Search

A large fraction of the world's population is directly impacted by acute or chronic viral infections, many of which have high mortality. As was brought home to us in 2020, viruses also have great potential to generate global pandemics that have killed millions and caused massive damage to economies. Clearly, we need cost-effective and rapid methods for finding drug treatments for poorly met infectious diseases and for responding effectively to the current and future pandemics. Repurposing or off-label use of existing drugs, whose safety and pharmacokinetics are well understood, is one useful way to provide fast drug therapies for patients. Computational methods have an important role to play because of their increasing effectiveness, high speed, and relatively low cost. Here we review the application of the main types of computational drug repurposing methods to discovery of therapies for viral diseases and for future pandemics highly likely to be caused by viral pathogens. [ABSTRACT FROM AUTHOR]

The study of specific physiological processes from the perspective of network physiology has gained recent attention. Modeling the global information integration among the separated functionalized modules in structural and functional brain networks is a central problem. In this article, the preferentially cutting-rewiring operation (PCRO) is introduced to approximatively describe the above physiological process, which consists of the cutting procedure and the rewiring procedure with specific preferential constraints. By applying the PCRO on the classical Erdös-Rényi random network (ERRN), three types of isolated nodes are generated, based on which the common leaves (CLs) are formed between the two hubs. This makes the initially homogeneous ERRN experience drastic changes and become heterogeneous. Importantly, a statistical analysis method is proposed to theoretically analyze the statistical properties of an ERRN with a PCRO. Specifically, the probability distributions of these three types of isolated nodes are derived, based on which the probability distribution of the CLs can be obtained easily. Furthermore, the validity and universality of our statistical analysis method have been confirmed in numerical experiments. Our contributions may shed light on a new perspective in the interdisciplinary field of complexity science and biological science and would be of great and general interest to network physiology. [ABSTRACT FROM AUTHOR]

Understanding the brain requires studying its multiscale interactions from molecules to networks. The increasing availability of large-scale datasets detailing brain circuit composition, connectivity, and activity is transforming neuroscience. However, integrating and interpreting this data remains challenging. Concurrently, advances in supercomputing and sophisticated modeling tools now enable the development of highly detailed, large-scale biophysical circuit models. These mechanistic multiscale models offer a method to systematically integrate experimental data, facilitating investigations into brain structure, function, and disease. This review, based on a Society for Neuroscience 2024 MiniSymposium, aims to disseminate recent advances in large-scale mechanistic modeling to the broader community. It highlights (1) examples of current models for various brain regions developed through experimental data integration; (2) their predictive capabilities regarding cellular and circuit mechanisms underlying experimental recordings (e.g., membrane voltage, spikes, local-field potential, electroencephalography/magnetoencephalography) and brain function; and (3) their use in simulating biomarkers for brain diseases like epilepsy, depression, schizophrenia, and Parkinson’s, aiding in understanding their biophysical underpinnings and developing novel treatments. The review showcases state-of-the-art models covering hippocampus, somatosensory, visual, motor, auditory cortical, and thalamic circuits across species. These models predict neural activity at multiple scales and provide insights into the biophysical mechanisms underlying sensation, motor behavior, brain signals, neural coding, disease, pharmacological interventions, and neural stimulation. Collaboration with experimental neuroscientists and clinicians is essential for the development and validation of these models, particularly as datasets grow. Hence, this review aims to foster interest in detailed brain circuit models, leading to cross-disciplinary collaborations that accelerate brain research. [ABSTRACT FROM AUTHOR]

