Network Reconstruction Research Articles

Conditional cross-map-based technique: From pairwise dynamical causality to causal network reconstruction.

Causality detection methods based on mutual cross mapping have been fruitfully developed and applied to data originating from nonlinear dynamical systems, where the causes and effects are non-separable. However, these pairwise methods still have shortcomings in discriminating typical network structures, including common drivers, indirect dependencies, and facing the curse of dimensionality, when they are stepping to causal network reconstruction. A few endeavors have been devoted to conquer these shortcomings. Here, we propose a novel method that could be regarded as one of these endeavors. Our method, named conditional cross-map-based technique, can eliminate third-party information and successfully detect direct dynamical causality, where the detection results can exactly be categorized into four standard normal forms by the designed criterion. To demonstrate the practical usefulness of our model-free, data-driven method, data generated from different representative models covering all kinds of network motifs and measured from real-world systems are investigated. Because correct identification of the direct causal links is essential to successful modeling, predicting, and controlling the underlying complex systems, our method does shed light on uncovering the inner working mechanisms of real-world systems only using the data experimentally obtained in a variety of disciplines.

A Flexible Topology Reconstruction Strategy Based on Deep Q-Learning for Balance Performance and Efficiency of STINs

Due to the mobility and vulnerability of satellite nodes, the topology of satellite-terrestrial integrated networks (STINs) is highly dynamic. It requires a flexible reconstruction strategy to avoid severe performance loss. However, the current reconstruction strategies usually result in a heavy computational burden, especially in the case of a shortage of satellite resources. Hence, it is of great practical interest to design a flexible topology reconstruction strategy to balance the computational efficiency and network performance of STINs. We investigated this important problem in this paper and made the following contributions. First, a deep Q-learning model is proposed to dynamically optimize the node classification results and flexibly determine the range of network reconstruction, which could enhance the efficiency of the network recovery. Second, a modified SVM classification model is proposed to classify the node type with the hyperplane parameters dynamically adjusted, which can increase the accuracy of the classification result and improve the convergence efficiency of the reconstruction algorithm. Third, an artificial bee colony algorithm is proposed to flexible recover the performance of the damaged network, which considers the age of information and range of the topology cross iteration. Numerical results demonstrate that compared with existing algorithms, the average end-to-end delay decreased by about 13.68%, and other performance indicators of STINs where improved to a certain extent.

The study of value network reconstruction and business model innovation driven by entrepreneurial orientation

The impacts of business models on the successes of companies are convincing. Business Model Canvas (BMC) provides the basic framework that describes business logic. Yet it lacks an academic understanding of the internal structures of business models. This paper uses the grounded theory method, qualitative analysis as well as entrepreneurial orientation theory to conduct an individual case study on SHEIN’s business model. The research shows that SHEIN accurately addresses the customer pain points and customer benefits of fashionable women, and gains the trust of end-users by integrating its supply chain based in China. Entrepreneurial orientation drives SHEIN to set up its value network. From the perspective of value discovery, SHEIN accurately targets segmented customer groups. The enterprise reaches end users through social networks and establishes customer relationship with high viscosity. From the perspective of value creation, SHEIN reconstructs the value network, delivering great shopping experiences with low costs. From the perspective of value acquisition, SHEIN has scalability in cost structure and expandability in revenue source. The research found that SHEIN has occupied the low-end market of fast fashion through low-end entry, and is moving to the mainstream market of fast fashion through customer group upgrading, design upgrading and supply chain upgrading, bringing competitive challenge to the fast fashion market incumbents. The innovation of this research lies in: First, the Business Model Iterative Canvas (BMIC) could interpret the business models and their logic in-depth. Second, the research reveals how the competitive advantage is created through value network reconstruction.

An efficient complex network embedding model for hierarchical networks

Several models have been proposed for hierarchical network embedding, however, it is difficult to optimize them. In order to improve the accuracy and efficiency of hierarchical network embedding, we propose Hierarchical Complex Network Embedding (HCNE), which embeds hierarchical networks in complex space. The motivation that we utiliz the Euler formula of complex numbers for hierarchical network embedding is this can make the gradient descent algorithm easily work for complex vectors since vectors are non-Euclidean. To preserve the structure of the hierarchical networks well, we consider parent constraint and brother constraint for hierarchical network (tree) when modeling. Furthermore, we derive an accurate upper bound for the relative radius of complex embedding, which makes HCNE scalable into large hierarchical networks. We conducted a series of experiments on 4 hierarchy datasets, and the superiority of the proposed HCNE model is proved on the tasks of network reconstruction, node classification and network visualization. E.g., HCNE outperforms the suboptimal baseline by 5.1% in terms of MAP on Georgetown dataset for the tasks of network reconstruction and by 9.9% in terms of MP on Wordnet dataset for the tasks of node classification.

