Combined Network Analysis Research Articles

Protective Effects of Peucedanum japonicum Extract against Osteoarthritis in an Animal Model Using a Combined Systems Approach for Compound-Target Prediction.

Peucedanum japonicum Thunberg is an herbal medicine used to treat neuralgia, rheumatoid arthritis, and inflammatory-related diseases. However, its effects on osteoarthritis (OA) and its regulatory mechanisms have not been investigated by network analysis. Here, we investigated the pharmacological effects of Peucedanum japonicum extract (PJE) on OA, by combining in vivo effective verification and network pharmacology prediction. Rats in which OA was induced by monosodium iodoacetate (MIA) were treated with PJE (200 mg/kg), and histopathological parameters, weight bearing distribution and inflammatory factors in serum and joint tissue were measured after 28 days of treatment. Additionally, in silico network analysis was used to predict holistic OA regulatory mechanisms of PJE. The results showed that PJE exerted potential protective effects by recovering hind paw weight bearing distribution, alleviating histopathological features of cartilage and inhibiting inflammatory mediator levels in the OA rat model. Furthermore, network analysis identified caspase-3 (CASP3), caspase-7 (CASP7), and cytochrome P450 2D6 (CYP2D6) as potential target genes; in addition, the TNF (Tumor necrosis factor) signaling pathway was linked to OA therapeutic action. Our combined animal OA model and network analysis confirmed the therapeutic effects of PJE against OA and identified intracellular signaling pathways, active compounds and target genes linked to its therapeutic action.

Political topic-communities and their framing practices in the Dutch Twittersphere

In light of the need for political plurality and informed debate this study questions information distribution and curation on Twitter. We contribute to the understanding of ideological homophily by exploring the notion of the ‘echo chamber’. Using a sample of two weeks of Dutch Twitter data, we combine network analysis of retweet networks, with qualitative reading and categorisation of engagement with media content in tweets within political topic communities. We found that media references were predominantly framed in affirmative ways in relation to the referenced medium content. Our findings show that users consciously select media messages that correspond with the general sentiment within their topic community, or frame them accordingly. We see this as a willful ‘echo chamber’, or a ‘repillarisation’.

Socialbots in politischen Online-Konversationen. Eine (überschätzte) Gefahr für demokratische Willensbildung im Internet?

The manipulative use of so-called social bots on the Internet is increasingly regarded as a problem and danger to the public and to democracy . At the same time, however, research regarding the political use of social bots is nascent. Up to now, only few empirical studies on the topic exist, which primarily make use of automated big data analyses to identify social bots and their political agenda . In this article we argue that existing research on the role of social bots in the 2016 US presidential election campaign has significant shortcomings, which may limit the validity of results on the influence of social bots in political conversations on Twitter . While existing research suggests that social bots (identified using automated methods) were able to influence online conversations on Twitter and demonstrated bias towards the Republican presidential candidate Donald Trump, we contend that these findings were in large part the result of methodological choices that are inherent to conducting empirical research at scale using big data . In this paper, using Twitter data collected during the 2016 first US presidential debate, we combine network analysis methods with qualitative sociological approaches and find in fact that social bots tweeted rather negatively about Trump and did not exert significant influence on online conversations .

"Thinking" vs. "Talking": Differential Autocrine Inflammatory Networks in Isolated Primary Hepatic Stellate Cells and Hepatocytes under Hypoxic Stress.

We hypothesized that isolated primary mouse hepatic stellate cells (HSC) and hepatocytes (HC) would elaborate different inflammatory responses to hypoxia with or without reoxygenation. We further hypothesized that intracellular information processing (“thinking”) differs from extracellular information transfer (“talking”) in each of these two liver cell types. Finally, we hypothesized that the complexity of these autocrine responses might only be defined in the absence of other non-parenchymal cells or trafficking leukocytes. Accordingly, we assayed 19 inflammatory mediators in the cell culture media (CCM) and whole cell lysates (WCLs) of HSC and HC during hypoxia with and without reoxygenation. We applied a unique set of statistical and data-driven modeling techniques including Two-Way ANOVA, hierarchical clustering, Principal Component Analysis (PCA) and Network Analysis to define the inflammatory responses of these isolated cells to stress. HSC, under hypoxic and reoxygenation stresses, both expressed and secreted larger quantities of nearly all inflammatory mediators as compared to HC. These differential responses allowed for segregation of HSC from HC by hierarchical clustering. PCA suggested, and network analysis supported, the hypothesis that above a certain threshold of cellular stress, the inflammatory response becomes focused on a limited number of functions in both HSC and HC, but with distinct characteristics in each cell type. Network analysis of separate extracellular and intracellular inflammatory responses, as well as analysis of the combined data, also suggested the presence of more complex inflammatory “talking” (but not “thinking”) networks in HSC than in HC. This combined network analysis also suggested an interplay between intracellular and extracellular mediators in HSC under more conditions than that observed in HC, though both cell types exhibited a qualitatively similar phenotype under hypoxia/reoxygenation. Our results thus suggest that a stepwise series of computational and statistical analyses may help decipher how cells respond to environmental stresses, both within the cell and in its secretory products, even in the absence of cooperation from other cells in the liver.

