A Machine Learning Approach for Predicting Essentiality of Metabolic Genes

The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires large and costly growth assays of knockout strains. Here we describe a strategy to predict the essentiality of metabolic genes using binary classification algorithms. The approach combines elements from genome-scale metabolic models, directed graphs, and machine learning into a predictive model that can be trained on small knockout data. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli and various machine learning algorithms for binary classification.

Feats: A database of semantic features for early produced noun concepts

Semantic feature production norms have several desirable characteristics that have supported models of representation and processing in adults. However, several key challenges have limited the use of semantic feature norms in studies of early language acquisition. First, existing norms provide uneven and inconsistent coverage of early-acquired concepts that are typically produced and assessed in children under the age of three, which is a time of tremendous growth of early vocabulary skills. Second, it is difficult to assess the degree to which young children may be familiar with normed features derived from these adult-generated datasets. Third, it has been difficult to adopt standard methods to generate semantic network models of early noun learning. Here, we introduce Feats-a tool that was designed to make headway on these challenges by providing a database, the Language Learning and Meaning Acquisition (LLaMA) lab Noun Norms that extends a widely used set of feature norms McRae et al. Behavior Research Methods 37, 547-559, (2005) to include full coverage of noun concepts on a commonly used early vocabulary assessment. Feats includes several tools to facilitate exploration of features comprising early-acquired nouns, assess the developmental appropriateness of individual features using toddler-accessibility norms, and extract semantic network statistics for individual vocabulary profiles. We provide a tutorial overview of Feats. We additionally validate our approach by presenting an analysis of an overlapping set of concepts collected across prior and new data collection methods. Furthermore, using network graph analyses, we show that the extended set of norms provides novel, reliable results given their enhanced coverage.

Evaluation of the failure effects of a screwing station using a new approached FMEA

Recognizing the importance of risk assessment and the large-scale industrial spread of network research, we developed a new approach to risk assessment.•The risk assessment takes into account the chains of impact between each level and the frequency of effects and their causes.•In contrast to the traditional FMEA methodology, we evaluate the frequency of occurrence and detectability not only at the level of causes but also at the level of effects.•All this is complemented by a toolkit of network research methodology. The new methodology is validated through a real industry example, which is a risk assessment of a screwdriver station.

Local but not global graph theoretic measures of semantic networks generalize across tasks

"Dogs" are connected to "cats" in our minds, and "backyard" to "outdoors." Does the structure of this semantic knowledge differ across people? Network-based approaches are a popular representational scheme for thinking about how relations between different concepts are organized. Recent research uses graph theoretic analyses to examine individual differences in semantic networks for simple concepts and how they relate to other higher-level cognitive processes, such as creativity. However, it remains ambiguous whether individual differences captured via network analyses reflect true differences in measures of the structure of semantic knowledge, or differences in how people strategically approach semantic relatedness tasks. To test this, we examine the reliability of local and global metrics of semantic networks for simple concepts across different semantic relatedness tasks. In four experiments, we find that both weighted and unweighted graph theoretic representations reliably capture individual differences in local measures of semantic networks (e.g., how related pot is to pan versus lion). In contrast, we find that metrics of global structural properties of semantic networks, such as the average clustering coefficient and shortest path length, are less robust across tasks and may not provide reliable individual difference measures of how people represent simple concepts. We discuss the implications of these results and offer recommendations for researchers who seek to apply graph theoretic analyses in the study of individual differences in semantic memory.

Early development of the functional brain network in newborns

During the prenatal period and the first postnatal years, the human brain undergoes rapid growth, which establishes a preliminary infrastructure for the subsequent development of cognition and behavior. To understand the underlying processes of brain functioning and identify potential sources of developmental disorders, it is essential to uncover the developmental rules that govern this critical period. In this study, graph theory modeling and network science analysis were employed to investigate the impact of age, gender, weight, and typical and atypical development on brain development. Local and global topologies of functional connectomes obtained from rs-fMRI data were collected from 421 neonates aged between 31 and 45 postmenstrual weeks who were in natural sleep without any sedation. The results showed that global efficiency, local efficiency, clustering coefficient, and small-worldness increased with age, while modularity and characteristic path length decreased with age. The normalized rich-club coefficient displayed a U-shaped pattern during development. The study also examined the global and local impacts of gender, weight, and group differences between typical and atypical cases. The findings presented some new insights into the maturation of functional brain networks and their relationship with cognitive development and neurodevelopmental disorders.

Co-evolving dynamics and topology in a coupled oscillator model of resting brain function

Dynamic models of ongoing BOLD fMRI brain dynamics and models of communication strategies have been two important approaches to understanding how brain network structure constrains function. However, dynamic models have yet to widely incorporate one of the most important insights from communication models: the brain may not use all of its connections in the same way or at the same time. Here we present a variation of a phase delayed Kuramoto coupled oscillator model that dynamically limits communication between nodes on each time step. An active subgraph of the empirically derived anatomical brain network is chosen in accordance with the local dynamic state on every time step, thus coupling dynamics and network structure in a novel way. We analyze this model with respect to its fit to empirical time-averaged functional connectivity, finding that, with the addition of only one parameter, it significantly outperforms standard Kuramoto models with phase delays. We also perform analyses on the novel time series of active edges it produces, demonstrating a slowly evolving topology moving through intermittent episodes of integration and segregation. We hope to demonstrate that the exploration of novel modeling mechanisms and the investigation of dynamics of networks in addition to dynamics on networks may advance our understanding of the relationship between brain structure and function.

A network approach to understand co-occurrence and relative importance of different reasons for suicide: a territory-wide study using 2002-2019 Hong Kong Coroner's Court reports

Background

Suicide is a complex and multifaceted issue, and suicidal behaviors are often driven by multiple, interacting factors. It has been challenging to identify reasons for suicide using existing scientific methodologies. This study aims to identify critical reasons for suicide and suicidal behaviors through the application of novel network science methods.

Methods

Based on cases investigated by the Hong Kong Coroner's Court from 2002 to 2019, we modelled identified reasons for 13,001 suicide cases as a co-occurrence network, and calculated each reason's eigencentrality to determine their respective relative importance. We then analyzed the temporal and demographic changes in the structure and eigencentrality of the network. We further conducted simulation studies based on the United Nations population projection to assess potential burden of different reasons for suicide on the population in the coming years.