Apoptosis Genes as a Key to Identification of Inverse Comorbidity of Huntington's Disease and Cancer.

Cancer and neurodegenerative disorders present overwhelming challenges for healthcare worldwide. Epidemiological studies showed a decrease in cancer rates in patients with neurodegenerative disorders, including the Huntington disease (HD). Apoptosis is one of the most important processes for both cancer and neurodegeneration. We suggest that genes closely connected with apoptosis and associated with HD may affect carcinogenesis. We applied reconstruction and analysis of gene networks associated with HD and apoptosis and identified potentially important genes for inverse comorbidity of cancer and HD. The top 10 high-priority candidate genes included APOE, PSEN1, INS, IL6, SQSTM1, SP1, HTT, LEP, HSPA4, and BDNF. Functional analysis of these genes was carried out using gene ontology and KEGG pathways. By exploring genome-wide association study results, we identified genes associated with neurodegenerative and oncological disorders, as well as their endophenotypes and risk factors. We used publicly available datasets of HD and breast and prostate cancers to analyze the expression of the identified genes. Functional modules of these genes were characterized according to disease-specific tissues. This integrative approach revealed that these genes predominantly exert similar functions in different tissues. Apoptosis along with lipid metabolism dysregulation and cell homeostasis maintenance in the response to environmental stimulus and drugs are likely key processes in inverse comorbidity of cancer in patients with HD. Overall, the identified genes represent the promising targets for studying molecular relations of cancer and HD.

Landscape and perturbation of enhancer-driven core transcription regulatory circuits in cancer

Landscape and perturbation of enhancer-driven core transcription regulatory circuits in cancer

A fast and scalable method for inferring phylogenetic networks from trees by aligning lineage taxon strings.

The reconstruction of phylogenetic networks is an important but challenging problem in phylogenetics and genome evolution, as the space of phylogenetic networks is vast and cannot be sampled well. One approach to the problem is to solve the minimum phylogenetic network problem, in which phylogenetic trees are first inferred, and then the smallest phylogenetic network that displays all the trees is computed. The approach takes advantage of the fact that the theory of phylogenetic trees is mature, and there are excellent tools available for inferring phylogenetic trees from a large number of biomolecular sequences. A tree-child network is a phylogenetic network satisfying the condition that every nonleaf node has at least one child that is of indegree one. Here, we develop a new method that infers the minimum tree-child network by aligning lineage taxon strings in the phylogenetic trees. This algorithmic innovation enables us to get around the limitations of the existing programs for phylogenetic network inference. Our new program, named ALTS, is fast enough to infer a tree-child network with a large number of reticulations for a set of up to 50 phylogenetic trees with 50 taxa that have only trivial common clusters in about a quarter of an hour on average.

Research Progress on RGMb and its Signaling Pathway

Repulsive guidance molecule b (RGMb) is a member of RGM family, which is structurally composed of an N-terminal signal peptide, a vWFD domain and a C-peptide. RGMb is widely expressed in different systems in order to regulate various physiological and pathophysiological processes. So RGMb involves in tumor progression, immune regulation, retinal network, neural network reconstruction and other important aspects. Recently, numerous studies reflect the diversity and complexity of RGMb signaling pathway. This paper will focus on RGMb, and introduce its molecule structure, function, related signaling pathway, and above all, the research progresses.

Robust service restoration of distribution systems towards coordinated parallel network reconstruction and cold load pick-up

This paper proposed a robust service restoration method coordinating parallel network reconstruction and load restoration decisions considering cold load pick-up (CLPU) effects. Firstly, a novel parallel network reconstruction model is proposed, which can simultaneously guarantee the radiality of each subsystem and the feasibility of the restoration sequence. The load behaviors during restoration are modeled considering CLPU effects over multiple steps. A two-stage service restoration model is developed to address the uncertainties arising from renewable DGs and loads. The first stage is to minimize total load losses by optimizing the operational decisions of DGs, switches and loads ahead of restoration, while the second state aims to optimize power flow after the decisions are implemented. The model is further formulated as a two-stage robust optimization model with infeasibility penalties in the second stage, which can be solved by the column-and-constraint generation algorithm. Numerical simulation results on a 33-bus distribution system demonstrate the high efficiency and robustness of the proposed method.

Long-term cold, freezing and drought: overlapping and specific regulatory mechanisms and signal transduction in tea plant (Camellia sinensis (L.) Kuntze).