Combining network analysis with epidemiological data to inform risk-based surveillance: Application to hepatitis E virus (HEV) in pigs

Animal movements between farms are a major route of pathogen spread in the pig production sector. This study aimed to pair network analysis and epidemiological data in order to evaluate the impact of animal movements on pathogen prevalence in farms and assess the risk of local areas being exposed to diseases due to incoming movements. Our methodology was applied to hepatitis E virus (HEV), an emerging foodborne zoonotic agent of concern that is highly prevalent in pig farms. Firstly, the pig movement network in France (data recorded in 2013) and the results of a nation-wide seroprevalence study (data collected in 178 farms in 2009) were modelled and analysed. The link between network centrality measures of farms and HEV seroprevalence levels was explored using a generalised linear model. The in-degree and ingoing closeness of farms were found to be statistically associated with high HEV within-farm seroprevalence (p<0.05). Secondly, the risk of a French département (i.e. French local administrative areas) being exposed to HEV was calculated by combining the distribution of farm-level HEV prevalence in source départements with the number of movements coming from those same départements. By doing so, the risk of exposure for départements was mapped, highlighting differences between geographical patterns of HEV prevalence and the risk of exposure to HEV. These results suggest that not only highly prevalent areas but also those having at-risk movements from infected areas should be monitored. Pathogen management and surveillance options in the pig production sector should therefore take animal movements into consideration, paving the way for the development of targeted and risk-based disease surveillance strategies.

A dynamic framework for analyzing technology standardisation using network analysis and game theory

ABSTRACTAs more technologies and industries converge, technology standards are more likely to be a strategic factor for firms and governments that are interested in the market with standards-based competition. From the previous research, a new standardisation framework was proposed by combining network analysis and the game theory model but was constrained by feasibility and dynamic approach. In this study, the case of the standards war between HD-DVD and Blu-ray was analyzed with patent data as an empirical case considering a dynamic framework. With this framework, we observed a change in a firm’s technology relations and could predict the decline in a firm’s preference and the shift of equilibrium ahead of Toshiba’s resignation.

Factors associated with needle sharing among people who inject drugs in Yunnan, China: a combined network and regression analysis.

BackgroundNetwork analyses have been widely utilized to evaluate large datasets, but have not yet been used to explore factors associated with risk behaviours. In combination with traditional regression analysis, network analyses may provide useful information and highlight key factors for reducing needle sharing behaviours among people who inject drugs (PWID).MethodsSociodemographic data, and information on injection behaviour and sexual practices were collected from a cross-sectional survey that was conducted with PWID in five prefectures of Yunnan province, China. A combination of logistic regression and correlation network analyses were used to explore key factors for reducing needle-sharing behaviours among PWID.ResultsIn a total of 1 049 PWID, 37.5 % had a history of needle or syringe sharing. The logistic analysis showed that Zhaotong, Qujing, Dehong, or Lincang residents, diazepam use, longer injection duration, needle reuse, and infection with HIV, viral hepatitis, tuberculosis and/or malaria were independently associated with needle sharing. The correlation network analyses showed that, compared to PWID who had never shared needles, PWID who did share needles would achieve harm reduction goals faster and more permanently. HIV serostatus and marital status were found to be closely associated with other risk factors. By combining regression analyses with network analyses, it was shown that PWID who are HIV seropositive will be an ideal target group for harm reduction programs.ConclusionNeedle-sharing behaviours are common among PWID in Yunnan, and harm reduction programs may help PWID who are HIV seropositive reduce risk behaviours and prevent blood borne diseases.Electronic supplementary materialThe online version of this article (doi:10.1186/s40249-016-0169-y) contains supplementary material, which is available to authorized users.

