scholarly journals Microbiome Networks: A Systems Framework for Identifying Candidate Microbial Assemblages for Disease Management

2016 ◽  
Vol 106 (10) ◽  
pp. 1083-1096 ◽  
Author(s):  
R. Poudel ◽  
A. Jumpponen ◽  
D. C. Schlatter ◽  
T. C. Paulitz ◽  
B. B. McSpadden Gardener ◽  
...  
Keyword(s):  
Network Analysis ◽  
Disease Management ◽  
A Priori ◽  
Network Models ◽  
Disease Suppression ◽  
Plant Host ◽  
Network Analyses ◽  
General Network ◽  
Positive Direct ◽  
Systems Framework

Network models of soil and plant microbiomes provide new opportunities for enhancing disease management, but also challenges for interpretation. We present a framework for interpreting microbiome networks, illustrating how observed network structures can be used to generate testable hypotheses about candidate microbes affecting plant health. The framework includes four types of network analyses. “General network analysis” identifies candidate taxa for maintaining an existing microbial community. “Host-focused analysis” includes a node representing a plant response such as yield, identifying taxa with direct or indirect associations with that node. “Pathogen-focused analysis” identifies taxa with direct or indirect associations with taxa known a priori as pathogens. “Disease-focused analysis” identifies taxa associated with disease. Positive direct or indirect associations with desirable outcomes, or negative associations with undesirable outcomes, indicate candidate taxa. Network analysis provides characterization not only of taxa with direct associations with important outcomes such as disease suppression, biofertilization, or expression of plant host resistance, but also taxa with indirect associations via their association with other key taxa. We illustrate the interpretation of network structure with analyses of microbiomes in the oak phyllosphere, and in wheat rhizosphere and bulk soil associated with the presence or absence of infection by Rhizoctonia solani.

Network Analysis: A Systems Framework to Address Grand Challenges in Plant Pathology

2018 ◽  
Vol 56 (1) ◽  
pp. 559-580 ◽  
Author(s):  
K.A. Garrett ◽  
R.I. Alcalá-Briseño ◽  
K.F. Andersen ◽  
C.E. Buddenhagen ◽  
R.A. Choudhury ◽  
...  
Keyword(s):  
Plant Pathology ◽  
Network Analysis ◽  
Disease Management ◽  
Agricultural Development ◽  
Research Priorities ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Exponential Random Graph ◽  
Plant Disease Management ◽  
Systems Framework

Plant pathology must address a number of challenges, most of which are characterized by complexity. Network analysis offers useful tools for addressing complex systems and an opportunity for synthesis within plant pathology and between it and relevant disciplines such as in the social sciences. We discuss applications of network analysis, which ultimately may be integrated together into more synthetic analyses of how to optimize plant disease management systems. The analysis of microbiome networks and tripartite phytobiome networks of host-vector-pathogen interactions offers promise for identifying biocontrol strategies and anticipating disease emergence. Linking epidemic network analysis with social network analysis will support strategies for sustainable agricultural development and for scaling up solutions for disease management. Statistical tools for evaluating networks, such as Bayesian network analysis and exponential random graph models, have been underused in plant pathology and are promising for informing strategies. We conclude with research priorities for network analysis applications in plant pathology.

scholarly journals Psychometric Network Analysis of the Hungarian WAIS

2019 ◽  
Author(s):  
Christopher J. Schmank ◽  
Sara Anne Goring ◽  
Kristof Kovacs ◽  
Andrew R. A. Conway
Keyword(s):  
Network Analysis ◽  
Cognitive Ability ◽  
Cognitive Abilities ◽  
Latent Variable ◽  
Network Models ◽  
Latent Variable Models ◽  
Latent Variable Modeling ◽  
Attractive Alternative ◽  
Adult Intelligence Scale ◽  
Network Analyses

The positive manifold—the finding that cognitive ability measures demonstrate positive correlations with one another—has led to models of intelligence that include a general cognitive ability or general intelligence (g). This view has been reinforced using factor analysis and latent variable models. However, a new theory of intelligence, Process Overlap Theory (POT; Kovacs & Conway, 2016), posits that g is not a psychological attribute but an index of cognitive abilities that results from an interconnected network of cognitive processes. From this perspective, psychometric network analysis is an attractive alternative to latent variable modeling. Network analyses display partial correlations among observed variables that demonstrate direct relationships among observed variables. To demonstrate the benefits of this approach, the Hungarian Wechsler Adult Intelligence Scale Fourth Edition (H-WAIS-IV; Wechsler, 2008) was analyzed using both psychometric network analysis and latent variable modeling. Network models were directly compared to latent variable models. Results indicate that the H-WAIS-IV data was better fit by network models than by latent variable models. We argue that POT, and network models, provide a more accurate view of the structure of intelligence than traditional approaches.

