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Li W, Deng X, Liu J, Yu Z, Lou X. Delegated Proof of Stake Consensus Mechanism Based on Community Discovery and Credit Incentive. Entropy (Basel) 2023; 25:1320. [PMID: 37761619 PMCID: PMC10528498 DOI: 10.3390/e25091320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 08/26/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023]
Abstract
Consensus algorithms are the core technology of a blockchain and directly affect the implementation and application of blockchain systems. Delegated proof of stake (DPoS) significantly reduces the time required for transaction verification by selecting representative nodes to generate blocks, and it has become a mainstream consensus algorithm. However, existing DPoS algorithms have issues such as "one ballot, one vote", a low degree of decentralization, and nodes performing malicious actions. To address these problems, an improved DPoS algorithm based on community discovery is designed, called CD-DPoS. First, we introduce the PageRank algorithm to improve the voting mechanism, achieving "one ballot, multiple votes", and we obtain the reputation value of each node. Second, we propose a node voting enthusiasm measurement method based on the GN algorithm. Finally, we design a comprehensive election mechanism combining node reputation values and voting enthusiasm to select secure and reliable accounting nodes. A node credit incentive mechanism is also designed to effectively motivate normal nodes and drive out malicious nodes. The experimental simulation results show that our proposed algorithm has better decentralization, malicious node eviction capabilities and higher throughput than similar methods.
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Affiliation(s)
- Wangchun Li
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China; (W.L.)
| | - Xiaohong Deng
- School of Electronics and Information Engineering, Gannan University of Science and Technology, Ganzhou 341000, China
- Key Laboratory of Cloud Computing and Big Data, Ganzhou 341000, China
| | - Juan Liu
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China; (W.L.)
| | - Zhiwei Yu
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China; (W.L.)
| | - Xiaoping Lou
- College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China;
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Zhu Q, Yang J, Xu B, Hou Z, Sun L, Zhang D. Multimodal Brain Network Jointly Construction and Fusion for Diagnosis of Epilepsy. Front Neurosci 2021; 15:734711. [PMID: 34658773 PMCID: PMC8511490 DOI: 10.3389/fnins.2021.734711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 08/10/2021] [Indexed: 11/24/2022] Open
Abstract
Brain network analysis has been proved to be one of the most effective methods in brain disease diagnosis. In order to construct discriminative brain networks and improve the performance of disease diagnosis, many machine learning–based methods have been proposed. Recent studies show that combining functional and structural brain networks is more effective than using only single modality data. However, in the most of existing multi-modal brain network analysis methods, it is a common strategy that constructs functional and structural network separately, which is difficult to embed complementary information of different modalities of brain network. To address this issue, we propose a unified brain network construction algorithm, which jointly learns both functional and structural data and effectively face the connectivity and node features for improving classification. First, we conduct space alignment and brain network construction under a unified framework, and then build the correlation model among all brain regions with functional data by low-rank representation so that the global brain region correlation can be captured. Simultaneously, the local manifold with structural data is embedded into this model to preserve the local structural information. Second, the PageRank algorithm is adaptively used to evaluate the significance of different brain regions, in which the interaction of multiple brain regions is considered. Finally, a multi-kernel strategy is utilized to solve the data heterogeneity problem and merge the connectivity as well as node information for classification. We apply the proposed method to the diagnosis of epilepsy, and the experimental results show that our method can achieve a promising performance.
