optimized iterative procedure, which is quite general and can be applied to numerical treatment of arbitrary linear stability problems.     

基于多维彩色向量空间的火焰图像模糊聚类分割算法

针对火灾探测过程中早期火焰的分割技术研究,提出了一种基于多维彩色向量空间的火焰图像模糊聚类分割算法,该算法以运动目标序列图像之间变化的区域作为聚类模板,提取该聚类模板的RGB多维彩色特征向量,然后将图像的像素与聚类模板通过模糊聚类的方式进行分割。这种分割算法计算简单,时间开销较小,可以较好地获取火焰图像的边缘形态特征,并且能够明显消除不同光线下分割误差,实现快速无监督自动分割。     

ARD算法收敛性分析

对交替行对角化（ARD）算法收敛性进行分析.基于收敛性分析,给出了一些关于如何选取初始矩阵的策略,提出了算法终止的新准则.通过3个数值例子来说明所提出的分析策略的有效性.     

一种网络社团划分的评价及改进方法

主要从节点在所属群体内的相对重要程度出发, 尝试性地给出一种网络社团结构的新定义, 并在此基础上构建了一种网络社团划分的评价及改进方法。该方法既可以与现有的社团划分算法进行组合, 形成新的网络社团划分算法, 也可以独立使用, 对网络社团的划分结果进行评价与改进。最后, 通过MCL、GN、Factions等方法及算例对提出的算法进行了验证与分析。     

粗集理论及其在旋转机械故障诊断中的应用 (二)旋转机械故障诊断知识库的形成

旋转机械故障诊断知识库形成的主要环节是信号的采集和信号的分析、整理 ;在所有的机械故障信号中 ,振动诊断技术是故障诊断的常用手段 ,对振动信号实现连续、在线诊断的最重要的数学工具是快速傅里叶变换(FFT)和小波理论。通过对实验的观察 ,建立转子故障特征向量和故障类型之间对应关系的数据库 ,为基于粗集理论故障诊断的决策表的形成提供了原始的知识库     

PSN网络中基于社会属性的消息路由

PSN（pocket switched network）网络作为一种特殊的DTN（Delay Tolerance Network）网络,除了具有不包含持续端到端连接、高传输时延及节点资源受限的特点以外,具有更强的社会规律性.本文在研究了现有的算法的基础上,提出了一种基于社会信息的路由算法—BridgingCom.为使消息尽快从本地传递到目标节点,我们选择桥接中心度（Bridging Centrality）作为中继节点的选择依据.实验结果表明,与已有相关算法相比,本改进算法能够在网络负载较低的情况下获得较高的传输效率,较低的传输延迟.     

Protein interaction networks: centrality,modularity, dynamics,and applications

In the post-genomic era, proteomics has achieved significant theoretical and practical advances with the development of high-throughput technologies. Especially the rapid accumulation of protein-protein interactions (PPIs) provides a foundation for constructing protein interaction networks (PINs), which can furnish a new perspective for understanding cellular organizations, processes, and functions at network level. In this paper, we present a comprehensive survey on three main characteristics of PINs: centrality, modularity, and dynamics. 1) Different centrality measures, which are used to calculate the importance of proteins, are summarized based on the structural characteristics of PINs or on the basis of its integrated biological information; 2) Different modularity definitions and various clustering algorithms for predicting protein complexes or identifying functional modules are introduced; 3) The dynamics of proteins, PPIs and sub-networks are discussed, respectively. Finally, the main applications of PINs in the complex diseases are reviewed, and the challenges and future research directions are also discussed.     

Virtual network embedding based on real-time topological attributes

As a great challenge of network virtualization, virtual network embedding/mapping is increasingly important. It aims to successfully and efficiently assign the nodes and links of a virtual network (VN)...     

N重亏损状态矩阵广义特征向量灵敏度分析

为了克服具有N重特征值的状态矩阵发生亏损现象后对灵敏度分析的制约,本文基于亏损矩阵的若当标准形理论,引入广义特征向量及其伴随向量系,提出了广义特征向量灵敏度的全模态算法。求解亏损矩阵广义特征向量的灵敏度时,发现除一个灵敏度系数为非精确解外,其余灵敏度系数均为精确解。针对非精确解,通过引入松驰因子获得了较好的近似解。数值算例证实该算法适用于N重亏损状态矩阵的灵敏度分析。