Insights into the Ecology and Evolution of Polyploid Plants through Network Analysis.

Polyploidy is a widespread phenomenon throughout eukaryotes, with important ecological and evolutionary consequences. Although genes operate as components of complex pathways and networks, polyploid changes in genes and gene expression have typically been evaluated as either individual genes or as a part of broad-scale analyses. Network analysis has been fruitful in associating genomic and other 'omic'-based changes with phenotype for many systems. In polyploid species, network analysis has the potential not only to facilitate a better understanding of the complex 'omic' underpinnings of phenotypic and ecological traits common to polyploidy, but also to provide novel insight into the interaction among duplicated genes and genomes. This adds perspective to the global patterns of expression (and other 'omic') change that accompany polyploidy and to the patterns of recruitment and/or loss of genes following polyploidization. While network analysis in polyploid species faces challenges common to other analyses of duplicated genomes, present technologies combined with thoughtful experimental design provide a powerful system to explore polyploid evolution. Here, we demonstrate the utility and potential of network analysis to questions pertaining to polyploidy with an example involving evolution of the transgressively superior cotton fibres found in polyploid Gossypium hirsutum. By combining network analysis with prior knowledge, we provide further insights into the role of profilins in fibre domestication and exemplify the potential for network analysis in polyploid species.

Tracking Inter-Regional Carbon Flows: A Hybrid Network Model.

The mitigation of anthropogenic carbon emissions has moved beyond the local scale because they diffuse across boundaries, and the consumption that triggers emissions has become regional and global. A precondition of effective mitigation is to explicitly assess inter-regional transfer of emissions. This study presents a hybrid network model to track inter-regional carbon flows by combining network analysis and input-output analysis. The direct, embodied, and controlled emissions associated with regions are quantified for assessing various types of carbon flow. The network-oriented metrics called "controlled emissions" is proposed to cover the amount of carbon emissions that can be mitigated within a region by adjusting its consumption. The case study of the Jing-Jin-Ji Area suggests that CO2 emissions embodied in products are only partially controlled by a region from a network perspective. Controlled carbon accounted for about 70% of the total embodied carbon flows, while household consumption only controlled about 25% of Beijing's emissions, much lower than its proportion of total embodied carbon. In addition to quantifying emissions, the model can pinpoint the dominant processes and sectors of emissions transfer across regions. This technique is promising for searching efficient pathways of coordinated emissions control across various regions connected by trade.

TRC Networks and Systemic Risk

The authors introduce a new approach to identifying and monitoring systemic risk that combines network analysis and tail risk contribution (TRC). Network analysis provides great flexibility in representing and exploring linkages between institutions, but it can be overly general in describing the risk exposures of one entity to another. TRC provides a more focused view of key systemic risks and richer financial intuition, but it may miss important linkages between financial institutions. Integrating these two methods can provide information on key relationships between institutions that may become relevant during periods of systemic stress. The authors demonstrate this approach using the exposures of money market funds to major financial institutions during July 2011. The results for their example suggest that TRC networks can highlight both institutions and funds that may become distressed during a financial crisis.

Joint genetic and network analyses identify loci associated with root growth under NaCl stress in Arabidopsis thaliana.

Plants have evolved a series of tolerance mechanisms to saline stress, which perturbs physiological processes throughout the plant. To identify genetic mechanisms associated with salinity tolerance, we performed linkage analysis and genome-wide association study (GWAS) on maintenance of root growth of Arabidopsis thaliana in hydroponic culture with weak and severe NaCl toxicity. The top 200 single-nucleotide polymorphisms (SNPs) determined by GWAS could cumulatively explain approximately 70% of the variation observed at each stress level. The most significant SNPs were linked to the genes of ATP-binding cassette B10 and vacuolar proton ATPase A2. Several known salinity tolerance genes such as potassium channel KAT1 and calcium sensor SOS3 were also linked to SNPs in the top 200. In parallel, we constructed a gene co-expression network to independently verify that particular groups of genes work together to a common purpose. We identify molecular mechanisms to confer salt tolerance from both predictable and novel physiological sources and validate the utility of combined genetic and network analysis. Additionally, our study indicates that the genetic architecture of salt tolerance is responsive to the severity of stress. These gene datasets are a significant information resource for a following exploration of gene function.