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Affiliation(s)
- Qi Zhu
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Jing Yang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Bingliang Xu
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Zhenghua Hou
- Department of Psychosomatics and Psychiatry, Affiliated Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Liang Sun
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
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Kamberaj H. Heat flow random walks in biomolecular systems using symbolic transfer entropy and graph theory. J Mol Graph Model 2021; 104:107838. [PMID: 33529933 DOI: 10.1016/j.jmgm.2021.107838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 12/19/2020] [Accepted: 01/04/2021] [Indexed: 11/23/2022]
Abstract
This study combines the information- and graph-theoretic measures to investigate the cluster modulation of the amino acid residues and nucleotides at complex biomolecular interfaces. The symbolic transfer entropy is used as an information-theoretic measure. I also used graph theory to obtain information and heat flow weighted digraph models used to study the topology of information and heat flow paths at complex biomolecular interfaces. I introduce the graph-theoretic measures, such as the influence score and betweenness centrality, to identify the most influential amino acid and nucleotide sequences as sources of the information and absorb centers of the structure's heat flow. PageRank-like random walks algorithm is used to analyze the network of amino acid and nucleotide sequences at the protein-RNA interface combined with weighted digraph models. The cluster analysis using graph-theoretic measures revealed the modular molecular structure and the mechanism of the binding interface. In this study, the first benchmark system is an intuitive directed information flow network used to test the algorithms, and the second benchmark is a protein-RNA complex system. The approach was able to identify the most influential amino acid residues and nucleotides. Furthermore, the statistical cluster analysis using graph-theoretic measures revealed the modular molecular structure and the binding mechanism at the interface.
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Hyung D, Mallon AM, Kyung DS, Cho SY, Seong JK. TarGo: network based target gene selection system for human disease related mouse models. Lab Anim Res 2019; 35:23. [PMID: 32257911 PMCID: PMC7081697 DOI: 10.1186/s42826-019-0023-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 10/21/2019] [Indexed: 11/25/2022] Open
Abstract
Genetically engineered mouse models are used in high-throughput phenotyping screens to understand genotype-phenotype associations and their relevance to human diseases. However, not all mutant mouse lines with detectable phenotypes are associated with human diseases. Here, we propose the “Target gene selection system for Genetically engineered mouse models” (TarGo). Using a combination of human disease descriptions, network topology, and genotype-phenotype correlations, novel genes that are potentially related to human diseases are suggested. We constructed a gene interaction network using protein-protein interactions, molecular pathways, and co-expression data. Several repositories for human disease signatures were used to obtain information on human disease-related genes. We calculated disease- or phenotype-specific gene ranks using network topology and disease signatures. In conclusion, TarGo provides many novel features for gene function prediction.
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Affiliation(s)
- Daejin Hyung
- 1National Cancer Center, 323 Ilsan-ro, Goyang-si, Kyeonggi-do 10408 Republic of Korea
| | - Ann-Marie Mallon
- 2MRC Harwell Institute, Mammalian Genetics Unit, Oxfordshire, OX11 0RD UK
| | - Dong Soo Kyung
- 3Laboratory of Developmental Biology and Genomics, Research Institute for Veterinary Science, and BK21 Plus Program for Creative Veterinary Science, College of Veterinary Medicine, Seoul National University, Seoul, 08826 Republic of Korea.,4Korea Mouse Phenotyping Center (KMPC), Seoul National University, Seoul, 08826 Republic of Korea.,5Interdisciplinary Program for Bioinformatics, Program for Cancer Biology and BIO-MAX institute, Seoul National University, Seoul, 08826 Republic of Korea
| | - Soo Young Cho
- 1National Cancer Center, 323 Ilsan-ro, Goyang-si, Kyeonggi-do 10408 Republic of Korea.,4Korea Mouse Phenotyping Center (KMPC), Seoul National University, Seoul, 08826 Republic of Korea
| | - Je Kyung Seong
- 3Laboratory of Developmental Biology and Genomics, Research Institute for Veterinary Science, and BK21 Plus Program for Creative Veterinary Science, College of Veterinary Medicine, Seoul National University, Seoul, 08826 Republic of Korea.,4Korea Mouse Phenotyping Center (KMPC), Seoul National University, Seoul, 08826 Republic of Korea.,5Interdisciplinary Program for Bioinformatics, Program for Cancer Biology and BIO-MAX institute, Seoul National University, Seoul, 08826 Republic of Korea
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Kininmonth S, Weeks R, Abesamis RA, Bernardo LPC, Beger M, Treml EA, Williamson D, Pressey RL. Strategies in scheduling marine protected area establishment in a network system. Ecol Appl 2019; 29:e01820. [PMID: 30550634 DOI: 10.1002/eap.1820] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 05/27/2018] [Accepted: 08/20/2018] [Indexed: 05/12/2023]