Findings

School-related issues had the highest eigencentrality (eigencentrality = 0.49) for individuals younger than 20 years of age. Financial issues were crucial for adults aged 20-59 years, but their importance differed between males (eigencentrality = 0.51) and females (eigencentrality = 0.14). Physical illness (eigencentrality = 0.80) was the core concern for adults over 60 years. Across the Hong Kong population, the reasons for suicide appear to have shifted from financial issues in the early 2000s (eigencentrality = 0.46) to issues related to physical illnesses since 2011 (eigencentrality = 0.58). Simulation findings indicate that, by 2050, most suicides in Hong Kong will be due to physical illness-related issues (eigencentrality = 0.69) due to the rapidly aging population.

Interpretation

There have been important sex and age differences over time, in reasons for suicide. Given the projected increasing age of the Hong Kong population over the next decades, older adults with physical illnesses appear to be the highest contributors to suicide cases in the overall population. This novel network analysis approach provides important data-driven information upon which to base effective proactive public health suicide prevention strategies and interventions.

Funding

Hong Kong Jockey Club Charities Trust, Collaborative Research Fund (C7151-20G), and General Research Fund (17606521).

Discovering new pathways toward integration between health and sustainable development goals with natural language processing and network science

BACKGROUND

Research on health and sustainable development is growing at a pace such that conventional literature review methods appear increasingly unable to synthesize all relevant evidence. This paper employs a novel combination of natural language processing (NLP) and network science techniques to address this problem and to answer two questions: (1) how is health thematically interconnected with the Sustainable Development Goals (SDGs) in global science? (2) What specific themes have emerged in research at the intersection between SDG 3 ("Good health and well-being") and other sustainability goals?

METHODS

After a descriptive analysis of the integration between SDGs in twenty years of global science (2001-2020) as indexed by dimensions.ai, we analyze abstracts of articles that are simultaneously relevant to SDG 3 and at least one other SDG (N = 27,928). We use the top2vec algorithm to discover topics in this corpus and measure semantic closeness between these topics. We then use network science methods to describe the network of substantive relationships between the topics and identify 'zipper themes', actionable domains of research and policy to co-advance health and other sustainability goals simultaneously.

RESULTS

We observe a clear increase in scientific research integrating SDG 3 and other SDGs since 2001, both in absolute and relative terms, especially on topics relevant to interconnections between health and SDGs 2 ("Zero hunger"), 4 ("Quality education"), and 11 ("Sustainable cities and communities"). We distill a network of 197 topics from literature on health and sustainable development, with 19 distinct network communities - areas of growing integration with potential to further bridge health and sustainability science and policy. Literature focused explicitly on the SDGs is highly central in this network, while topical overlaps between SDG 3 and the environmental SDGs (12-15) are under-developed.

CONCLUSION

Our analysis demonstrates the feasibility and promise of NLP and network science for synthesizing large amounts of health-related scientific literature and for suggesting novel research and policy domains to co-advance multiple SDGs. Many of the 'zipper themes' identified by our method resonate with the One Health perspective that human, animal, and plant health are closely interdependent. This and similar perspectives will help meet the challenge of 'rewiring' sustainability research to co-advance goals in health and sustainability.

Model templates: transdisciplinary application and entanglement

The omnipresence of the same basic equations, function forms, algorithms, and quantitative methods is one of the most spectacular characteristics of contemporary modeling practice. Recently, the emergence of the discussion of templates and template transfer has addressed this striking cross-disciplinary reach of certain mathematical forms and computational algorithms. In this paper, we develop a notion of a model template, consisting of its mathematical structure, ontology, prototypical properties and behaviors, focal conceptualizations, and the paradigmatic questions it addresses. We apply this notion to three widely disseminated and powerful model templates: the Sherrington-Kirkpatrick model of spin glasses, scale-free networks, and the Kuramoto model of synchronization. We argue that what appears to be an interdisciplinary model transfer between different domains turns out, from a broader perspective, to be the application of transdisciplinary model templates across a multitude of domains. We also point out a further feature of template-based modeling that so far has not been discussed: template entanglement. Such entanglement enhances and makes manifest the conceptual side of model templates.

Two pathways in vocabulary development: Large-scale differences in noun and verb semantic structure

In adults, nouns and verbs have varied and multilevel semantic interrelationships. In children, evidence suggests that nouns and verbs also have semantic interrelationships, though the timing of the emergence of these relationships and their precise impact on later noun and verb learning are not clear. In this work, we ask whether noun and verb semantic knowledge in 16-30-month-old children tend to be semantically isolated from one another or semantically interacting from the onset of vocabulary development. Early word learning patterns were quantified using network science. We measured the semantic network structure for nouns and verbs in 3,804 16-30-month-old children at several levels of granularity using a large, open dataset of vocabulary checklist data. In a cross-sectional approach in Experiment 1, early nouns and verbs exhibited stronger network relationships with other nouns and verbs than expected across multiple network levels. Using a longitudinal approach in Experiment 2, we examined patterns of normative vocabulary development over time. Initial noun and verb learning was supported by strong semantic connections to other nouns, whereas later-learned words exhibited strong connections to verbs. Overall, these two experiments suggest that nouns and verbs demonstrate early semantic interactions and that these interactions impact later word learning. Early verb and noun learning is affected by the emergence of noun and verb semantic networks during early lexical development.

Structure and function in artificial, zebrafish and human neural networks

Network science provides a set of tools for the characterization of the structure and functional behavior of complex systems. Yet a major problem is to quantify how the structural domain is related to the dynamical one. In other words, how the diversity of dynamical states of a system can be predicted from the static network structure? Or the reverse problem: starting from a set of signals derived from experimental recordings, how can one discover the network connections or the causal relations behind the observed dynamics? Despite the advances achieved over the last two decades, many challenges remain concerning the study of the structure-dynamics interplay of complex systems. In neuroscience, progress is typically constrained by the low spatio-temporal resolution of experiments and by the lack of a universal inferring framework for empirical systems. To address these issues, applications of network science and artificial intelligence to neural data have been rapidly growing. In this article, we review important recent applications of methods from those fields to the study of the interplay between structure and functional dynamics of human and zebrafish brain. We cover the selection of topological features for the characterization of brain networks, inference of functional connections, dynamical modeling, and close with applications to both the human and zebrafish brain. This review is intended to neuroscientists who want to become acquainted with techniques from network science, as well as to researchers from the latter field who are interested in exploring novel application scenarios in neuroscience.

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

BACKGROUND

Understanding 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.

RESULTS

Our 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.

CONCLUSIONS

The 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.

