Quantitative Network Analysis Research Articles

Phosphoproteomics: a valuable tool for uncovering molecular signaling in cancer cells

ABSTRACT Introduction Many pathologies, including cancer, have been associated with aberrant phosphorylation-mediated signaling networks that drive altered cell proliferation, migration, metabolic regulation, and can lead to systemic inflammation. Phosphoproteomics, the large-scale analysis of protein phosphorylation sites, has emerged as a powerful tool to define signaling network regulation and dysregulation in normal and pathological conditions. Areas Covered We provide an overview of methodology for global phosphoproteomics as well as enrichment of specific subsets of the phosphoproteome, including phosphotyrosine and phospho-motif enrichment of kinase substrates. We review quantitative methods, advantages and limitations of different mass spectrometry acquisition formats, and computational approaches to extract biological insight from phosphoproteomics data. Throughout, we discuss various applications and their challenges in implementation. Expert opinion Over the past 20 years the field of phosphoproteomics has advanced to enable deep biological and clinical insight through the quantitative analysis of signaling networks. Future areas of development include Clinical Laboratory Improvement Amendments (CLIA)-approved methods for analysis of clinical samples, continued improvements in sensitivity to enable analysis of small numbers of rare cells and tissue microarrays, and computational methods to integrate data resulting from multiple systems-level quantitative analytical methods.

Dung preference and trophic association of dung beetles (Coleoptera: Scarabaeidae) in the moist forests of the South-western Ghats of the Indian subcontinent

• Quantitative dung beetle-feeding trophic networks from the Western Ghats in India. • Dung-association pattern and dietary specificity at native mammalian species level. • Assemblage of few dung specialists, species with preferences and more generalists. • Low overall specialisation and low resource partitioning. • Boar dung availability: crucial for sustainability rare species. First quantitative dung beetle-feeding trophic network analysis for the Oriental region is carried out by investigating trophic network interaction between dung beetles and mammal dung types in the moist forests of the Western Ghats a global biodiversity hot spot in south-western India. Dung-beetle assemblage associated with the dung of the prominent mammals, such as the macaque, boar, gaur, elephant and deer, showed differences in richness, abundance and composition among different dung types. Most dung beetles were generalists with low resource specificity and community-wide generalist feeding on herbivore and omnivore dung types. Dung beetles in the region displayed high species richness and abundance in boar dung. The high attraction and specificity of dung beetles towards the odoriferous boar dung indicate that the omnivore mammal Sus scrofa has a major role in maintaining the dung beetle community in the forests of the Western Ghats. Network interaction analysis shows that the vast majority in the assemblage are generalist species, and the few specialist species were all with low abundance. Low overall specialisation and low resource partitioning with high species richness is recorded in the assemblage. The assemblage's trophic level preference is reflected in the high dung specificity recorded in the omnivore and herbivore dung types.

Learning spaces in multi-stakeholder initiatives: The German Partnership for Sustainable Textiles as a platform for dialogue and learning?

Learning in collaborative governance is often recognized as an important component for sustainability transformation, but is mainly seen as a side-effect alongside governance outcomes. However, bringing together heterogeneous perspectives is crucial to fully capture the wicked sustainability challenges. We know very little about how learning is facilitated in collaborative governance. This empirical article assesses a multi-stakeholder initiative (MSI) and examines the extent to which it provides spaces for learning. It proposes an analytical framework for investigating social learning spaces along three prerequisites: diversity of member organizations, structure and quality of interactions. A qualitative and quantitative social network analysis (SNA) indicates that the Partnership for Sustainable Textiles (Textiles Partnership) has a large number of heterogeneous members, which holds great potential for learning. However, due to deliberative-democratic structures and the prioritisation of governance outcomes it closes down spaces for learning for the wide diversity of members. The article argues for the design of spaces for learning by emphasising that diversity of perceptions is crucial for a better understanding of the wicked sustainability challenges.

Developing a Topic Network of Published Systems Engineering Research

AbstractThis paper investigates past systems engineering research structure by developing a network of research topics from an extensive corpus of publications. The bibliometric data analysis from the published research articles provides valuable information on past progress and future scientific discipline opportunities. Topic modelling, a form of unsupervised machine‐learning‐based natural language processing, is applied to extract the main topics from the titles and abstracts of a wide range of papers published about systems engineering. The co‐occurrence of research topics in these papers provides the data for generating network diagrams. A visual and quantitative network analysis of these topics revealed several cliques and clusters of research topics in systems engineering. Systems engineering researchers should consider these relationships between these research topics to plan a systems engineering research project.