Case formulation is at the heart of personalised care in psychotherapy. Scientific research into case formulations can provide new insights in the heterogeneity of psychopathology which are relevant for advances in personalised psychopathology research and practice. This mixed-methods study examined the content of 483 fully personalised network-based case formulations in psychotherapy in terms of uniqueness (i.e., frequencies of concepts) and commonality (i.e., the presence of common themes over the different case formulations). In a real-world clinical care setting, patients co-created network-based case formulations with their therapist as part of their routine diagnostic process. These case formulations feature concepts that are relevant to individual patients and their current situation. We assessed how often concepts were used by different patients to quantify uniqueness. We applied a bottom-up thematic analysis to identify patient-relevant themes from the concepts. The case formulations of 483 patients diagnosed with mood and/or anxiety disorders contained a total of 4908 interpretable concepts of which 4272 (87%) were completely unique. Through thematic analysis, we identified seven overarching themes in the concepts: autonomy, connectedness, emotions, self-care, identity, self-efficacy, and bodily sensations. Case formulations were highly unique, thereby illustrating the importance of personalised diagnostics. The unique concepts could be grouped under seven overarching themes which seem to encompass basic human needs. Current advancements in personalised diagnostics and assessment should have a broader scope than symptoms alone, and could use the themes identified here as part of a topic list in the generation of (network-based) case formulations. [ABSTRACT FROM AUTHOR]

Abstract Ecosystems are rapidly degraded by anthropogenic pressures, affecting the provision of ecosystem services. Therefore, it is increasingly important that we can quantify and manage ecosystem services to maintain human well‐being. Many ecosystem services are underpinned by ecosystem functions and processes that are driven by interspecific ecological interactions. Humans then benefit from ecosystem services through socio‐ecological interactions. Therefore, interaction network approaches can provide a unique understanding of ecosystem service flows. In this paper, we assess the current interface between network ecology and ecosystem services, before exploring how work linking these two fields could be enhanced. We emphasise the value of network approaches and explore network methods to improve the assessment and management of ecosystem services. Within this, we highlight the role of local and indigenous knowledge in operationalising network approaches in a useful ecosystem service context. Read the free Plain Language Summary for this article on the Journal blog.

Claiming causal inferences in network settings necessitates careful consideration of the often complex dependency between outcomes for actors. Of particular importance are treatment spillover or outcome interference effects. We consider causal inference when the actors are connected via an underlying network structure. Our key contribution is a model for causality when the underlying network is endogenous; where the ties between actors and the actor covariates are statistically dependent. We develop a joint model for the relational and covariate generating process that avoids restrictive separability and fixed network assumptions, as these rarely hold in realistic social settings. While our framework can be used with general models, we develop the highly expressive class of Exponential-family Random Network models (ERNM) of which Markov random fields and Exponential-family Random Graph models are special cases. We present potential outcome-based inference within a Bayesian framework and propose a modification to the exchange algorithm to allow for sampling from ERNM posteriors. We present results of a simulation study demonstrating the validity of the approach. Finally, we demonstrate the value of the framework in a case study of smoking in the context of adolescent friendship networks. [ABSTRACT FROM AUTHOR]

To help incorporate security into INCOSE's Systems Engineering Vision 2035, the INCOSE systems security engineering working group endorses a paradigmatic shift to reframe systems security as trustworthy, loss‐driven, and capabilities‐based. Similar research out of Sandia National Laboratories has explored cutting‐edge approaches to systems security for national security applications. Together, these efforts highlight the need for—‐and a path toward—‐a scientific foundation for security. Leveraging underlying tenets of systems theory, observed security heuristics, and the concepts emerging from INCOSE's SSE working helps triangulate a set of "first principles" as part of a scientific foundation for security as an emergent systems property that incorporates traditional physical security designs, cyber security architectures, and personnel security programs—‐as well as the (often ignored) interactions between them. These first principles, in turn, are the basis for a set of derived systems security performance axioms that support current INCOSE SSE working efforts. We have demonstrated this approach's logic and designability with a multilayer network model‐based approach for systems security. The structure of this scientific foundation for security offers additional, innovative opportunities to achieve desired levels of trustworthiness, creative mechanisms to meet needs, innovative loss‐driven approaches, and enhanced capabilities—‐all aimed at producing more efficient and effective systems security solutions against current and emerging threats, uncertainties, and complexities. [ABSTRACT FROM AUTHOR]