Representing melodic relationships using network science

Music is a complex system consisting of many dimensions and hierarchically organized information-the organization of which, to date, we do not fully understand. Network science provides a powerful approach to representing such complex systems, from the social networks of people to modelling the underlying network structures of different cognitive mechanisms. In the present research, we explored whether network science methodology can be extended to model the melodic patterns underlying expert improvised music. Using a large corpus of transcribed improvisations, we constructed a network model in which 5-pitch sequences were linked depending on consecutive occurrences, constituting 116,403 nodes (sequences) and 157,429 edges connecting them. We then investigated whether mathematical graph modelling relates to musical characteristics in real-world listening situations via a behavioral experiment paralleling those used to examine language. We found that as melodic distance within the network increased, participants judged melodic sequences as less related. Moreover, the relationship between distance and reaction time (RT) judgements was quadratic: participants slowed in RT up to distance four, then accelerated; a parallel finding to research in language networks. This study offers insights into the hidden network structure of improvised tonal music and suggests that humans are sensitive to the property of melodic distance in this network. More generally, our work demonstrates the similarity between music and language as complex systems, and how network science methods can be used to quantify different aspects of its complexity.

Real-time updating of dynamic social networks for COVID-19 vaccination strategies

Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamic systems where contact networks are highly time-variant. Furthermore, the numerous mutations of SARS-CoV-2 have a significant impact on the infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamic networks collected in a high school, followed by sequential multi-layer networks generated relying on the Barabasi-Albert model emulating large-scale social networks with several communities.

Dauer fate in a Caenorhabditis elegans Boolean network model

Cellular fates are determined by genes interacting across large, complex biological networks. A critical question is how to identify causal relationships spanning distinct signaling pathways and underlying organismal phenotypes. Here, we address this question by constructing a Boolean model of a well-studied developmental network and analyzing information flows through the system. Depending on environmental signals Caenorhabditis elegans develop normally to sexual maturity or enter a reproductively delayed, developmentally quiescent 'dauer' state, progressing to maturity when the environment changes. The developmental network that starts with environmental signal and ends in the dauer/no dauer fate involves genes across 4 signaling pathways including cyclic GMP, Insulin/IGF-1, TGF-β and steroid hormone synthesis. We identified three stable motifs leading to normal development, each composed of genes interacting across the Insulin/IGF-1, TGF-β and steroid hormone synthesis pathways. Three genes known to influence dauer fate, daf-2, daf-7 and hsf-1, acted as driver nodes in the system. Using causal logic analysis, we identified a five gene cyclic subgraph integrating the information flow from environmental signal to dauer fate. Perturbation analysis showed that a multifactorial insulin profile determined the stable motifs the system entered and interacted with daf-12 as the switchpoint driving the dauer/no dauer fate. Our results show that complex organismal systems can be distilled into abstract representations that permit full characterization of the causal relationships driving developmental fates. Analyzing organismal systems from this perspective of logic and function has important implications for studies examining the evolution and conservation of signaling pathways.

Modular structures and the delivery of inpatient care in hospitals: a Network Science perspective on healthcare function and dysfunction

BACKGROUND

Reinforced by the COVID-19 pandemic, the capacity of health systems to cope with increasing healthcare demands has been an abiding concern of both governments and the public. Health systems are made up from non-identical human and physical components interacting in diverse ways in varying locations. It is challenging to represent the function and dysfunction of such systems in a scientific manner. We describe a Network Science approach to that dilemma. General hospitals with large emergency caseloads are the resource intensive components of health systems. We propose that the care-delivery services in such entities are modular, and that their structure and function can be usefully analysed by contemporary Network Science. We explore that possibility in a study of Australian hospitals during 2019 and 2020.

METHODS

We accessed monthly snapshots of whole of hospital administrative patient level data in two general hospitals during 2019 and 2020. We represented the organisations inpatient services as network graphs and explored their graph structural characteristics using the Louvain algorithm and other methods. We related graph topological features to aspects of observable function and dysfunction in the delivery of care.

RESULTS

We constructed a series of whole of institution bipartite hospital graphs with clinical unit and labelled wards as nodes, and patients treated by units in particular wards as edges. Examples of the graphs are provided. Algorithmic identification of community structures confirmed the modular structure of the graphs. Their functional implications were readily identified by domain experts. Topological graph features could be related to functional and dysfunctional issues such as COVID-19 related service changes and levels of hospital congestion.

DISCUSSION AND CONCLUSIONS

Contemporary Network Science is one of the fastest growing areas of current scientific and technical advance. Network Science confirms the modular nature of healthcare service structures. It holds considerable promise for understanding function and dysfunction in healthcare systems, and for reconceptualising issues such as hospital capacity in new and interesting ways.

Social Network Analysis as a Cybernetic Modelling Facility for Participatory Design in Technology-Supported College Curricula

Despite iterative learning design being increasingly implemented, such approaches are often delineated by well-defined periods of design/implementation. However, second-order cybernetics, which suggests a participatory approach to learning design, involves responsively adapting learning environments to meet students' needs, treating them as agentic participants in the classroom. In our mixed methods study, we investigate whether such a process can facilitate egalitarian participation and collaborative interactions in a technology-assisted classroom. We use the example of a graduate psychology class of 17 students and suggest that adaptation of live-chat activities by a participant observer on the Reddit social media platform that supplemented the in-person lecture dynamically, using a network analysis and qualitative ethnography as a modelling facility mimicked the ongoing feedback loops of social media platforms, enabling students to use social media with a critical eye, and engage in productive collaboration. Our quantitative results present network graphs for weekly eigen centrality to understand the egalitarian nature of the network, and transitivity to understand the likelihood for collaboration between more than two agents. Our qualitative results elaborate selected Reddit posts, and weekly field notes to explain how redesigning the chat weekly helped augment lecture-based discussion with the instructor and critique of student presentations, spurring egalitarian participation through a space-place dialectic. Students also provided end-semester feedback that was analyzed using inductive coding, to design future courseware.

Obtaining interactions among science, technology, and research policy for developing an innovation strategy: A case study of supercapacitors

Comprehensive observations of science, technology, and research policy transactions are important for developing an innovation strategy. We propose a new method that combines the academic landscape and matrix analysis to understand the relationships among activities of three aspects of the technological landscape: science, technology, and research policy. First, we divided academic research into 28 knowledge domains by clustering a citation network of scientific papers. Next, we developed a new matrix classifying them into three groups: “mature technology,” “intermediate technology,” and “emerging technology.” The results showed that research domains in “emerging technology” showed a high rate of patent increase, indicating that they were commercializing rapidly. Finally, we identified the group that each country focused on, and this result reflected the countries' research policies. China and Singapore showed high rates, whereas Japan, France, and Germany had low values. This result reflects countries’ research policies and implies that specialty research areas differed by country. As above, our research result implies that academia, industry, and government have paid attention to knowledge domains in “emerging technology” and these are important for creating innovation. A supercapacitor, also known as an electric double layer capacitor or ultracapacitor, was selected as an example in our method. This research could help academic researchers, industrial companies, and policymakers in developing innovation strategies.