Low temperatures and drought are two main environmental constraints reducing the yield and geographical distribution of horticultural crops worldwide. Understanding the genetic crosstalk between stress responses has potential importance for crop improvement. In this study, Illumina RNA-seq and Pac-Bio genome resequencing were used to annotate genes and analyze transcriptome dynamics in tea plants under long-term cold, freezing, and drought. The highest number of differentially expressed genes (DEGs) was identified under long-term cold (7,896) and freezing (7,915), with 3,532 and 3,780 upregulated genes, respectively. The lowest number of DEGs was observed under 3-day drought (47) and 9-day drought (220), with five and 112 genes upregulated, respectively. The recovery after the cold had 6.5 times greater DEG numbers as compared to the drought recovery. Only 17.9% of cold-induced genes were upregulated by drought. In total, 1,492 transcription factor genes related to 57 families were identified. However, only 20 transcription factor genes were commonly upregulated by cold, freezing, and drought. Among the 232 common upregulated DEGs, most were related to signal transduction, cell wall remodeling, and lipid metabolism. Co-expression analysis and network reconstruction showed 19 genes with the highest co-expression connectivity: seven genes are related to cell wall remodeling (GATL7, UXS4, PRP-F1, 4CL, UEL-1, UDP-Arap, and TBL32), four genes are related to calcium-signaling (PXL1, Strap, CRT, and CIPK6), three genes are related to photo-perception (GIL1, CHUP1, and DnaJ11), two genes are related to hormone signaling (TTL3 and GID1C-like), two genes are involved in ROS signaling (ERO1 and CXE11), and one gene is related to the phenylpropanoid pathway (GALT6). Based on our results, several important overlapping mechanisms of long-term stress responses include cell wall remodeling through lignin biosynthesis, o-acetylation of polysaccharides, pectin biosynthesis and branching, and xyloglucan and arabinogalactan biosynthesis. This study provides new insight into long-term stress responses in woody crops, and a set of new target candidate genes were identified for molecular breeding aimed at tolerance to abiotic stresses.

RNA-seq analysis and reconstruction of gene networks involved in response to salinity stress in quinoa (cv. Titicaca)

To better understand the mechanisms involved in salinity stress, the adaptability of quinoa cv. Titicaca—a halophytic plant—was investigated at the transcriptome level under saline and non-saline conditions. RNA-sequencing analysis of leaf tissue at the four-leaf stage by Illumina paired—end method was used to compare salt stress treatment (four days after stress at 13.8 dsm−1) and control. Among the obtained 30,846,354 transcripts sequenced, 30,303 differentially expressed genes from the control and stress treatment samples were identified, with 3363 genes expressed ≥ 2 and false discovery rate (FDR) of < 0.001. Six differential expression genes were then selected and qRT-PCR was used to confirm the RNA-seq results. Some of the genes (Include; CML39, CBSX5, TRX1, GRXC9, SnRKγ1 and BAG6) and signaling pathways discussed in this paper not been previously studied in quinoa. Genes with ≥ 2 were used to design the gene interaction network using Cytoscape software, and AgriGO software and STRING database were used for gene ontology. The results led to the identification of 14 key genes involved in salt stress. The most effective hub genes involved in salt tolerance were the heat shock protein gene family. The transcription factors that showed a significant increase in expression under stress conditions mainly belonged to the WRKY, bZIP and MYB families. Ontology analysis of salt stress-responsive genes and hub genes revealed that metabolic pathways, binding, cellular processes and cellular anatomical entity are among the most effective processes involved in salt stress.

Theory and Practice of Hydrodynamic Reconstruction in Plain River Networks

The river networks in the plains of China are in low-lying terrain with mild bed slopes and weak hydrodynamics conditions. Filled with intense human activities, these areas are characterized by serious water security problems, e.g., frequent floods, poor water self-purification capabilities, and fragile water ecosystems. In this paper, it’s found that all these problems are related to hydrodynamics, and the spatiotemporal imbalance of river network hydrodynamics is identified as the common cause of these water-related problems. From this, a theory for the hydrodynamic reconstruction of plain river networks is proposed. In addition to the importance of the flow volume, this theory highlights the role of hydrodynamics and limited energy in improving the ecological water environment. The layout of water conservancy project systems (e.g., sluices and pumping stations) is optimized to fully tapping the potential integrative benefit of projects. The optimal temporal and spatial distributions of hydrodynamic patterns is reconstructed in order to meet the needs of the integrated management of complex water-related problems in river networks. On this basis, a complete theoretical method and technical system for multiscale hydrodynamic reconstruction and multi-objective hydraulic regulation in plain river networks with weak hydrodynamics is established. The principles of the integrated management of water problems in river network areas are put forward. The practical application and efficacy of the theory are demonstrated through a case study aiming to improve the water quality of the river network in the main urban area of Yangzhou City.