Ecosystems are rapidly degraded by anthropogenic pressures, affecting the provision of ecosystem services. Therefore, it is increasingly important that we can quantify and manage ecosystem services to maintain human well‐being.Many ecosystem services are underpinned by ecosystem functions and processes that are driven by interspecific ecological interactions. Humans then benefit from ecosystem services through socio‐ecological interactions. Therefore, interaction network approaches can provide a unique understanding of ecosystem service flows.In this paper, we assess the current interface between network ecology and ecosystem services, before exploring how work linking these two fields could be enhanced.We emphasise the value of network approaches and explore network methods to improve the assessment and management of ecosystem services. Within this, we highlight the role of local and indigenous knowledge in operationalising network approaches in a useful ecosystem service context. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

In this paper, I argue that epidemic modeling of disease spreading involves both multi-model as well as multi-methodology reasoning. I demonstrate that epidemic modeling includes two different modeling methodologies: compartment and network modeling. Furthermore, within each of those methodologies, models are often constructed as structured groups of models, rather than as single models. I analyse the epistemic advantages of this mode of modeling, including complementary reasoning with different modeling methodologies and the ability to easily compare explanatory advantages of different models.

The discrete- and continuous-time network models of opinions control and resource allocation in marketing are considered. Three cases of interaction of economic agents are studied: independent behavior, cooperation, and hierarchical control by the resource-owning Principal. The corresponding dynamic games are analytically solved. The agents' payoffs in these cases are compared. Two concepts, "enough resources" and "a lack of resources," are introduced and investigated. The theoretical results are illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

Policy PointsPromoting healthy public policies is a national priority, but state policy adoption is driven by a complex set of internal and external factors.This study employs new social network methods to identify underlying connections among states and to predict the likelihood of new firearm‐related policy adoption given changes to this interstate network.This approach could be used to assess the likelihood that a given state will adopt a specific new firearm‐related law and to identify points of influence that could either inhibit or promote wider diffusion of specific laws. Context: US states are largely responsible for the regulation of firearms within their borders. Each state has developed a different legal environment with regard to firearms based on different values and beliefs of citizens, legislators, governors, and other stakeholders. Predicting the types of firearm laws that states may adopt is therefore challenging. Methods: We propose a parsimonious model for this complex process and provide credible predictions of state firearm laws by estimating the likelihood they will be passed in the future. We employ a temporal exponential‐family random graph model to capture the bipartite state law–state network data over time, allowing for complex interdependencies and their temporal evolution. Using data on all state firearm laws over the period 1979–2020, we estimate these models' parameters while controlling for factors associated with firearm law adoption, including internal and external state characteristics. Predictions of future firearm law passage are then calculated based on a number of scenarios to assess the effects of a given type of firearm law being passed in the future by a given state. Findings: Results show that a set of internal state factors are important predictors of firearm law adoption, but the actions of neighboring states may be just as important. Analysis of scenarios provide insights into the mechanics of how adoption of laws by specific states (or groups of states) may perturb the rest of the network structure and alter the likelihood that new laws would become more (or less) likely to continue to diffuse to other states. Conclusions: The methods used here outperform standard approaches for policy diffusion studies and afford predictions that are superior to those of an ensemble of machine learning tools. The proposed framework could have applications for the study of policy diffusion in other domains. [ABSTRACT FROM AUTHOR]