Stakeholder influence and relationships inform engagement strategies in marine conservation

ABSTRACT Many current marine conservation approaches do not adequately consider the diverse social elements and human aspects necessary to achieve conservation outcomes. The results of conservation research are therefore not always useful for conservation managers to apply in practice. To address this gap, this study combines qualitative methods and quantitative social network analysis to help conservation managers gain in-depth insight into social elements of marine conservation and opportunities that can help achieve conservation outcomes. Specifically, using the North West Shelf Flatback Turtle Conservation Program as a case study, our analysis shows that 1) a stakeholder’s position in the turtle conservation network is not the sole or best indicator of their leadership potential to achieve conservation outcomes, 2) peripheral stakeholders are also important for trust, decision-making, and future success, 3) mixed-methods can identify additional opportunities to maintain and further build trust and influence between diverse stakeholders 4) building relationships to support conservation outcomes is accomplished by leveraging stakeholders’ roles in the conservation program. By identifying who has influence and who needs to be involved in marine conservation to achieve success, this study demonstrates the value of mixed-methods research approaches. Specifically, our findings show how social network approaches can help conservation managers and stakeholders strategically build communication and engagement strategies that can be used to achieve conservation outcomes.

Quantitative analysis of trade networks: data and robustness

A common issue in trade network analysis is missing data, as some countries do not report trade flows. This paper explores what constitutes suitable data, how to deal with missing data, and demonstrates the results using key network measures. All-to-all potential connectivity of trade between countries is considered as a starting point, in contrast to the common approach of analyzing trade networks using only the countries that actually report trade flows. In order to fill the gap between the two approaches, a more complete dataset than just the dataset of trade between reporting countries is reconstructed and the robustness of studying this bigger dataset is examined. The difference between imputed and actual network adjacency matrices is evaluated based on several centrality measures. The results are illustrated using ten commodity groups from the United Nations Database, which demonstrate that under the proposed reconstruction procedure the ranks of the countries do not change significantly as the size of the imputed network becomes bigger or smaller. Further, the degree distributions of networks based on reporting countries and trading partners are the same to within their uncertainties. So, it is robust to study the imputed bigger network that provides richer insights into trade relations, particularly for nonreporting countries.

Collaborative Service Innovation: A Quantitative Analysis of Innovation Networks in a Multisectoral Setting

Partial least squares structural equation modelling has proven very valuable to study the unexplored and complex public service innovation networks (PSINs) in the public sector, from a socio-economic stance. Web have modelled PSINs’ three structural variables—Social, Actors, and Functioning mode—using a sample of original data (n = 233). Our PSINs’ model confirms them as instruments that produce public service innovation—involving technological and nontechnological characteristics. Additionally, we set-up a novel and potentially fruitful methodology to study the intricate formation and impact of complex socio-economical structures that connect innovation and public services. Hence, our research supports a better and extended use of PSINs as a tool for policy and service co-design and co-implementation. And we open a promising line of studies involving multi-actor collaboration in the public sector.

Quantitative Analysis of Tyrosine Phosphorylation from FFPE Tissues Reveals Patient-Specific Signaling Networks.

Human tissue samples commonly preserved as formalin-fixed paraffin-embedded (FFPE) tissues after diagnostic or surgical procedures in the clinic represent an invaluable source of clinical specimens for in-depth characterization of signaling networks to assess therapeutic options. Tyrosine phosphorylation (pTyr) plays a fundamental role in cellular processes and is commonly dysregulated in cancer but has not been studied to date in FFPE samples. In addition, pTyr analysis that may otherwise inform therapeutic interventions for patients has been limited by the requirement for large amounts of frozen tissue. Here we describe a method for highly sensitive, quantitative analysis of pTyr signaling networks, with hundreds of sites quantified from one to two 10-μm sections of FFPE tissue specimens. A combination of optimized magnetic bead-based sample processing, optimized pTyr enrichment strategies, and tandem mass tag multiplexing enabled in-depth coverage of pTyr signaling networks from small amounts of input material. Phosphotyrosine profiles of flash-frozen and FFPE tissues derived from the same tumors suggested that FFPE tissues preserve pTyr signaling characteristics in patient-derived xenografts and archived clinical specimens. pTyr analysis of FFPE tissue sections from breast cancer tumors as well as lung cancer tumors highlighted patient-specific oncogenic driving kinases, indicating potential targeted therapies for each patient. These data suggest the capability for direct translational insight from pTyr analysis of small amounts of FFPE tumor tissue specimens. SIGNIFICANCE: This study reports a highly sensitive method utilizing FFPE tissues to identify dysregulated signaling networks in patient tumors, opening the door for direct translational insights from FFPE tumor tissue banks in hospitals.