scholarly journals Teachers’ professional collaboration and trust relationships: An inferential social network analysis of teacher teams

2021 ◽  
pp. 003452372110315
Author(s):  
Nina Kolleck ◽  
Johannes Schuster ◽  
Ulrike Hartmann ◽  
Cornelia Gräsel
Keyword(s):  
Social Network ◽  
Teacher Collaboration ◽  
Classroom Practices ◽  
Research Literature ◽  
School Level ◽  
Teacher Teams ◽  
Exponential Random Graph Models ◽  
Network Analyses ◽  
Trust Relationships ◽  
General Network

In recent years, teachers around the world have been increasingly confronted with various expectations concerning the improvement of their classroom practices and school activities. One factor widely acknowledged to facilitate school and classroom improvement is a strong collaborative culture among teachers. As such, teachers are expected to work in teacher teams, to collaborate closely with colleagues, to co-construct classroom practices, and thus to strengthen trust relationships within the team. A growing number of researchers has analyzed how teachers address these expectations. They suggest that there is a link between teachers’ embeddedness in collaboration networks and teachers’ trust relationships. The present study seeks to contribute to the research literature by presenting results of Social Network Analyses (SNA) and exponential random graph models (ERGMs) on teacher collaboration in nine secondary schools in Germany (N = 366 teachers). We investigate how the involvement of teachers in co-constructive collaboration in schools, measured by the amount of team teaching (TT), relates to teachers’ trust levels. Results of our analyses suggest that a high amount of TT is not necessarily related to a higher degree of trust among teachers at the school level. However, a high involvement of teachers in TT is related positively to their being perceived as trustworthy. Furthermore, the emergence of trust relations in teacher networks depends on general network characteristics, such as homophily, reciprocity and transitivity.

Abstract 313: Visualizing Out-Of-Hospital Cardiac Arrest in the Young: A Network Analysis of the Cardiac Arrest Registry for Enhanced Survival in Chicago

Circulation ◽  
2019 ◽  
Vol 140 (Suppl_2) ◽  
Author(s):  
Alyssa Vermeulen ◽  
Marina Del Rios ◽  
Teri L Campbell ◽  
Hai Nguyen ◽  
Hoang H Nguyen
Keyword(s):  
Cardiac Arrest ◽  
Network Analysis ◽  
Emergency Room ◽  
American Indians ◽  
Network Models ◽  
Spontaneous Circulation ◽  
Data Set ◽  
Qualitative And Quantitative ◽  
Complex Network Models ◽  
Hospital Cardiac Arrest

Introduction: The interactions of various variables on out-of-hospital cardiac arrest (OHCA) in the young (1-35 years old) outcomes are complex. Network models have emerged as a way to abstract complex systems and gain insights into relational patterns among observed variables. Hypothesis: Network analysis helps provide qualitative and quantitative insights into how various variables interact with each other and affect outcomes in OHCA in the young. Methods: A mixed graphical network analysis was performed using variables collected by CARES. The network allows the visualization and quantification of each unique interaction between two variables that cannot be explained away by other variables in the data set. The strength of the underlying interaction is proportional to the thickness of the connections (edges) between the variables (nodes). We used the mgm package in R. Results: Figure 1 shows the network of the OHCA in the young cases in Chicago from 2013 to 2017. There are apparent clusters. Sustained return of spontaneous circulation and hypothermia are strongly correlated with survival and neurological outcomes. This cluster is in turn connected to the rest of the network by survival to emergency room. The interaction between any two variables can also be quantified. For example, American Indians cases occur more often in disadvantaged locations when compared to Whites (OR 4.5). The network also predicts how much one node can be explained by adjacent nodes. Only 20% of survival to emergency room is explained by its adjacent nodes. The remaining 80% is attributed to variables not represented in this network. This suggests that interventions to improve this node is difficult unless further data is available. Conclusion: Network analysis provides both a qualitative and quantitative evaluation of the complex system governing OHCA in the young. The networks predictive capability could help in identifying the most effective interventions to improve outcomes.