The sum score on a psychological test is, and should continue to be, a tool central in psychometric practice. This position runs counter to several psychometricians' belief that the sum score represents a pre-scientific conception that must be abandoned from psychometrics in favor of latent variables. First, we reiterate that the sum score stochastically orders the latent variable in a wide variety of much-used item response models. In fact, item response theory provides a mathematically based justification for the ordinal use of the sum score. Second, because discussions about the sum score often involve its reliability and estimation methods as well, we show that, based on very general assumptions, classical test theory provides a family of lower bounds several of which are close to the true reliability under reasonable conditions. Finally, we argue that eventually sum scores derive their value from the degree to which they enable predicting practically relevant events and behaviors. None of our discussion is meant to discredit modern measurement models; they have their own merits unattainable for classical test theory, but the latter model provides impressive contributions to psychometrics based on very few assumptions that seem to have become obscured in the past few decades. Their generality and practical usefulness add to the accomplishments of more recent approaches. [ABSTRACT FROM AUTHOR]

In conventional vehicle design approaches, there is typically little understanding of the consequences of early stage design choices. This may be attributed to the conventional approach’s limitations in capturing complex interactions, which leads to increased design iterations. To overcome this, holistic multidisciplinary models have been developed. However, these models introduce the burden of complexity and costs due to their intricate nature. Additionally, it is challenging to gain meaningful insights without a deeper understanding of the model’s structure and behavior. In this article, an alternative form of model representation is proposed to address these shortcomings. This was achieved by integrating two concepts: network theory and sensitivity analysis. A detailed and robust framework is provided, which represents complex multidisciplinary models as network models, reducing their complexity and allowing insights to be extracted from them. This approach was demonstrated through a case study of a rail vehicle traction system, including a traction motor and an inverter, coupled with an operational drive cycle. Among the 246 factors identified in the traction system network model, the three most influential inputs were determined for the selected output factor of interest. Subsequently, the knock-on effects of these inputs were assessed. The results indicate a significant reduction in the network graph size compared to the complete network graph of the traction system model, highlighting a substantial decrease in the number of factors considered in the analysis. This demonstrates the capability of the proposed framework to simplify the analysis while retaining the ability to examine intricate interaction effects.

Centrality measures are widely utilized in complex networks to assess the importance of nodes. The choice of measure depends on the network type, leading to diverse node rankings. This paper aims to compare various centrality measures by examining their correlations. We specifically focus on the Pearson correlation coefficient and Spearman correlation. Pearson correlation considers node centrality values, while Spearman correlation is based on node ranks. Our study encompasses different network topologies, including random, scale-free, and small-world networks. We investigate how these network structures influence correlation values. The main part of the paper describes the relationship between correlations and network model parameters. Additionally, we explore the impact of global network characteristics on correlations, as well as their direct connection to network parameters. Through a systematic review of literature-based centrality measures, we have identified and selected the most commonly employed ones to investigate their correlation including degree centrality, betweenness centrality, eigenvector centrality, and closeness centrality. Our findings reveal that correlations in random networks are minimally affected by network structure, whereas restructuring significantly impacts correlations in other networks. In particular, we show a notable impact of structural parameter variations on correlations within small-world networks. Furthermore, we demonstrate the substantial influence of fundamental network characteristics such as spectral gap, global efficiency, and majorization gap on correlations. We show that amongst the various properties, the spectral gap stands out as the most valuable indicator for estimating correlations.

Since the seminal research of Harry Markowitz, the importance of Pareto Optimal portfolios and asset correlation has been a foundation of modern portfolio theory. Recent researchers have expanded on Markowitz-efficient portfolios using advanced statistical methods to identify correlations among assets, while other researchers have demonstrated the decline in asset correlations during periods of market volatility and economic shocks. We extend this research by applying a novel approach based on the concept of population analysis to study the correlation of assets under major economic shocks. We use similarity networks to investigate the impact of the COVID-19 pandemic on various sectors, and then compare it with the behavioural patterns associated with the 9/11 event. The population analysis revealed that during the current pandemic, the behavioural pattern of the finance and energy sectors was significantly different than other sectors. Similar results were found for the finance and the industrial sectors during the 9/11 attacks. [ABSTRACT FROM AUTHOR]