Inferring Gene Regulatory Networks via Ensemble Path Consistency Algorithm Based on Conditional Mutual Information.

Utilizing gene expression data to infer gene regulatory networks has received great attention because gene regulation networks can reveal complex life phenomena by studying the interaction mechanism among nodes. However, the reconstruction of large-scale gene regulatory networks is often not ideal due to the curse of dimensionality and the impact of external noise. In order to solve this problem, we introduce a novel algorithms called ensemble path consistency algorithm based on conditional mutual information (EPCACMI), whose threshold of mutual information is dynamically self-adjusted. We first use principal component analysis to decompose a large-scale network into several subnetworks. Then, according to the absolute value of coefficient of each principal component, we could remove a large number of unrelated nodes in every subnetwork and infer the relationships among these selected nodes. Finally, all inferred subnetworks are integrated to form the structure of the complete network. Rather than inferring the whole network directly, the influence of a mass of redundant noise could be weakened. Compared with other related algorithms like MRNET, ARACNE, PCAPMI and PCACMI, the results show that EPCACMI is more effective and more robust when inferring gene regulatory networks with more nodes.

Noncoding RNA of Zika Virus Affects Interplay between Wnt-Signaling and Pro-Apoptotic Pathways in the Developing Brain Tissue.

Zika virus (ZIKV) has a unique ability among flaviviruses to cross the placental barrier and infect the fetal brain causing severe abnormalities of neurodevelopment known collectively as congenital Zika syndrome. In our recent study, we demonstrated that the viral noncoding RNA (subgenomic flaviviral RNA, sfRNA) of the Zika virus induces apoptosis of neural progenitors and is required for ZIKV pathogenesis in the developing brain. Herein, we expanded on our initial findings and identified biological processes and signaling pathways affected by the production of ZIKV sfRNA in the developing brain tissue. We employed 3D brain organoids generated from induced human pluripotent stem cells (ihPSC) as an ex vivo model of viral infection in the developing brain and utilized wild type (WT) ZIKV (producing sfRNA) and mutant ZIKV (deficient in the production of sfRNA). Global transcriptome profiling by RNA-Seq revealed that the production of sfRNA affects the expression of >1000 genes. We uncovered that in addition to the activation of pro-apoptotic pathways, organoids infected with sfRNA-producing WT, but not sfRNA-deficient mutant ZIKV, which exhibited a strong down-regulation of genes involved in signaling pathways that control neuron differentiation and brain development, indicating the requirement of sfRNA for the suppression of neurodevelopment associated with the ZIKV infection. Using gene set enrichment analysis and gene network reconstruction, we demonstrated that the effect of sfRNA on pathways that control brain development occurs via crosstalk between Wnt-signaling and proapoptotic pathways.

Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data

BackgroundUnderstanding the mechanisms underlining forage production and its biomass nutritive quality at the omics level is crucial for boosting the output of high-quality dry matter per unit of land. Despite the advent of multiple omics integration for the study of biological systems in major crops, investigations on forage species are still scarce.ResultsOur results identified substantial changes in gene co-expression and metabolite-metabolite network topologies as a result of genetic perturbation by hybridizing L. perenne with another species within the genus (L. multiflorum) relative to across genera (F. pratensis). However, conserved hub genes and hub metabolomic features were detected between pedigree classes, some of which were highly heritable and displayed one or more significant edges with agronomic traits in a weighted omics-phenotype network. In spite of tagging relevant biological molecules as, for example, the light-induced rice 1 (LIR1), hub features were not necessarily better explanatory variables for omics-assisted prediction than features stochastically sampled and all available regressors.ConclusionsThe utilization of computational techniques for the reconstruction of co-expression networks facilitates the identification of key omic features that serve as central nodes and demonstrate correlation with the manifestation of observed traits. Our results also indicate a robust association between early multi-omic traits measured in a greenhouse setting and phenotypic traits evaluated under field conditions.