Stroke recovery phenotyping through network trajectory approaches and graph neural networks

Stroke is a leading cause of neurological injury characterized by impairments in multiple neurological domains including cognition, language, sensory and motor functions. Clinical recovery in these domains is tracked using a wide range of measures that may be continuous, ordinal, interval or categorical in nature, which can present challenges for multivariate regression approaches. This has hindered stroke researchers’ ability to achieve an integrated picture of the complex time-evolving interactions among symptoms. Here, we use tools from network science and machine learning that are particularly well-suited to extracting underlying patterns in such data, and may assist in prediction of recovery patterns. To demonstrate the utility of this approach, we analyzed data from the NINDS tPA trial using the Trajectory Profile Clustering (TPC) method to identify distinct stroke recovery patterns for 11 different neurological domains at 5 discrete time points. Our analysis identified 3 distinct stroke trajectory profiles that align with clinically relevant stroke syndromes, characterized both by distinct clusters of symptoms, as well as differing degrees of symptom severity. We then validated our approach using graph neural networks to determine how well our model performed predictively for stratifying patients into these trajectory profiles at early vs. later time points post-stroke. We demonstrate that trajectory profile clustering is an effective method for identifying clinically relevant recovery subtypes in multidimensional longitudinal datasets, and for early prediction of symptom progression subtypes in individual patients. This paper is the first work introducing network trajectory approaches for stroke recovery phenotyping, and is aimed at enhancing the translation of such novel computational approaches for practical clinical application.

Introduction to Genomic Network Reconstruction for Cancer Research

High-throughput genomic technologies have revolutionized the study of cancer. Current research in oncology is now limited more for the capacity of analyzing and interpreting data, rather than the availability of data itself. Integrative approaches to obtain functional information from data are at the core of the disciplines gathered under the systems biology banner. In this context, network models have been used to study cancer, from the identification of key molecules involved in the disease to the discovery of functional alterations associated with specific manifestations of the disease.In this chapter, we describe the state of the art of network reconstruction from genomic data, with an emphasis in gene expression experiments. We explore the strengths and limitations of correlation, Bayesian, and information theoretic approaches to network reconstruction. We present tools that leverage the flexibility of network science to gain a deeper understanding of cancer biology.

System-level consequences of synergies and trade-offs between SDGs: quantitative analysis of interlinkage networks at country level

The Sustainable Development Goals (SDGs) present a complex system of 17 goals and 169 individual targets whose interactions can be described in terms of co-benefits and trade-offs between policy actions. We analyse in detail target-by-target interlinkage networks established by the Institute for Global Environmental Strategies (IGES) SDG Interlinkages Tool. We discuss two quantitative measures of network structure; the leading eigenvector of the interlinkage networks ('eigencentrality') and a notion of hierarchy within the network motivated by the concept of trophic levels for species in food webs. We use three interlinkage matrices generated by IGES: the framework matrix which provides a generic network model of the interlinkages at the target level, and two country-specific matrices for Bangladesh and Indonesia that combine SDG indicator data with the generic framework matrix. Our results echo, and are confirmed by, similar work at the level of whole SDGs that has shown that SDGs 1-3 (ending poverty, and providing food security and healthcare) are much more likely to be achieved than the environmentally- related SDGs 13-15 concerned with climate action, life on land and life below water. Our results here provide a refinement in terms of specific targets within each of these SDGs. We find that not all targets within SDGs 1-3 are equally well-supported, and not all targets within SDGs 13-15 are equally at risk of not being achieved. Finally, we point to the recurring issue of data gaps that hinders our quantitative analysis, in particular for SDGs 5 (gender equality) and 13 (climate action) where the huge gaps in indicator data that mean the true nature of the interlinkages and importance of these two SDGs are not fully recognised.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11625-022-01109-y.

Editors' Introduction to Networks of the Mind: How Can Network Science Elucidate Our Understanding of Cognition?

Thinking is complex. Over the years, several types of methods and paradigms have developed across the psychological, cognitive, and neural sciences to study such complexity. A rapidly growing multidisciplinary quantitative field of network science offers quantitative methods to represent complex systems as networks, or graphs, and study the network properties of these systems. While the application of network science to study the brain has greatly advanced our understanding of the brains structure and function, the application of these tools to study cognition has been done to a much lesser account. This topic is a collection of papers that discuss the fruitfulness of applying network science to study cognition across a wide scope of research areas from generalist accounts of memory and encoding, to individual differences, to communities, and finally to cultural and individual change.

The Value of Statistical Learning to Cognitive Network Science

To study the human mind is to consider the nature of associations-how are they learned, what are their constituent parts, and how can they be severed or adjusted? The manipulation of associations stands as a pillar of statistical learning (SL) research, which strongly suggests that processes as diverse as word segmentation, learning of grammatical patterns, and event perception can be explained by the learner's sensitivity to simple temporal dependencies (among other regularities). Used to determine the edges of a network, associations are similarly crucial to consider when quantifying the graph-theoretical properties of various cognitive systems. With this point of convergence in mind, the present work reaffirms the unique value of network science in illuminating the broad-level architectures of complex cognitive systems. However, I also describe how insights from the SL literature, coupled with insights from psycholinguistics more broadly, offer a strong theoretical backbone upon which we can develop and study networks that reflect, as closely as possible, the psychological realities of learning.

Identification of sampling points for the detection of SARS-CoV-2 in the sewage system

A suitable tool for monitoring the spread of SARS-CoV-2 is to identify potential sampling points in the wastewater collection system that can be used to monitor the distribution of COVID-19 disease affected clusters within a city. The applicability of the developed methodology is presented through the description of the 72,837 population equivalent wastewater collection system of the city of Nagykanizsa, Hungary and the results of the analytical and epidemiological measurements of the wastewater samples. The wastewater sampling was conducted during the 3rd wave of the COVID-19 epidemic. It was found that the overlap between the road system and the wastewater network is high, it is 82 %. It was showed that the proposed methodological approach, using the tools of network science, determines confidently the zones of the wastewater collection system and provides the ideal monitoring points in order to provide the best sampling resolution in urban areas. The strength of the presented approach is that it estimates the network based on publicly available information. It was concluded that the number of zones or sampling points can be chosen based on relevant epidemiological intervention and mitigation strategies. The algorithm allows for continuous effective monitoring of the population infected by SARS-CoV-2 in small-sized cities.