Background: Laparoscopic surgery demands high precision and skill, necessitating effective training protocols that account for factors such as hand dominance. This study investigates the impact of hand dominance on the acquisition and proficiency of laparoscopic surgical skills, utilizing a novel assessment method that combines Network Models and electromyography (EMG) data. Methods: Eighteen participants, comprising both medical and non-medical students, engaged in laparoscopic simulation tasks, including peg transfer and wire loop tasks. Performance was assessed using Network Models to analyze EMG data, capturing muscle activity and learning progression. The NASA Task Load Index (TLX) was employed to evaluate subjective task demands and workload perceptions. Results: Our analysis revealed significant differences in learning progression and skill proficiency between dominant and non-dominant hands, suggesting the need for tailored training approaches. Network Models effectively identified patterns of skill acquisition, while NASA-TLX scores correlated with participants' performance and learning progression, highlighting the importance of considering both objective and subjective measures in surgical training. Conclusions: The study underscores the importance of hand dominance in laparoscopic surgical training and suggests that personalized training protocols could enhance surgical precision, efficiency, and patient outcomes. By leveraging advanced analytical techniques, including Network Models and EMG data analysis, this research contributes to optimizing clinical training methodologies, potentially revolutionizing surgical education and improving patient care. [ABSTRACT FROM AUTHOR]

The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications. [ABSTRACT FROM AUTHOR]

The self-disorder model provides a phenomenological framework for understanding how the core symptoms of Schizophrenia Spectrum Disorders (SSDs) are rooted in an instability of minimal selfhood. This instability involves a range of “anomalous experiences”: transformations in an individual’s perceptual field and sense of being an agent of action. The explanatory value of this theoretical model can be summarized in two claims about the role of anomalous experiences in self-disorders: (1) anomalous experiences express a common trait-like disturbance that is characteristic of SSDs, and (2) anomalous experiences contribute to explanations regarding how self-disorders progress into SSDs. While hypotheses consistent with the first claim have been substantiated in several studies, hypotheses consistent with the second claim remain underexamined. We argue that attending to this gap in the literature requires considering which methodological strategies are best suited for investigating if and how anomalous experiences contribute to the development of self-disorders into SSDs and psychotic states. Network modeling is discussed as one such strategy suitable for examining the role of anomalies of lived-world in the maintenance of paranoid delusions in SSDs. [ABSTRACT FROM AUTHOR]

The number one criticism of average-case analysis is that we do not actually know the probability distribution of real-world inputs. Thus, analyzing an algorithm on some random model has no implications for practical performance. At its core, this criticism doubts the existence of external validity; i.e., it assumes that algorithmic behavior on the somewhat simple and clean models does not translate beyond the models to practical performance real-world input. With this article, we provide a first step toward studying the question of external validity systematically. To this end, we evaluate the performance of six graph algorithms on a collection of 2,740 sparse real-world networks depending on two properties: heterogeneity (variance in the degree distribution) and locality (tendency of edges to connect vertices that are already close). We compare this with the performance on generated networks with varying locality and heterogeneity. We find that the performance in the idealized setting of network models translates surprisingly well to real-world networks. Moreover, heterogeneity and locality appear to be the core properties impacting the performance of many graph algorithms. [ABSTRACT FROM AUTHOR]

Depressed individuals hold negative schemas and experience less positivity in their lives. Network analyses suggest that this may be due to connectivity among negative concepts within depressed individuals' schemas. However, the extent to which positivity interacts with negativity in depressed persons' schemas has not been thoroughly assessed. Thus, we examined the interactive components of the Fear of Happiness Scale, a construct that examines negative emotions associated with happiness, in 966 individuals with depressive symptoms ranging from none to severe via a moderated network model. Patterns of connectivity differed by depressive symptom severity. Notably, as depressive symptoms increased, items representing avoidance and dampening of positivity with negative thoughts went from weakly, negatively connected at no depressive symptoms to strongly, positively connected at elevated depressive symptoms. In other words, individuals who experience higher levels of depressive symptoms devalue positivity in their semantic networks, demonstrating an opposite pattern to those without depressive symptoms. Future work should longitudinally examine other individual differences related to the devaluation of reward to ascertain how these individual differences directly contribute to and maintain depressive symptoms. [ABSTRACT FROM AUTHOR]