Role of hydrogen peroxide in intra-operative wound preparation based on an in vitro fibrin clot degradation model

Three per cent hydrogen peroxide (H2O2) is widely used to irrigate acute and chronic wounds in the surgical setting and clinical experience tells us that it is more effective at removing dried-on blood than normal saline alone. We hypothesise that this is due to the effect of H2O2 on fibrin clot architecture via fibrinolysis. We investigate the mechanisms and discuss the clinical implications using an in vitro model. Coagulation assays with normal saline (NaCl), 1% and 3% concentrations of H2O2 were performed to determine the effect on fibrin clot formation. These effects were confirmed by spectrophotometry. The effects of 1%, 3% and 10% H2O2 on the macroscopic and microscopic features of fibrin clots were assessed at set time intervals and compared to a NaCl control. Quantitative analysis of fibrin networks was undertaken to determine the fibre length, diameter, branch point density and pore size. Fibrin clots immersed in 1%, 3% and 10% H2O2 demonstrated volume losses of 0.09-0.25mm3/min, whereas those immersed in the normal saline gained in volume by 0.02±0.13 mm3/min. Quantitative analysis showed that H2O2 affects the structure of the fibrin clot in a concentration-dependent manner, with the increase in fibre length, diameter and consequently pore sizes. Our results support our hypothesis that the efficacy of H2O2 in cleaning blood from wounds is enhanced by its effects on fibrin clot architecture in a concentration- and time-dependent manner. The observed changes in fibre size and branch point density suggest that H2O2 is acting on the quaternary structure of the fibrin clot, most likely via its effect on cross-linking of the fibrin monomers and may therefore be of benefit for the removal of other fibrin-dependent structures such as wound slough.

A qualitative enhancement to quantitative social network analysis

The literature on social media analytics typically focuses on the social processes within the social media group that is being studied. On the other hand, the subjective views of the social media analysts have received less attention. Here, we demonstrate that the subjectivity of the analyst can have a bearing on both qualitative and quantitative social network analytics. We provide a methodology to analyze the analysts’ subjective frames of reference and their impact on the way they interpret the social group under study and also the social context within their organization that prompted the inquiry in the first place. We offer a methodology for analyzing the types of resources, characteristics, and knowledge held by key influencers in a social media group, and then use the findings to better understand the frames of reference of its members at large. We propose that social media analytics should be carried out by groups of analysts who discuss their individual viewpoints and bring about a “fusion of horizons,” in contrast to the prevalent practice of analysts conducting the analysis alone. We test our methodology with a study of #Blogchat, the largest live chat group on Twitter.

Stakeholder Involvement in Destination Marketing: A Network Analysis of two Destinations in Vietnam

Purpose –This paper aims to identify the stakeholders involved in tourism destination marketing networks. The involvement and collaboration of multiple individuals and organisations is widely recognised in destination marketing to promote a destination. Internal stakeholders of a destination and their collaboration are frequently studied in destination marketing research, but little attention is paid to the involvement of external stakeholders. Design/Methodology/Approach – Quantitative social network analysis is an important approach to understanding stakeholder connections and roles in tourism destinations. In this paper, this analysis was conducted using primary data collected from social network surveys in Da Nang and Hue, two local destinations in central Vietnam. Sixty-nine questionnaires were collected in Da Nang, and 60 questionnaires in Hue. The network data were each analysed separately using UCINET software. Findings – The results show the involvement of different stakeholders in marketing activities in these local destinations. Most of the marketing networks of Da Nang and Hue consist of business units. While the Da Nang marketing network focuses on critical stakeholders and fundamental business firms with strong financial resources and big brands, the Hue marketing network includes more diverse stakeholders and more significant participation of small and medium local firms. Originality of research – This research found the participation of national and international stakeholders located outside the two destinations studied in their marketing network. Their involvement was identified through their collaborative relationships with the DMOs and internal stakeholders of the two destinations to promote the destinations nationally and internationally.

Reimagining the global food regimes for relational spaces

AbstractAs the food regime approach was originally built on the influential world‐system analytical framework, it is worth highlighting human geographers’ efforts to transcend world‐system theory (and the food regime approach) to emphasise how different types of political‐economic processes operate relationally and differentially across regions in the capitalist world. Meanwhile, other geographical perspectives and approaches, such as “critical GIS,” have contributed to the re‐imagination of regions relationally at a range of geographical scales, operating as distinct pathways for commodities over time. Accordingly, drawing on the more‐than‐qualitative relational approach supported by critical GIS and the regional and political geography literature, this paper applies centrality analysis – a quantitative social network analysis – and time‐series analysis to explore and visualise the transformation of the trade networks of high‐value crops in Asia. The paper argues that applying a more‐than‐qualitative relational approach to food regime scholarship can advance our understanding of the emerging regional dynamics in the global food system. Particularly, the findings of this paper show that the rise of China in the global food system has been accompanied by the emergence of Thailand and Vietnam as central actors. Such a multipolar food regime is applicable to address a regional food system, as its regional centrality is defined by clusters of states with more diffuse centrality.