Scalable and distributed strategies for socially distanced human mobility

COVID-19 is a global health crisis that has caused ripples in every aspect of human life. Amid widespread vaccinations testing, manufacture and distribution efforts, nations still rely on human mobility restrictions to mitigate infection and death tolls. New waves of infection in many nations, indecisiveness on the efficacy of existing vaccinations, and emerging strains of the virus call for intelligent mobility policies that utilize contact pattern and epidemiological data to check contagion. Our earlier work leveraged network science principles to design social distancing optimization approaches that show promise in slowing infection spread however, they prove to be computationally prohibitive and require complete knowledge of the social network. In this work, we present scalable and distributed versions of the optimization approaches based on Markov Chain Monte Carlo Gibbs sampling and grid-based spatial parallelization that tackle both the challenges faced by the optimization strategies. We perform extensive simulation experiments to show the ability of the proposed strategies to meet necessary network science measures and yield performance comparable to the optimal counterpart, while exhibiting significant speed-up. We study the scalability of the proposed strategies as well as their performance in realistic scenarios when a fraction of the population temporarily flouts the location recommendations.

Endocrine disrupting chemicals and COVID-19 relationships: A computational systems biology approach

BACKGROUND

Patients at high risk of severe forms of COVID-19 frequently suffer from chronic diseases, but other risk factors may also play a role. Environmental stressors, such as endocrine disrupting chemicals (EDCs), can contribute to certain chronic diseases and might aggravate the course of COVID-19.

OBJECTIVES

To explore putative links between EDCs and COVID-19 severity, an integrative systems biology approach was constructed and applied.

METHODS

As a first step, relevant data sets were compiled from major data sources. Biological associations of major EDCs to proteins were extracted from the CompTox database. Associations between proteins and diseases known as important COVID-19 comorbidities were obtained from the GeneCards and DisGeNET databases. Based on these data, we developed a tripartite network (EDCs-proteins-diseases) and used it to identify proteins overlapping between the EDCs and the diseases. Signaling pathways for common proteins were then investigated by over-representation analysis.

RESULTS

We found several statistically significant pathways that may be dysregulated by EDCs and that may also be involved in COVID-19 severity. The Th17 and the AGE/RAGE signaling pathways were particularly promising.

CONCLUSIONS

Pathways were identified as possible targets of EDCs and as contributors to COVID-19 severity, thereby highlighting possible links between exposure to environmental chemicals and disease development. This study also documents the application of computational systems biology methods as a relevant approach to increase the understanding of molecular mechanisms linking EDCs and human diseases, thereby contributing to toxicology prediction.

COVID-19 and Networks

Ongoing COVID-19 pandemic poses many challenges to the research of artificial intelligence. Epidemics are important in network science for modeling disease spread over networks of contacts between individuals. To prevent disease spread, it is desirable to introduce prioritized isolation of the individuals contacting many and unspecified others, or connecting different groups. Finding such influential individuals in social networks, and simulating the speed and extent of the disease spread are what we need for combating COVID-19. This article focuses on the following topics, and discusses some of the traditional and emerging research attempts: (1) topics related to epidemics in network science, such as epidemic modeling, influence maximization and temporal networks, (2) recent research of network science for COVID-19 and (3) datasets and resources for COVID-19 research.

Multilayer networks: An untapped tool for understanding bilingual neurocognition

Cross-linguistic similarity is a term so broad and multi-faceted that it is not easily defined. The degree of overlap between languages is known to affect lexical competition during online processing and production, and its relevance for second language acquisition has also been established. Nevertheless, determining what makes two languages similar (or not) increases in complexity when multiple levels of the language hierarchy (e.g., phonology, syntax) are considered. How can we feasibly account for the patterns of convergence and divergence at each level of representation, as well as the interactions between them? The growing field of network science brings new methodologies to bear on this longstanding question. Below, we summarize current network science approaches to modeling language structure and discuss implications for understanding various linguistic processes. Critically, we stress the particular value of multilayer techniques, unique and powerful in their ability to simultaneously accommodate an array of node-to-node (or word-to-word) relationships.

The effect of aging on facial attractiveness: An empirical and computational investigation

How does aging affect facial attractiveness? We tested the hypothesis that people find older faces less attractive than younger faces, and furthermore, that these aging effects are modulated by the age and sex of the perceiver and by the specific kind of attractiveness judgment being made. Using empirical and computational network science methods, we confirmed that with increasing age, faces are perceived as less attractive. This effect was less pronounced in judgments made by older than younger and middle-aged perceivers, and more pronounced by men (especially for female faces) than women. Attractive older faces were perceived as elegant more than beautiful or gorgeous. Furthermore, network analyses revealed that older faces were more similar in attractiveness and were segregated from younger faces. These results indicate that perceivers tend to process older faces categorically when making attractiveness judgments. Attractiveness is not a monolithic construct. It varies by age, sex, and the dimensions of attractiveness being judged.

Connecting brain and behavior in clinical neuroscience: A network approach

In recent years, there has been an increase in applications of network science in many different fields. In clinical neuroscience and psychopathology, the developments and applications of network science have occurred mostly simultaneously, but without much collaboration between the two fields. The promise of integrating these network applications lies in a united framework to tackle one of the fundamental questions of our time: how to understand the link between brain and behavior. In the current overview, we bridge this gap by introducing conventions in both fields, highlighting similarities, and creating a common language that enables the exploitation of synergies. We provide research examples in autism research, as it accurately represents research lines in both network neuroscience and psychological networks. We integrate brain and behavior not only semantically, but also practically, by showcasing three methodological avenues that allow to combine networks of brain and behavioral data. As such, the current paper offers a stepping stone to further develop multi-modal networks and to integrate brain and behavior.

Incorporating dynamic flight network in SEIR to model mobility between populations

Current efforts of modelling COVID-19 are often based on the standard compartmental models such as SEIR and their variations. As pre-symptomatic and asymptomatic cases can spread the disease between populations through travel, it is important to incorporate mobility between populations into the epidemiological modelling. In this work, we propose to modify the commonly-used SEIR model to account for the dynamic flight network, by estimating the imported cases based on the air traffic volume and the test positive rate. We conduct a case study based on data found in Canada to demonstrate how this modification, called Flight-SEIR, can potentially enable (1) early detection of outbreaks due to imported pre-symptomatic and asymptomatic cases, (2) more accurate estimation of the reproduction number and (3) evaluation of the impact of travel restrictions and the implications of lifting these measures. The proposed Flight-SEIR is essential in navigating through this pandemic and the next ones, given how interconnected our world has become.