Ordinal data are extremely common in psychological research, with variables often assessed using Likert-type scales that take on only a few values. At the same time, researchers are increasingly fitting network models to ordinal item-level data. Yet very little work has evaluated how network estimation techniques perform when data are ordinal. We use a Monte Carlo simulation to evaluate and compare the performance of three estimation methods applied to either Pearson or polychoric correlations: extended Bayesian information criterion graphical lasso with regularized edge estimates ("EBIC"), Bayesian information criterion model selection with partial correlation edge estimates ("BIC"), and multiple regression with p-value-based edge selection and partial correlation edge estimates ("MR"). We vary the number and distribution of thresholds, distribution of the underlying continuous data, sample size, model size, and network density, and we evaluate results in terms of model structure (sensitivity and false positive rate) and edge weight bias. Our results show that the effect of treating the data as ordinal versus continuous depends primarily on the number of levels in the data, and that estimation performance was affected by the sample size, the shape of the underlying distribution, and the symmetry of underlying thresholds. Furthermore, which estimation method is recommended depends on the research goals: MR methods tended to maximize sensitivity of edge detection, BIC approaches minimized false positives, and either one of these produced accurate edge weight estimates in sufficiently large samples. We identify some particularly difficult combinations of conditions for which no method produces stable results. Psychometric network models are statistical models used to explore the associations among variables thought to mutually influence each other, and have been widely applied in psychopathology, politics, and intelligence. Although psychological researchers often collect data measured on an ordinal scale, meaning that variables can only take on a few values, little research has examined how network models perform when applied to such data, or which estimation technique researchers should use. In this article, we conduct a simulation study evaluating and comparing three different estimation methods when applied to ordinal data, varying how the data was treated and characteristics of both the data and network model. The three approaches we compare are: extended Bayesian information criterion graphical lasso ("EBIC"; a widely-used network estimation approach), Bayesian information criterion model selection ("BIC"; a popular nonregularized approach), and a multiple regression-based approach ("MR"). Our results indicate that if researchers are interested in estimating networks with ordinal data, then they should be aware that estimation performance of all three methods is affected by whether the data is treated as ordinal versus continuous, the sample size, the shape of the underlying continuous distribution, and the symmetry of the underlying thresholds. Furthermore, which estimation method we recommend depends on the research goal: MR methods maximize the ability to detect relations between variables, BIC methods minimize the detection of false positive relations, and either of these approaches accurately estimate the strength of the relations once the sample size is large enough. [ABSTRACT FROM AUTHOR]

Abstract Metapopulation models include spatial population dynamics such as dispersion and migration between subpopulations. Integral projection models (IPMs) can include demographic rates as a function of size. Traditionally, metapopulation models do not included detailed populaiton models such as IPMs. In some situations, both local population dynamics (e.g. size‐based survival) and spatial dynamics are important. We present a Python package, MetaIPM, which places IPMs into a metapopulation framework, and allow users to readily construct and apply these models that combine local population dynamics within a metapopulation framework. MetaIPM includes an IPM for each subpopulation that is connected to other subpopulations via a metapopulation movement model. These movements can include dispersion, migration or other patterns. The IPM can include for size‐specific demographic rates (e.g. survival, recruitment) as well as management actions, such as length‐based harvest (e.g. gear specific capture sizes, varying slot limits across political boundaries). The model also allows for changes in metapopulation connectivity between locations, such as a fish passage ladders to enhance movement or deterrents to reduce movement. Thus, resource managers can use MetaIPM to compare different management actions such as the harvest gear type (which can be length‐specific) and harvest locations. We demonstrate how MetaIPM may be applied to inform managers seeking to limit the spread of an invasive species in a system with important metapopulation dynamics. Specifically, we compared removal lengths (all length fish versus longer fish only) for an invasive fish population in a fragmented, inland river system. MetaIPM allowed users to compare the importance of harvesting source populations away from the invasion front, as well as species at the invasion front. The model would also allow for future comparisons of different deterrent placement locations in the system. Moving beyond our example system, we describe how MetaIPM can be applied to other species, systems and management approaches. The MetaIPM packages includes Jupyter Notebooks documenting the package as well as a second set of JupyterNotebooks showing the application of the package to our example system.