Risk countermeasures of international wheat trade under the network reconstruction

In today’s globalized world, grain is not only food, but also a strategic weapon, a trump card in the game of nations. As one of the three staple grains, international trade of wheat faces high supply risk. Each country should not only pay attention to the ways in which trade risks are spread, but also pay high attention to how resources are reallocated between countries to reduce losses from supply risks. Therefore, based on complex network theory and global wheat trade data, we construct an international wheat trade network (IWTN) and analyze the feedback of network robustness under random and targeted attacks. Then, we propose a global wheat trade redistribution model through the maximum entropy principle to simulate the resource redistribution among wheat trading countries under different supply risks. Finally, changes in trade relations among major countries are analyzed. The results of the study provide an important reference for the recovery of national resources after wheat supply risk and new ideas for researchers related to international trade.

Reconstruction of rain sewer networks in the historical border of the city of Samara

The state of the centralized wastewater disposal system has undergone some changes over several decades, the article presents the problem of the negative impact of reducing water consumption costs on engineering structures, from precipitation and cementation of sediment in pipelines and concentration of runoff due to reduced flow, to the impact on the operation of pumping stations and treatment facilities. It is suggested that the problem under study can be partially solved with the help of rainwater, which is discharged without proper treatment into water bodies. The article discusses the existing drainage systems in Samara, the cost of treatment facilities for rainwater, as well as the proposed project for the reconstruction of the rain network for outlets located within the boundaries of the historical part of Samara.

Study on the Impact of Social Capital on Agricultural Land Transfer Decision: Based on 1017 Questionnaires in Hubei Province

In building a market for the transfer of land contracting rights, it is crucial to clarify the influencing factors for farmers’ farmland transfer decisions to promote the orderly transfer of farmland. This article investigates the impact of social capital on farmland transfer and analyzes the moderating effect of marketization of farmland transfer using research data from 1017 farm households in Hubei Province. The results showed that social capital significantly contributes to farmers’ farmland transfer decisions; social networks and social trust have more potent effects on farmland transfer-in than farmland transfer-out and social norms have more substantial effects on farmland transfer-out than farmland transfer-in; and farmland transfer marketization plays a moderating role in the process of social capital’s influence on farmland transfer decisions. In terms of farmland transfer-out, marketization of farmland transfer plays a negative moderating role between social network, social trust, and farmland transfer decision, and a positive moderating role between social norm and farmland transfer decision. In terms of farmland transfer-in, marketization of farmland transfer plays a negative moderating role between social network, social trust, and farmland transfer decision, and no moderating role in social norm and farmland transfer decision. The study concludes that local governments need to strengthen the construction of social norms and the reconstruction of social trust and networks to create a favorable environment for agricultural land transfer while improving the construction of regional agricultural land transfer markets and promoting the development of market transformation.

Construction and analysis of multi-relationship bipartite network model

Bipartite networks that characterize complex relationships among data arise in various domains. The existing bipartite network models are mainly based on a type of relationship between objects, and cannot effectively describe multiple relationships in the real world. In this paper, we propose a multi-relationship bipartite network (MBN) model, which can describe multiple relationships between two types of objects, and realizes simple weighted bipartite network reconstruction. Our model contains three major modules, namely multi-relationship bipartite network modeling (MBNM), multi-relationship aggregation module (MAM) and network reconstruction module (NRM). In MBNM, a multi-relationship bipartite network is proposed to describe multiple relationships between two types of objects. In the MAM, considering that different relationships have different information for the model, we introduce a novel relationship-level attention mechanism, and the aggregation of multiple relationships is carried out through the importance of each relationship. Based on the learning framework, the NRM can learn the potential representations of nodes after multi-relationship aggregation, and design a nonlinear fusion mechanism to reconstruct weighted bipartite network. We conducted extensive experiments on three real-world datasets and the results show that multi-relationship aggregation can effectively improve the performance of the model. In addition, experiments also show that our model can outperform existing competitive baseline method.

BioGraph: Data Model for Linking and Querying Diverse Biological Metadata.

Studying the association of gene function, diseases, and regulatory gene network reconstruction demands data compatibility. Data from different databases follow distinct schemas and are accessible in heterogenic ways. Although the experiments differ, data may still be related to the same biological entities. Some entities may not be strictly biological, such as geolocations of habitats or paper references, but they provide a broader context for other entities. The same entities from different datasets can share similar properties, which may or may not be found within other datasets. Joint, simultaneous data fetching from multiple data sources is complicated for the end-user or, in many cases, unsupported and inefficient due to differences in data structures and ways of accessing the data. We propose BioGraph-a new model that enables connecting and retrieving information from the linked biological data that originated from diverse datasets. We have tested the model on metadata collected from five diverse public datasets and successfully constructed a knowledge graph containing more than 17 million model objects, of which 2.5 million are individual biological entity objects. The model enables the selection of complex patterns and retrieval of matched results that can be discovered only by joining the data from multiple sources.