Abstract
Instantaneous implementation of systematic conservation plans at regional scales is rare. More typically, planned actions are applied incrementally over periods of years or decades. During protracted implementation, the character of the connected ecological system will change as a function of external anthropogenic pressures, local metapopulation processes, and environmental fluctuations. For heavily exploited systems, habitat quality will deteriorate as the plan is implemented, potentially influencing the schedule of protected area implementation necessary to achieve conservation objectives. Understanding the best strategy to adopt for applying management within a connected environment is desirable, especially given limited conservation resources. Here, we model the sequential application of no-take marine protected areas (MPAs) in the central Philippines within a metapopulation framework, using a range of network-based decision rules. The model was based on selecting 33 sites for protection from 101 possible sites over a 35-yr period. The graph-theoretic network criteria to select sites for protection included PageRank, maximum degree, closeness centrality, betweenness centrality, minimum degree, random, and historical events. We also included a dynamic strategy called colonization-extinction rate that was updated every year based on the changing capacity of each site to produce and absorb larvae. Each rule was evaluated in the context of achieving the maximum metapopulation mean lifetime at the conclusion of the implementation phase. MPAs were designated through the alteration of the extinction risk parameter. The highest ranked criteria were PageRank while the actual implementation from historical records ranked lowest. Our results indicate that protecting the sites ranked highest with regard to larval supply is likely to yield the highest benefit for fish abundance and fish metapopulation persistence. Model results highlighted the benefits of including network processes in conservation planning.
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Affiliation(s)
- Stuart Kininmonth
- Stockholm Resilience Centre, Stockholm University, Kräftriket, Sweden
- Centre for Ecological and Evolutionary Synthesis, University of Oslo, Oslo, Norway
- School of Marine Studies, The University of South Pacific, Suva, Fiji
| | - Rebecca Weeks
- ARC Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Queensland, Australia
| | - Rene A Abesamis
- ARC Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Queensland, Australia
- Silliman University-Angelo King Center for Research and Environmental Management, Dumaguete City, Philippines
| | | | - Maria Beger
- University of Queensland, Brisbane, Queensland, Australia
- School of Biology, Faculty of Biological Sciences, University of Leeds, Leeds, UK
| | - Eric A Treml
- University of Melbourne, Melbourne, Victoria, Australia
- School of Life and Environmental Sciences, Deakin University, Geelong, Victoria, Australia
| | - David Williamson
- ARC Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Queensland, Australia
| | - Robert L Pressey
- ARC Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Queensland, Australia
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Aris-Brosou S. Inferring influenza global transmission networks without complete phylogenetic information. Evol Appl 2014; 7:403-12. [PMID: 24665342 PMCID: PMC3962300 DOI: 10.1111/eva.12138] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2013] [Accepted: 11/06/2013] [Indexed: 11/30/2022] Open
Abstract
Influenza is one of the most severe respiratory infections affecting humans throughout the world, yet the dynamics of its global transmission network are still contentious. Here, I describe a novel combination of phylogenetics, time series, and graph theory to analyze 14.25 years of data stratified in space and in time, focusing on the main target of the human immune response, the hemagglutinin gene. While bypassing the complete phylogenetic inference of huge data sets, the method still extracts information suggesting that waves of genetic or of nucleotide diversity circulate continuously around the globe for subtypes that undergo sustained transmission over several seasons, such as H3N2 and pandemic H1N1/09, while diversity of prepandemic H1N1 viruses had until 2009 a noncontinuous transmission pattern consistent with a source/sink model. Irrespective of the shift in the structure of H1N1 diversity circulation with the emergence of the pandemic H1N1/09 strain, US prevalence peaks during the winter months when genetic diversity is at its lowest. This suggests that a dominant strain is generally responsible for epidemics and that monitoring genetic and/or nucleotide diversity in real time could provide public health agencies with an indirect estimate of prevalence.
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Affiliation(s)
- Stéphane Aris-Brosou
- Department of Biology, Center for Advanced Research in Environmental Genomics, University of Ottawa Ottawa, ON, Canada ; Department of Mathematics and Statistics, University of Ottawa Ottawa, ON, Canada
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