Combining 4D Flow MRI and Complex Networks Theory to Characterize the Hemodynamic Heterogeneity in Dilated and Non-dilated Human Ascending Aortas

Motivated by the evidence that the onset and progression of the aneurysm of the ascending aorta (AAo) is intertwined with an adverse hemodynamic environment, the present study characterized in vivo the hemodynamic spatiotemporal complexity and organization in human aortas, with and without dilated AAo, exploring the relations with clinically relevant hemodynamic and geometric parameters. The Complex Networks (CNs) theory was applied for the first time to 4D flow magnetic resonance imaging (MRI) velocity data of ten patients, five of them presenting with AAo dilation. The time-histories along the cardiac cycle of velocity-based quantities were used to build correlation-based CNs. The CNs approach succeeded in capturing large-scale coherent flow features, delimiting flow separation and recirculation regions. CNs metrics highlighted that an increasing AAo dilation (expressed in terms of the ratio between the maximum AAo and aortic root diameter) disrupts the correlation in forward flow reducing the correlation persistence length, while preserving the spatiotemporal homogeneity of secondary flows. The application of CNs to in vivo 4D MRI data holds promise for a mechanistic understanding of the spatiotemporal complexity and organization of aortic flows, opening possibilities for the integration of in vivo quantitative hemodynamic information into risk stratification and classification criteria.

Fusion of text and graph information for machine learning problems on networks

Today, increased attention is drawn towards network representation learning, a technique that maps nodes of a network into vectors of a low-dimensional embedding space. A network embedding constructed this way aims to preserve nodes similarity and other specific network properties. Embedding vectors can later be used for downstream machine learning problems, such as node classification, link prediction and network visualization. Naturally, some networks have text information associated with them. For instance, in a citation network, each node is a scientific paper associated with its abstract or title; in a social network, all users may be viewed as nodes of a network and posts of each user as textual attributes. In this work, we explore how combining existing methods of text and network embeddings can increase accuracy for downstream tasks and propose modifications to popular architectures to better capture textual information in network embedding and fusion frameworks.

What Can Network Science Tell Us About Phonology and Language Processing?

Contemporary psycholinguistic models place significant emphasis on the cognitive processes involved in the acquisition, recognition, and production of language but neglect many issues related to the representation of language‐related information in the mental lexicon. In contrast, a central tenet of network science is that the structure of a network influences the processes that operate in that system, making process and representation inextricably connected. Here, we consider how the structure found across phonological networks of several languages from different language families may influence language processing as we age and experience diseases that affect cognition during the typical and atypical acquisition of new words, during typical perception and production of speech in adults, and during language change over time. We conclude that the network science approach may not only provide insights into specific language processes but also provide a way to connect the work from these domains, which are becoming increasingly balkanized.

Methodological Considerations for Incorporating Clinical Data Into a Network Model of Retrieval Failures

Difficulty retrieving information (e.g., words) from memory is prevalent in neurogenic communication disorders (e.g., aphasia and dementia). Theoretical modeling of retrieval failures often relies on clinical data, despite methodological limitations (e.g., locus of retrieval failure, heterogeneity of individuals, and progression of disorder/disease). Techniques from network science are naturally capable of handling these limitations. This paper reviews recent work using a multiplex lexical network to account for word retrieval failures and highlights how network science can address the limitations of clinical data. Critically, any model we employ could impact clinical practice and patient lives, harkening the need for theoretically well-informed network models.

Association between exposure to airborne pollutants and COVID-19 in Los Angeles, United States with ensemble-based dynamic emission model

This study aims to find the association between short-term exposure to air pollutants, such as particulate matters and ground-level ozone, and SARS-CoV-2 confirmed cases. Generalized linear models (GLM), a typical choice for ecological modeling, have well-established limitations. These limitations include apriori assumptions, inability to handle multicollinearity, and considering differential effects as the fixed effect. We propose an Ensemble-based Dynamic Emission Model (EDEM) to address these limitations. EDEM is developed at the intersection of network science and ensemble learning, i.e., a specialized approach of machine learning. Generalized Additive Model (GAM), i.e., a variant of GLM, and EDEM are tested in Los Angeles and Ventura counties of California, which is one of the biggest SARS-CoV-2 clusters in the US. GAM depicts that a 1 μg/m³, 1 μg/m³, and 1 ppm increase (lag 0-7) in PM 2.5, PM 10, and O3 is associated with 4.51% (CI: 7.01 to -2.00) decrease, 1.62% (CI: 2.23 to -1.022) decrease, and 4.66% (CI: 0.85 to 8.47) increase in daily SARS-CoV-2 cases, respectively. Subsequent increment in lag resulted in the negative association between pollutants and SARS-CoV-2 cases. EDEM results in an R2 score of 90.96% and 79.16% on training and testing datasets, respectively. EDEM confirmed the negative association between particulates and SARS-CoV-2 cases; whereas, the O3 depicts a positive association; however, the positive association observed through GAM is not statistically significant. In addition, the county-level analysis of pollutant concentration interactions suggests that increased emissions from other counties positively affect SARS-CoV-2 cases in adjoining counties as well. The results reiterate the significance of uniformly adhering to air pollution mitigation strategies, especially related to ground-level ozone.

Evaluating three stuttering assessments through network analysis, random forests and cluster analysis

PURPOSE

In stuttering, cognitive and behavioural variables interact in nonlinear fashion. These variables can be assessed by instruments which evaluate perceived impact of stuttering and stuttering severity. We applied three statistical methods in combination to the analysis of three assessment protocols to discover relationships within and between the tests to better understand variations in behavioural and social aspects of stuttering.

METHODS

Scores from Stuttering Severity Index (SSI-IV), Overall Assessment of the Speaker's Experience of Stuttering scale (OASES), and Unhelpful Thoughts and Beliefs About Stuttering scale (UTBAS), collected from 26 participants were compared using three statistical methods: network analysis, random forests, and cluster analysis.

RESULTS

Network analysis demonstrated that SSI-IV only weakly interacts with a quality of life index (OASES) and a self-perception and belief systems index (UTBAS). Random forest analyses revealed the last two measures relate strongly to each other. The results from cluster analysis suggest a) a possible regrouping of OASES items and b) a possible use of one UTBAS scale instead of the three.