An integrated approach for spatial analysis of the coupling relationship between tourism footprint and environment in the Beijing–Tianjin–Hebei region of China

ABSTRACTThis study aims at examining the spatial relationship between tourism footprint network and comprehensive environment which contains economic, social and ecological environment. The integrated approach combining network analysis and the coupling coordination degree model with spatial autocorrelation analysis was developed to analyze the network structure of regional tourism footprint, to evaluate the coupling coordination degree of tourism footprint network and comprehensive environment, and to explore the spatial pattern of the coupling coordination development. Data were collected from the Beijing–Tianjin–Hebei region of China. The results revealed that tourism footprints were concentrated in a few cities, and the regional coupling coordination degree showed a weak negative spatial autocorrelation. Beijing, Tianjin and Baoding had high agglomeration, while their spillover effect is not significant. Shijiazhuang and Qinhuangdao located in the High–Low area are potential growth poles. Findings indicate the integrated approach established is objective and effective, and contributes to macro policy-making of regional tourism development.

Resilience to climate change in a cross-scale tourism governance context: a combined quantitative-qualitative network analysis

Luthe, T., and R. Wyss. 2016. Resilience to climate change in a cross-scale tourism governance context: a combined quantitative-qualitative network analysis. Ecology and Society 21(1):27.http://dx.doi.org/10.5751/ES-08234-210127

Abstract A20: Mitochondrial phosphoenolpyruvate carboxykinase (PCK2) regulates metabolic adaptation and enables glucose-independent tumor cell growth

Abstract Cancer cells must adapt metabolically to survive and proliferate when nutrients are limiting. Here we used combined transcriptional-metabolomic network analysis to identify metabolic pathways that support glucose-independent tumor cell growth. We found that glucose deprivation stimulated the re-wiring of the tricarboxylic acid (TCA) cycle and early steps of gluconeogenesis to promote cell proliferation under low glucose. Glucose limitation promoted the production of phosphoenolpyruvate (PEP) from glutamine, which was used to fuel biosynthetic pathways normally sustained by glucose, including serine and purine biosynthesis. Mitochondrial PEP-carboxykinase (PCK2) was required for this glutamine-dependent metabolic reprogramming. PCK2 expression was dependent on the hypoxia-inducible factors (HIFs), and required to maintain tumor cell proliferation under limiting glucose availability. Elevated PCK2 expression is observed in several human tumor types, and enriched in tumor tissue from non-small cell lung cancer (NSCLC) patients. Our results define a novel role for PCK2 in cancer cell metabolic reprogramming that promotes glucose-independent cell growth and metabolic stress resistance in human tumors. Citation Format: Emma E. Vincent, Alexey Sergushichev, Takla Griss, Maxime N. Artyomov, Russell G. Jones. Mitochondrial phosphoenolpyruvate carboxykinase (PCK2) regulates metabolic adaptation and enables glucose-independent tumor cell growth. [abstract]. In: Proceedings of the AACR Special Conference: Metabolism and Cancer; Jun 7-10, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(1_Suppl):Abstract nr A20.

TRC Networks and Systemic Risk

We introduce a new approach for identifying and monitoring systemic risk that combines network analysis and tail risk contribution (TRC). Network analysis provides great flexibility in representing and exploring linkages between institutions, but can be overly general in describing the risk exposures of one entity to another. TRC provides a more focused view of key systemic risks fng with richer financial intuition, but it may miss important linkages between financial institutions. Integrating these two methods can provide information on key relationships between institutions that may become relevant during periods of systemic stress. We demonstrate this approach using exposures of money market funds to major financial institutions during July 2011. The results for our example suggest that TRC networks can highlight both institutions and funds that may become distressed during a financial crisis.