Cover versions as an impact indicator in popular music: A quantitative network analysis.

In contemporary popular music, covering or adapting previous songs signals a tribute or reworking of popular hits, which implies acknowledgement of the original musicians. This connection can be interpreted as a form of musical impact among artists. A network graph with more than 106k artists and 855k cover versions extracted from the web site SecondHandSongs was created. The objective is to explore the shape of this network, identify the most relevant artists according to different impact measurements and to visualize connections between music genres. This analysis is done from a longitudinal perspective with the aim of understanding how cover versions can inform us about the history of the contemporary popular music. Results show that the number of covers by artist is skewed distributed, diminishing gradually since the 1950s. Different network metrics have allowed to identify the most covered (weighted indegree), the most influential (PageRank) and the most crossover artists (weighted betweenness centrality). The network graph also shows that genre affinity is the main criterion for covering songs between artists, language being the second. Remakes from other genres reflect that Jazz and Pop/Rock are the most influential because they emerge stronger and form the core of their respective sub-networks. Cover songs describe two cycles. In a first phase from 1900s to 1950s, dominated by Jazz and Vocal artists, the covers are more frequent and associated with the notion of reworking (e.g. jazz standards); in a second stage, since the 1950s, when the Pop/Rock emerges, cover songs are less common and seen as tribute.

Affording Twitter in Emergency Situations

This study focuses on Twitter affordances and sense-making outcomes during a single emergency situation. By using an interpretive affordance lens, this study aims to assess rumors as influencers of sense-making during the 2017 Manchester terrorist attack. The authors combined a quantitative network analysis with a qualitative content analysis to assess the role of rumors during the emergency management after the attack. This study provides argumentative grounds for the notion of sense-making as a consequence of affording social media and builds on prior research to place sense-making as a cognitive process within the affordance concept. The authors emphasize new potentials to prevent or control rumors on social media for practitioners and contribute insights to rumor research. Namely, the authors contribute a novel perspective of rumors and their role during emergency management on social media.

Selection of optimal intensity measures for fragility assessment of circular tunnels in soft soil deposits

This study aims to identify optimal intensity measures (IMs) for use in probabilistic seismic demand models (PSDMs) for circular tunnels in soft soil deposits. To this end, we performed an extended numerical parametric study involving two-dimensional time history analyses of selected soil-tunnel configurations to evaluate the response of the selected tunnels under transverse seismic shaking. A series of 18 IMs were selected and tested, corresponding to free field conditions. The selected IMs were tested on several metrics, such as correlation, efficiency, practicality, and proficiency, based on an extended number of regression analyses between the IMs and the damage measure (DM), for the studied tunnels. DM is defined as the ratio of the actual bending moment (M) to the capacitive bending moment (MRd) of the tunnel lining. The results indicate that the peak ground acceleration (PGA) at the ground surface can be considered as the optimal IM for the shallow tunnels, whereas the peak ground velocity (PGV) can be considered as the optimal IM for both the moderately deep and deep tunnels. Finally, various fragility curves were constructed for the studied circular tunnels under the context of PSDMs. The findings of this study can serve as a reference for the seismic fragility analysis of circular tunnels in soft soil deposits, toward more reliable quantitative risk analysis (QRA), improved resilience, and adaptability of transportation networks.

Quantitative analysis of character networks in Polish 19th- and 20th-century novels

Abstract This article presents a study on induction and quantitative analysis of character networks inferred from Polish novels. The corpus compiled for this study includes both 19th- and 20th-century literary works in the form of electronic editions of novels and OCR-derived texts obtained from publicly available sources. The natural language processing pipeline developed for the purpose of inducing character networks from novels is described. The structural properties of the inferred character networks are investigated, and significant differences between the properties of networks induced from 19th- to 20th-century novels are demonstrated.