CONCLUSION

A combination of three statistical methods allowed us to evaluate the three assessments in more depth. The lack of interaction between the SSI-IV on the one hand, and OASES and UTBAS on the other, suggests that the network of the three commonly used stuttering assessments may be fractured in a non-productive way. A potential gap may exist for an assessment tool that would link behavioural and social aspects of stuttering.

Survey on graph embeddings and their applications to machine learning problems on graphs

Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.

Network model and analysis of the spread of Covid-19 with social distancing

The first mitigation response to the Covid-19 pandemic was to limit person-to-person interaction as much as possible. This was implemented by the temporary closing of many workplaces and people were required to follow social distancing. Networks are a great way to represent interactions among people and the temporary severing of these interactions. Here, we present a network model of human-human interactions that could be mediators of disease spread. The nodes of this network are individuals and different types of edges denote family cliques, workplace interactions, interactions arising from essential needs, and social interactions. Each individual can be in one of four states: susceptible, infected, immune, and dead. The network and the disease parameters are informed by the existing literature on Covid-19. Using this model, we simulate the spread of an infectious disease in the presence of various mitigation scenarios. For example, lockdown is implemented by deleting edges that denote non-essential interactions. We validate the simulation results with the real data by matching the basic and effective reproduction numbers during different phases of the spread. We also simulate different possibilities of the slow lifting of the lockdown by varying the transmission rate as facilities are slowly opened but people follow prevention measures like wearing masks etc. We make predictions on the probability and intensity of a second wave of infection in each of these scenarios.

Evaluating the effect of city lock-down on controlling COVID-19 propagation through deep learning and network science models

The special epistemic characteristics of the COVID-19, such as the long incubation period and the infection through asymptomatic cases, put severe challenge to the containment of its outbreak. By the end of March 2020, China has successfully controlled the within- spreading of COVID-19 at a high cost of locking down most of its major cities, including the epicenter, Wuhan. Since the low accuracy of outbreak data before the mid of Feb. 2020 forms a major technical concern on those studies based on statistic inference from the early outbreak. We apply the supervised learning techniques to identify and train NP-Net-SIR model which turns out robust under poor data quality condition. By the trained model parameters, we analyze the connection between population flow and the cross-regional infection connection strength, based on which a set of counterfactual analysis is carried out to study the necessity of lock-down and substitutability between lock-down and the other containment measures. Our findings support the existence of non-lock-down-typed measures that can reach the same containment consequence as the lock-down, and provide useful guideline for the design of a more flexible containment strategy.

Heterogeneity in SIR epidemics modeling: superspreaders and herd immunity

Deterministic epidemic models, such as the Susceptible-Infected-Recovered (SIR) model, are immensely useful even if they lack the nuance and complexity of social contacts at the heart of network science modeling. Here we present a simple modification of the SIR equations to include the heterogeneity of social connection networks. A typical power-law model of social interactions from network science reproduces the observation that individuals with a high number of contacts, "hubs" or "superspreaders", can become the primary conduits for transmission. Conversely, once the tail of the distribution is saturated, herd immunity sets in at a smaller overall recovered fraction than in the analogous SIR model. The new dynamical equations suggest that cutting off the tail of the social connection distribution, i.e., stopping superspreaders, is an efficient non-pharmaceutical intervention to slow the spread of a pandemic, such as the Coronavirus Disease 2019 (COVID-19).

Quantifying the Interplay of Semantics and Phonology During Failures of Word Retrieval by People With Aphasia Using a Multiplex Lexical Network

Investigating instances where lexical selection fails can lead to deeper insights into the cognitive machinery and architecture supporting successful word retrieval and speech production. In this paper, we used a multiplex lexical network approach that combines semantic and phonological similarities among words to model the structure of the mental lexicon. Network measures at different levels of analysis (degree, network distance, and closeness centrality) were used to investigate the influence of network structure on picture naming accuracy and errors by people with Anomic, Broca's, Conduction, and Wernicke's aphasia. Our results reveal that word retrieval is influenced by the multiplex lexical network structure in at least two ways-(a) the accuracy of production and error type on incorrect productions were influenced by the degree and closeness centrality of the target word, and (b) error type also varied in terms of network distance between the target word and produced error word. Taken together, the analyses demonstrate that network science techniques, particularly the use of the multiplex lexical network to simultaneously represent semantic and phonological relationships among words, reveal how the structure of the mental lexicon influences language processes beyond traditionally examined psycholinguistic variables. We propose a framework for how the multiplex lexical network approach allows for understanding the influence of mental lexicon structure on word retrieval processes, with an eye toward a better understanding of the nature of clinical impairments, like aphasia.

Uncovering differential identifiability in network properties of human brain functional connectomes

The identifiability framework (𝕀f) has been shown to improve differential identifiability (reliability across-sessions and -sites, and differentiability across-subjects) of functional connectomes for a variety of fMRI tasks. But having a robust single session/subject functional connectome is just the starting point to subsequently assess network properties for characterizing properties of integration, segregation, and communicability, among others. Naturally, one wonders whether uncovering identifiability at the connectome level also uncovers identifiability on the derived network properties. This also raises the question of where to apply the 𝕀f framework: on the connectivity data or directly on each network measurement? Our work answers these questions by exploring the differential identifiability profiles of network measures when 𝕀f is applied (a) on the functional connectomes, and (b) directly on derived network measurements. Results show that improving across-session reliability of functional connectomes (FCs) also improves reliability of derived network measures. We also find that, for specific network properties, application of 𝕀f directly on network properties is more effective. Finally, we discover that applying the framework, either way, increases task sensitivity of network properties. At a time when the neuroscientific community is focused on subject-level inferences, this framework is able to uncover FC fingerprints, which propagate to derived network properties.

Development of an adverse drug event network to predict drug toxicity

Despite of their therapeutic effects, drug's exposure may have negative effects on human health such as adverse drug reaction (ADR) and side effects (SE). Adverse drug events (ADEs), that correspond to an event occurring during the drug treatment (i.e. ADR and SE), is not necessarily caused by the drug itself, as this is the case with medical errors and social factors. Due to the complexity of the biological systems, not all ADEs are known for marketed drugs. Therefore, new and effective methods are needed to determine potential risks, including the development of computational strategies. We present an ADE association network based on 90,827 drug-ADE associations between 930 unique drug and 6221 unique ADE, on which we implemented a scoring system based on a pull-down approach for prediction of drug-ADE combination. Based on our network, ADEs proposed for three drugs, safinamide, sonidegib, rufinamide are further discussed. The model was able to identify, already known drug-ADE associations that are supported by the literature and FDA reports, and also to predict uncharacterized associations such as dopamine dysregulation syndrome, or nicotinic acid deficiency for the drugs safinamide and sonidegib respectively, illustrating the power of such integrative toxicological approach.