Exploring Green Brand Associations through a Network Analysis Approach

ABSTRACTIn compliance with society's growing concern for the environment, an increasing number of enterprises are willing to act in socially responsible and environmentally conscious ways. Green‐related issues for enterprises have been an important academic research topic for at least three decades; however, few studies have focused on green brand associations. Based on the associative network perspective, the authors attempt to explore green brand associations through a network analysis approach. By combining network analysis with the qualitative elicitation method, the authors utilize commonly used measures of network analysis (i.e., centrality and cohesion measures) to reveal the content and structure of associative networks for green brands; next, they demonstrate empirical support for the findings. The results contribute to providing brand researchers with different analytical perspectives on the existing knowledge about green brand associations and offering strategic information for green brand practitioners.

Toward BIM-Enabled Decision Making for In-Building Response Missions

Decision making for rescue and evacuation is critical for disaster response operations not only in open spaces but also in buildings. For infrastructures with large and complex geometrical layouts, decision making could be facilitated with computer-aided approaches for better accuracy and efficiency. This paper aims to respond to fire in buildings. We combined network analysis with building information modeling (BIM) to facilitate the decision making for response operations. Building geometry is retrieved from the BIM model for graph construction and route finding. In this paper, the visibility graph (VG) and medial axis transform (MAT) are evaluated for graph construction. A hybrid method that combines VG and MAT is proposed, and encouraging results have been shown. Risks based on building materials and traffic of pedestrians are to be adapted for network edge costs to enable the lowest utility route finding in the future. The accuracy and efficiency for emergency decisions are expected to be improved for in-building rescue and evacuation operations.

Mitochondrial Phosphoenolpyruvate Carboxykinase Regulates Metabolic Adaptation and Enables Glucose-Independent Tumor Growth

Cancer cells adapt metabolically to proliferate under nutrient limitation. Here we used combined transcriptional-metabolomic network analysis to identify metabolic pathways that support glucose-independent tumor cell proliferation. We found that glucose deprivation stimulated re-wiring of the tricarboxylic acid (TCA) cycle and early steps of gluconeogenesis to promote glucose-independent cell proliferation. Glucose limitation promoted the production of phosphoenolpyruvate (PEP) from glutamine via theactivity of mitochondrial PEP-carboxykinase (PCK2). Under these conditions, glutamine-derived PEP was used to fuel biosynthetic pathways normally sustained by glucose, including serine and purine biosynthesis. PCK2 expression was required tomaintain tumor cell proliferation under limited-glucose conditions invitro and tumor growth invivo. Elevated PCK2 expression is observed in several human tumor types and enriched in tumor tissue from non-small-cell lung cancer (NSCLC) patients. Our results define a role for PCK2 in cancer cell metabolic reprogramming that promotes glucose-independent cell growth and metabolic stress resistance in human tumors.

Cryptic diversity within two endemic crayfish species of the Southeastern US revealed by molecular genetics and geometric morphometrics

Crayfishes reach their peak biodiversity in North America, with the Mobile Basin of the southeastern US harboring high levels of endemism. However, the genetic diversity and phylogeography of crayfishes from this region are poorly understood. Here, the genetic structure and shape variation among multiple populations of Cambarus englishi Hobbs and Halland Cambarus halli Hobbs, both endemic to the Tallapoosa River, were examined from three catchments of the drainage (Upper, Little, and Middle) using mitochondrial cytochrome c oxidase subunit I (COI) gene sequences and geometric morphometrics. Notably, a combined COI network analysis found haplotypes from each species coalescing into common networks based on catchment, with significant genetic differentiation being identified between nearly all populations. Geometric morphometric analyses largely agreed with genetic analyses, with shape overlap yet significant differences between species and between the three catchments for C. halli (but not C. englishi). Together this suggests that genetic separation is manifested in part by shape variation and overall, these crayfishes appear to represent “cryptic species complexes.” Our data provide evidence for the establishment of evolutionarily significant units along the Upper, Little, and Middle Tallapoosa and that there is considerable population isolation and cryptic diversity within North American crayfishes.

Combining network analysis with Cognitive Work Analysis: insights into social organisational and cooperation analysis

Cognitive Work Analysis (CWA) allows complex, sociotechnical systems to be explored in terms of their potential configurations. However, CWA does not explicitly analyse the manner in which person-to-person communication is performed in these configurations. Consequently, the combination of CWA with Social Network Analysis provides a means by which CWA output can be analysed to consider communication structure. The approach is illustrated through a case study of a military planning team. The case study shows how actor-to-actor and actor-to-function mapping can be analysed, in terms of centrality, to produce metrics of system structure under different operating conditions.