scholarly journals Why it is important to consider negative ties when studying polarized debates: A signed network analysis of a Dutch cultural controversy on Twitter

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256696
Author(s):  
Anna Keuchenius ◽  
Petter Törnberg ◽  
Justus Uitermark
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Network Analysis ◽  
Language Processing ◽  
User Interaction ◽  
User Interactions ◽  
Online Interactions ◽  
Network Analyses ◽  
Social Media Platforms ◽  
Signed Network

Despite the prevalence of disagreement between users on social media platforms, studies of online debates typically only look at positive online interactions, represented as networks with positive ties. In this paper, we hypothesize that the systematic neglect of conflict that these network analyses induce leads to misleading results on polarized debates. We introduce an approach to bring in negative user-to-user interaction, by analyzing online debates using signed networks with positive and negative ties. We apply this approach to the Dutch Twitter debate on ‘Black Pete’—an annual Dutch celebration with racist characteristics. Using a dataset of 430,000 tweets, we apply natural language processing and machine learning to identify: (i) users’ stance in the debate; and (ii) whether the interaction between users is positive (supportive) or negative (antagonistic). Comparing the resulting signed network with its unsigned counterpart, the retweet network, we find that traditional unsigned approaches distort debates by conflating conflict with indifference, and that the inclusion of negative ties changes and enriches our understanding of coalitions and division within the debate. Our analysis reveals that some groups are attacking each other, while others rather seem to be located in fragmented Twitter spaces. Our approach identifies new network positions of individuals that correspond to roles in the debate, such as leaders and scapegoats. These findings show that representing the polarity of user interactions as signs of ties in networks substantively changes the conclusions drawn from polarized social media activity, which has important implications for various fields studying online debates using network analysis.

scholarly journals Studying Positive and Negative Direct and Extended Contact: Complementing Self-Reports With Social Network Analysis

2017 ◽  
Vol 43 (11) ◽  
pp. 1566-1581 ◽  
Author(s):  
Ralf Wölfer ◽  
Eva Jaspers ◽  
Danielle Blaylock ◽  
Clarissa Wigoder ◽  
Joanne Hughes ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Intergroup Relations ◽  
Large Scale ◽  
Intergroup Contact ◽  
Network Analyses ◽  
Self Reports ◽  
Extended Contact ◽  
Contact Parameters

Traditionally, studies of intergroup contact have primarily relied on self-reports, which constitute a valid method for studying intergroup contact, but has limitations, especially if researchers are interested in negative or extended contact. In three studies, we apply social network analyses to generate alternative contact parameters. Studies 1 and 2 examine self-reported and network-based parameters of positive and negative contact using cross-sectional datasets ( N = 291, N = 258), indicating that both methods help explain intergroup relations. Study 3 examines positive and negative direct and extended contact using the previously validated network-based contact parameters in a large-scale, international, and longitudinal dataset ( N = 12,988), demonstrating that positive and negative direct and extended contact all uniquely predict intergroup relations (i.e., intergroup attitudes and future outgroup contact). Findings highlight the value of social network analysis for examining the full complexity of contact including positive and negative forms of direct and extended contact.

scholarly journals Subtyping psychological distress in the population: a semi-parametric network approach

2019 ◽  
Vol 29 ◽  
Author(s):  
S. de Vos ◽  
S. Patten ◽  
E. C. Wit ◽  
E. H. Bos ◽  
K. J. Wardenaar ◽  
...  
Keyword(s):  
Psychological Distress ◽  
Community Sample ◽  
A Priori ◽  
Network Models ◽  
Optimal Choice ◽  
Data Driven ◽  
Network Density ◽  
Parametric Network ◽  
Distress Scale ◽  
Non Parametric