Ordinal data are extremely common in psychological research, with variables often assessed using Likert-type scales that take on only a few values. At the same time, researchers are increasingly fitting network models to ordinal item-level data. Yet very little work has evaluated how network estimation techniques perform when data are ordinal. We use a Monte Carlo simulation to evaluate and compare the performance of three estimation methods applied to either Pearson or polychoric correlations: extended Bayesian information criterion graphical lasso with regularized edge estimates ("EBIC"), Bayesian information criterion model selection with partial correlation edge estimates ("BIC"), and multiple regression with p-value-based edge selection and partial correlation edge estimates ("MR"). We vary the number and distribution of thresholds, distribution of the underlying continuous data, sample size, model size, and network density, and we evaluate results in terms of model structure (sensitivity and false positive rate) and edge weight bias. Our results show that the effect of treating the data as ordinal versus continuous depends primarily on the number of levels in the data, and that estimation performance was affected by the sample size, the shape of the underlying distribution, and the symmetry of underlying thresholds. Furthermore, which estimation method is recommended depends on the research goals: MR methods tended to maximize sensitivity of edge detection, BIC approaches minimized false positives, and either one of these produced accurate edge weight estimates in sufficiently large samples. We identify some particularly difficult combinations of conditions for which no method produces stable results. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

NDEx, the Network Data Exchange (https://www.ndexbio.org) is a web-based resource where users can find, store, share and publish network models of any type and size. NDEx is integrated with Cytoscape, the widely used desktop application for network analysis and visualization. NDEx and Cytoscape are the pillars of the Cytoscape Ecosystem, a diverse environment of resources, tools, applications and services for network biology workflows. In this article, we introduce researchers to NDEx and highlight how it can simplify common tasks in network biology workflows as well as streamline publication and access to). Finally, we show how NDEx can be used programmatically via Python with the ndex2 client library, and point readers to additional examples for other popular programming languages such as JavaScript and R. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Getting started with NDEx Basic Protocol 2: Using NDEx and Cytoscape in a publication-oriented workflow Basic Protocol 3: Manipulating networks in NDEx via Python.

The analysis for estimating operator's activity and quantity of information in control rooms of underground coal mines is presented in this paper. Monitoring of operators in control rooms was conducted in mines Rembas, Soko, Ibar, Jasenovac and Stavalj. The network models are used during the analysis of operator's activity in control rooms. The result of the analysis is proposed critical path network model. The application of network models improves the quality and level of research, making it more efficient. Most of the analysed information are related to normal and alarm state data received from the mine shaft and operator's response to this data. Results are given according to actual scope of work depending on the work dynamics in underground coal mines, via histogram of changes in average number of activities per 1 h for 24-h period, then number of operator's activities per work hour and insight into the global view of technological process in the coal mine with focus on the work scope of mine workers. Analysis of operator's activities presented in this paper determines the relations between devices in control rooms and operators' capabilities in order to get better ergonomic design of control rooms. [ABSTRACT FROM AUTHOR]

