Evolutionary Network Analysis Research Articles

Haplotype variations of sucrose phosphate synthase B gene among sugarcane accessions with different sucrose content

BackgroundSucrose phosphate synthase B (SPSB) gene encoding an important rate-limiting enzyme for sucrose synthesis in sugarcane is mainly expressed on leaves, where its alleles control sucrose synthesis. In this study, genetic variation of SPSB gene represented by different haplotypes in sugarcane was investigated in hybrid clones with high and low sugar content and various accessory species.ResultsA total of 39 haplotypes of SPSB gene with 2, 824 bp in size were identified from 18 sugarcane accessions. These haplotypes mainly distributed on Chr3B, Chr3C, and Chr3D according to the AP85-441 reference genome. Single nucleotide polymorphisms (SNPs) and insertion/deletion (InDels) were very dense (42 bp/sequence variation) including 44 transitional and 23 transversional SNPs among the 39 haplotypes. The sequence diversity related Hd, Eta, and Pi values were all lower in clones of high sucrose content (HS) than those in clones of low sucrose content (LS). The evolutionary network analysis showed that about half SPSB haplotypes (19 out of 39) were clustered into one group, including 6 (HAP4, HAP6, HAP7, HAP9, HAP17 and HAP20) haplotypes under positive selection in comparison to HAP26 identified in Badila (S. officinarum), an ancestry noble cane species and under purification selection (except HAP19 under neutral selection) in comparison to HAP18 identified from India1 (S. spontaneum), an ancestry species with low sugar content but high stress tolerance. The average number of haplotypes under positive selection in HS clones was twice as that in LS. Most of the SNPs and InDels sequence variation sites were positively correlated with sucrose and fiber content and negatively correlated with reducing sugar.ConclusionsA total of 39 haplotypes of SPSB gene were identified in this study. Haplotypes potentially associated with high sucrose synthesis efficiency were identified. The mutations of SPSB haplotypes in HS were favorable and tended to be selected and fixed. The results of this study are informative and beneficial to the molecular assisted breeding of sucrose synthesis in sugarcane in the future.

Phase Transition in Group Emotion

This article explores the formation and change of group emotion. The phase of group emotion is defined, and the phase transition of group emotion is studied from the following two aspects: the group with and without network structure (the group with coevolution between emotion and network structure). For the group without network structure, the threshold of group emotional phase transition is obtained, and the phenomenon of group emotional polarization is verified. For the group with co-evolution of emotion and network structure, node attractiveness is defined from three aspects: node emotional propensity, node aggregation degree, and node importance. The new network evolution model based on “node attractiveness” is constructed, and the degree distribution of the network is analyzed. The processes and conditions of phase transition in group emotion are obtained based on this evolutionary network analysis. The results show that there are three phases of group emotion: disorder phase, neutral phase, and extreme phase.

Temporally Factorized Network Modeling for Evolutionary Network Analysis.

The problem of evolutionary network analysis has gained increasing attention in recent years, because of an increasing number of networks, which are encountered in temporal settings. For example, social networks, communication networks, and information networks continuously evolve over time, and it is desirable to learn interesting trends about how the network structure evolves over time, and in terms of other interesting trends. One challenging aspect of networks is that they are inherently resistant to parametric modeling, which allows us to truly express the edges in the network as functions of time. This is because, unlike multidimensional data, the edges in the network reflect interactions among nodes, and it is difficult to independently model the edge as a function of time, without taking into account its correlations and interactions with neighboring edges. Fortunately, we show that it is indeed possible to achieve this goal with the use of a matrix factorization, in which the entries are parameterized by time. This approach allows us to represent the edge structure of the network purely as a function of time, and predict the evolution of the network over time. This opens the possibility of using the approach for a wide variety of temporal network analysis problems, such as predicting future trends in structures, predicting links, and node-centric anomaly/event detection. This flexibility is because of the general way in which the approach allows us to express the structure of the network as a function of time. We present a number of experimental results on a number of temporal data sets showing the effectiveness of the approach.

Evolutionary Network Analysis

Evolutionary network analysis has found an increasing interest in the literature because of the importance of different kinds of dynamic social networks, email networks, biological networks, and social streams. When a network evolves, the results of data mining algorithms such as community detection need to be correspondingly updated. Furthermore, the specific kinds of changes to the structure of the network, such as the impact on community structure or the impact on network structural parameters, such as node degrees, also needs to be analyzed. Some dynamic networks have a much faster rate of edge arrival and are referred to as network streams or graph streams. The analysis of such networks is especially challenging, because it needs to be performed with an online approach, under the one-pass constraint of data streams. The incorporation of content can add further complexity to the evolution analysis process. This survey provides an overview of the vast literature on graph evolution analysis and the numerous applications that arise in different contexts.

Incremental by Design? On the Role of Incumbents in Technology Niches – An Evolutionary Network Analysis

In this paper, we study the evolution of governance structures in technological niches. At the case of public funded research projects and the resulting cooperation networks related to smart grid and systems in Denmark, we raise the questions which actors over time inherit a central position—associated with high influence on the development of research trajectories—in the network. We are particularly interested in what role incumbent actors, connected to the old regime of fossil based energy production, play in shaping future technological trajectories. The protected space theoretically created by such public research funding offers firms an environment to experiment in joint learning activities on emerging technologies, shielded from the selection pressure on open markets, thereby facilitating socio-technological transitions. Generally, the engagement of large incumbent actors in the development of emerging technologies, particularly in joint research projects with entrepreneurial ventures, is positively perceived, as their resource endowment enables them to stem large projects and bring them all the way to the market.

Evolutionary network analysis of word lists: Visualising the relationships between alpine romance languages*

The tree model and the wave model of language evolution are united into one geometric network model. A related approach has previously been used for reconstructing DNA evolution, and we now present a network approach for diagnostic word lists of closely related languages. When applied to 17 Alpine Romance languages, the resulting evolutionary network reproduces known linguistic relationships and also fairly accurately reflects the geographic location of each language.