Abstract Aims The mechanisms underlying both depressive and anxiety disorders remain poorly understood. One of the reasons for this is the lack of a valid, evidence-based system to classify persons into specific subtypes based on their depressive and/or anxiety symptomatology. In order to do this without a priori assumptions, non-parametric statistical methods seem the optimal choice. Moreover, to define subtypes according to their symptom profiles and inter-relations between symptoms, network models may be very useful. This study aimed to evaluate the potential usefulness of this approach. Methods A large community sample from the Canadian general population (N = 254 443) was divided into data-driven clusters using non-parametric k-means clustering. Participants were clustered according to their (co)variation around the grand mean on each item of the Kessler Psychological Distress Scale (K10). Next, to evaluate cluster differences, semi-parametric network models were fitted in each cluster and node centrality indices and network density measures were compared. Results A five-cluster model was obtained from the cluster analyses. Network density varied across clusters, and was highest for the cluster of people with the lowest K10 severity ratings. In three cluster networks, depressive symptoms (e.g. feeling depressed, restless, hopeless) had the highest centrality. In the remaining two clusters, symptom networks were characterised by a higher prominence of somatic symptoms (e.g. restlessness, nervousness). Conclusion Finding data-driven subtypes based on psychological distress using non-parametric methods can be a fruitful approach, yielding clusters of persons that differ in illness severity as well as in the structure and strengths of inter-symptom relationships.

scholarly journals Information sharing in supply chains: a review of risks and opportunities using the systematic literature network analysis (SLNA)

2019 ◽  
Vol 24 (1) ◽  
pp. 5-21 ◽  
Author(s):  
Claudia Colicchia ◽  
Alessandro Creazza ◽  
Carlo Noè ◽  
Fernanda Strozzi
Keyword(s):  
Supply Chain ◽  
Network Analysis ◽  
Literature Review ◽  
Supply Chains ◽  
Information Sharing ◽  
Content Type ◽  
Network Analyses ◽  
Dynamic Representation ◽  
Research Areas ◽  
Over Time

Purpose The purpose of this paper is to identify and discuss the most important research areas on information sharing in supply chains and related risks, taking into account their evolution over time. This paper sheds light on what is happening today and what the trajectories for the future are, with particular respect to the implications for supply chain management. Design/methodology/approach The dynamic literature review method called Systematic Literature Network Analysis (SLNA) was adopted. It combines the Systematic Literature Review approach and bibliographic network analyses, and it relies on objective measures and algorithms to perform quantitative literature-based detection of emerging topics. Findings The focus of the literature seems to be on threats that are internal to the extended supply chain rather than on external attacks, such as viruses, traditionally related to information technology (IT). The main arising risk appears to be the intentional or non-intentional leakage of information. Also, papers analyze the implications for information sharing coming from “soft” factors such as trust and collaboration among supply chain partners. Opportunities are also highlighted and include how information sharing can be leveraged to confront disruptions and increase resilience. Research limitations/implications The adopted methodology allows for providing an original perspective on the investigated topic, that is, how information sharing in supply chains and related risks are evolving over time because of the turbulent advances in technology. Practical implications Emergent and highly critical risks related to information sharing are highlighted to support the design of supply chain risks strategies. Also, critical areas to the development of “beyond-the-dyad” initiatives to manage information sharing risks emerge. Opportunities coming from information sharing that are less known and exploited by companies are provided. Originality/value This paper focuses on the supply chain perspective rather than the traditional IT-based view of information sharing. According to this perspective, this paper provides a dynamic representation of the literature on the investigated topic. This is an important contribution to the topic of information sharing in supply chains is continuously evolving and shaping new supply chain models.

Steady Flow Calculations Using Transient Analysis Combined With Network Analysis

2003 ◽  
Author(s):  
Masashi Shimada ◽  
Ryota Kurisu
Keyword(s):  
Network Analysis ◽  
Transient Analysis ◽  
Steady Flows ◽  
Speed Of Convergence ◽  
Wave Speeds ◽  
Network Analyses ◽  
Cpu Time ◽  
Friction Factors ◽  
Optimization Parameter ◽  
Time Marching

This paper proposes a method of solving steady flows in large pipelines with the transient analysis (MOC) combined with the network analysis. The existing methods of accelerating the speed of convergence to steady flows in pipelines, i.e., the time marching approach (TMA) replaces the system dimensions (lengths of pipes, friction factors, wave speeds) by not actual ones and dynamically controls one optimization parameter to reduce the spectral radius. That method will be applied to two pipeline systems having a few thousand of pipes. To accelerate much more the convergence the graph-theoretical information used in the network analyses is implemented. From the discharges computed with TMA the heads at each node are adequately modified using the information of “Tree” of the directed-graph defined for pipelines. Two variations of the method are also proposed. They reduces much the Cpu time to solve steady flows in large pipelines.

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