The greater inflammatory pathway-high clinical potential by innovative predictive, preventive, and personalized medical approach

BACKGROUND AND LIMITATIONS

Impaired wound healing (WH) and chronic inflammation are hallmarks of non-communicable diseases (NCDs). However, despite WH being a recognized player in NCDs, mainstream therapies focus on (un)targeted damping of the inflammatory response, leaving WH largely unaddressed, owing to three main factors. The first is the complexity of the pathway that links inflammation and wound healing; the second is the dual nature, local and systemic, of WH; and the third is the limited acknowledgement of genetic and contingent causes that disrupt physiologic progression of WH.

PROPOSED APPROACH

Here, in the frame of Predictive, Preventive, and Personalized Medicine (PPPM), we integrate and revisit current literature to offer a novel systemic view on the cues that can impact on the fate (acute or chronic inflammation) of WH, beyond the compartmentalization of medical disciplines and with the support of advanced computational biology.

CONCLUSIONS

This shall open to a broader understanding of the causes for WH going awry, offering new operational criteria for patients' stratification (prediction and personalization). While this may also offer improved options for targeted prevention, we will envisage new therapeutic strategies to reboot and/or boost WH, to enable its progression across its physiological phases, the first of which is a transient acute inflammatory response versus the chronic low-grade inflammation characteristic of NCDs.

Egocentric sexual networks of men who have sex with men in the United States: Results from the ARTnet study

In this paper, we present an overview and descriptive results from one of the first egocentric network studies of men who have sex with men (MSM) from across the United States: the ARTnet study. ARTnet was designed to support prevention research for human immunodeficiency virus (HIV) and other sexually transmitted infections (STIs) that are transmitted across partnership networks. ARTnet implemented a population-based egocentric network study design that sampled egos from the target population and asked them to report on the number, attributes, and timing of their sexual partnerships. Such data provide the foundation needed for parameterizing stochastic network models that are used for disease projection and intervention planning. ARTnet collected data online from 2017 to 2019, with a final sample of 4904 participants who reported on 16198 sexual partnerships. The aims of this paper were to characterize the joint distribution of three network parameters needed for modeling: degree distributions, assortative mixing, and partnership age, with heterogeneity by partnership type (main, casual and one-time), demography, and geography. Participants had an average of 1.19 currently active partnerships ("mean degree"), which was higher for casual partnerships (0.74) than main partnerships (0.45). The mean rate of one-time partnership acquisition was 0.16 per week (8.5 partners per year). Main partnerships lasted 272.5 weeks on average, while casual partnerships lasted 133.0 weeks. There was strong but heterogenous assortative mixing by race/ethnicity for all groups. The mean absolute age difference for all partnership types was 9.5 years, with main partners differing by 6.3 years compared to 10.8 years for casual partners. Our analysis suggests that MSM may be at sustained risk for HIV/STI acquisition and transmission through high network degree of sexual partnerships. The ARTnet network study provides a robust and reproducible foundation for understanding the dynamics of HIV/STI epidemiology among U.S. MSM and supporting the implementation science that seeks to address persistent challenges in HIV/STI prevention.

Implementing a concept network model

The same concept can mean different things or be instantiated in different forms, depending on context, suggesting a degree of flexibility within the conceptual system. We propose that a feature-based network model can be used to capture and predict this flexibility. We modeled individual concepts (e.g., BANANA, BOTTLE) as graph-theoretical networks, in which properties (e.g., YELLOW, SWEET) were represented as nodes and their associations as edges. In this framework, networks capture within-concept statistics that reflect how properties relate to one another across instances of a concept. We extracted formal measures of these networks that capture different aspects of network structure, and explored whether a concept's network structure relates to its flexibility of use. To do so, we compared network measures to a text-based measure of semantic diversity, as well as to empirical data from a figurative-language task and an alternative-uses task. We found that network-based measures were predictive of the text-based and empirical measures of flexible concept use, highlighting the ability of this approach to formally capture relevant characteristics of conceptual structure. Conceptual flexibility is a fundamental attribute of the cognitive and semantic systems, and in this proof of concept we reveal that variations in concept representation and use can be formally understood in terms of the informational content and topology of concept networks.

What would a synthetic connectome look like?

A major challenge of contemporary neuroscience is to unravel the structure of the connectome, the ensemble of neural connections that link between different functional units of the brain, and to reveal how this structure relates to brain function. This thriving area of research largely follows the general tradition in biology of reverse-engineering, which consists of first observing and characterizing a biological system or process, and then deconstructing it into its fundamental building blocks in order to infer its modes of operation. However, a complementary form of biology has emerged, synthetic biology, which emphasizes construction-based forward-engineering. The synthetic biology approach comprises the assembly of new biological systems out of elementary biological parts. The rationale is that the act of building a system can be a powerful method for gaining deep understanding of how that system works. As the fields of connectomics and synthetic biology are independently growing, I propose to consider the benefits of combining the two, to create synthetic connectomics, a new form of neuroscience and a new form of synthetic biology. The goal of synthetic connectomics would be to artificially design and construct the connectomes of live behaving organisms. Synthetic connectomics could serve as a unifying platform for unraveling the complexities of brain operation and perhaps also for generating new forms of artificial life, and, in general, could provide a valuable opportunity for empirically exploring theoretical predictions about network function. What would a synthetic connectome look like? What purposes would it serve? How could it be constructed? This review delineates the novel notion of a synthetic connectome and aims to lay out the initial steps towards its implementation, contemplating its impact on science and society.

NET-EXPO: A Gephi Plugin Towards Social Network Analysis of Network Exposure for Unipartite and Bipartite Graphs

Social network analysis (SNA) concerns itself in studying network structures in relation to individuals' behavior. Individuals may be influenced by their network members in their behavior, and thus past researchers have developed computational methods that allow us to measure the extent to which individuals are exposed to members with certain behavior within one's social network, and that be correlated with their own behavior. Some of these methods include network exposure model, affiliation exposure model, and decomposed network exposure models. We developed a Gephi plugin that computes and visualizes these various kinds of network exposure models called NET-EXPO. We experimented with NET-EXPO on some social network datasets to demonstrate its pragmatic use in social network research. This plugin has the potential to equip researchers with a tool to compute network exposures in a user friendly way and simplify the process to compute and visualize the network data.