Know Your Stars Before They Fall Apart: A Social Network Analysis of Telecom Industry to Foster Employee Retention Using Data Mining Technique

Social network analysis (SNA) has emerged as a significant paradigm for research in data mining community for measuring and analyzing human dynamic network structure. At organizational level, SNA can enhance our understanding of work place social interactions and unveil the hidden stars embedded in informal networks by investigating nodes and edges of complex networks. For this study, we aim to formulate a network centrality based quantitative method to identify the High potential employees (HiPos) and Influencers of telecom sector and explore the relationship between degree centrality of these star employees and their turnover intention by modeling the dynamics of their workplace social ties and predictive data mining technique. We investigated the multiplex work and advice network in two leading telecom operators of Pakistan i.e. Ufone and Zong. For the statistical analysis we conducted a quantitative and visual network analysis in UCINET along with correlation and regression. Our results showed a negative correlation between HiPos out-degree centrality and turnover intention and a positive correlation between influencer’s in-degree centrality and turnover. Whereas perceived investment in employee development (PIED) was found to mediate the relationship between in-degree centrality of influencer and turnover intention. The correlation results were then verified in regression model. These findings will guide the telecom operators in designing an optimal structure for business intelligence by providing critical insights of their star employees and help them to investigate the influence of central nodes on dynamical processes of its heterogeneous networks and thus enhance employee retention before a star falls out.

Academic performance and social position analysis of the networks of engineering students

Is there always a statistically positive correlation between academic achievement and social capital? What is the role of individual characteristics in this correlation? The present study combines quantitative Social Network Analysis (SNA) with qualitative interviews to examine these questions among students in a 'bachelor's degree program. The research operationalized students' social capital as friendship ties and social network centralities and correlated these parameters with their academic performance. The study uses and elucidates the relationship between academic performance and social position. It uses this data and qualitative data obtained through in-depth interviews to characterize students who are both academic and social high-performers.

The effect of baking time and temperature on gluten protein structure and celiac peptide digestibility

Previous work has shown that baking induces structural changes within the gluten macropolymer (GMP) that reduce gluten protein digestibility. The precise nature of these structural changes within dough/bread, and how they alter the in vitro release profile of immunogenic gluten peptides that activate celiac disease is unknown. This work examined the effect of dough baking temperature and duration on the GMP’s structure and the release profile of immunogenic gluten peptides. Dough was baked at either 150 °C or 230 °C for 25, 35 or 45 min. The structure of the GMP within the resulting loaves was defined and compared using confocal microscopy, quantitative protein network analysis, gliadin protein extractability (HPLC) and determination of the free thiol content. Both bread and dough were digested in vitro (INFOGEST) and the release profile of six immunogenic gluten peptides (including the immunodominant 33mer) defined using quantitative mass spectrometry. Higher baking temperatures and longer durations increased the degree of intermolecular disulfide bonds between the sulfur-rich gliadins and GMP backbone. The thermal load did not alter the GMP macrostructure, but significant differences between bread and dough were observed. Baking altered the concentration and release profile of the immunogenic gluten peptides throughout in vitro digestion causing the digestion of immunogenic gluten peptides differed between raw and heat-treated bread.

Mycorrhizal symbiosis modulates the rhizosphere microbiota to promote rhizobia–legume symbiosis

Plants establish symbioses with mutualistic fungi, such as arbuscular mycorrhizal (AM) fungi, and bacteria, such as rhizobia, to exchange key nutrients and thrive. Plants and symbionts have coevolved and represent vital components of terrestrial ecosystems. Plants employ an ancestral AM signaling pathway to establish intracellular symbioses, including the legume–rhizobia symbiosis, in their roots. Nevertheless, the relationship between the AM and rhizobial symbioses in native soil is poorly understood. Here, we examined how these distinct symbioses affect root-associated bacterial communities in Medicago truncatula by performing quantitative microbiota profiling (QMP) of 16S rRNA genes. We found that M. truncatula mutants that cannot establish AM or rhizobia symbiosis have an altered microbial load (quantitative abundance) in the rhizosphere and roots, and in particular that AM symbiosis is required to assemble a normal quantitative root-associated microbiota in native soil. Moreover, quantitative microbial co-abundance network analyses revealed that AM symbiosis affects Rhizobiales hubs among plant microbiota and benefits the plant holobiont. Through QMP of rhizobial rpoB and AM fungal SSU rRNA genes, we revealed a new layer of interaction whereby AM symbiosis promotes rhizobia accumulation in the rhizosphere of M. truncatula. We further showed that AM symbiosis-conditioned microbial communities within the M. truncatula rhizosphere could promote nodulation in different legume plants in native soil. Given that the AM and rhizobial symbioses are critical for crop growth, our findings might inform strategies to improve agricultural management. Moreover, our work sheds light on the co-evolution of these intracellular symbioses during plant adaptation to native soil conditions.