We study multiple notions of Hilbert spaces of functions which, via the respective inner products, reproduce function values, or differences of function values. We do this by extending results from the more familiar settings of reproducing kernel Hilbert spaces, RKHSs. Our main results deal with operations on infinite graphs G = (V , E) of vertices and edges, and associated Hilbert spaces. For electrical network models, the differences f (x) − f (y) represent voltage differences for pairs of vertices x, y. In these cases, relative RKHSs will depend on choices of conductance functions c, where an appropriate function c is specified as a positive function defined on the edge-set E from G. Our present study of higher order differences, using choices of relative RKHSs, is motivated in part by existing numerical algorithms for discretization of PDEs. Our approach to higher order differences uses both combinatorial operations on graphs, and operator theory for the respective RKHSs. Starting with a graph G = (V , E) , we introduce an induced graph G ′ such that the vertices in G ′ are the edges in E from G, while the edges in G ′ are pairs of neighboring edges from G. [ABSTRACT FROM AUTHOR]

In various real-world networks, the set of nodes is partitioned in groups or types forming densely connected clusters commonly called ‘communities’. Several methods have been developed to find community structure in networks; one of them is based on modularity, a measure that represents the fraction of edges between nodes of the same type minus the expected fraction of such edges if they are established by a random process. Our work introduces a dynamic generation model of directed heterogeneous networks in which the set of nodes is partitioned in two groups. Networks based on our model grow by the addition of new nodes and the creation of new edges at each instant of time according to the combination of two mechanisms: (i) affinity-weighted preferential attachment and (ii) reciprocity response. We characterise the dynamics and limit values for the sum of the in- and out-degree of the nodes of the network which help us to determine the tail of the degree distributions (in and out) for each type of nodes. We show that both distributions follow a power law with equal scaling exponent. Using the limit values associated with the degree properties of our networks, we derive the dynamics and the asymptotic behaviour of the directed network modularity and establish conditions to guarantee the occurrence of communities according to the type of nodes. [ABSTRACT FROM AUTHOR]

Metapopulation models include spatial population dynamics such as dispersion and migration between subpopulations. Integral projection models (IPMs) can include demographic rates as a function of size. Traditionally, metapopulation models do not included detailed populaiton models such as IPMs. In some situations, both local population dynamics (e.g. size‐based survival) and spatial dynamics are important.We present a Python package, MetaIPM, which places IPMs into a metapopulation framework, and allow users to readily construct and apply these models that combine local population dynamics within a metapopulation framework.MetaIPM includes an IPM for each subpopulation that is connected to other subpopulations via a metapopulation movement model. These movements can include dispersion, migration or other patterns. The IPM can include for size‐specific demographic rates (e.g. survival, recruitment) as well as management actions, such as length‐based harvest (e.g. gear specific capture sizes, varying slot limits across political boundaries). The model also allows for changes in metapopulation connectivity between locations, such as a fish passage ladders to enhance movement or deterrents to reduce movement. Thus, resource managers can use MetaIPM to compare different management actions such as the harvest gear type (which can be length‐specific) and harvest locations.We demonstrate how MetaIPM may be applied to inform managers seeking to limit the spread of an invasive species in a system with important metapopulation dynamics. Specifically, we compared removal lengths (all length fish versus longer fish only) for an invasive fish population in a fragmented, inland river system. MetaIPM allowed users to compare the importance of harvesting source populations away from the invasion front, as well as species at the invasion front. The model would also allow for future comparisons of different deterrent placement locations in the system.Moving beyond our example system, we describe how MetaIPM can be applied to other species, systems and management approaches. The MetaIPM packages includes Jupyter Notebooks documenting the package as well as a second set of JupyterNotebooks showing the application of the package to our example system. [ABSTRACT FROM AUTHOR]

New mechanical philosophy posits that explanations in the life sciences involve the decomposition of a system into its entities and their respective activities and organization that are responsible for the explanandum phenomenon. This mechanistic account of explanation has proven problematic in its application to mathematical models, leading the mechanists to suggest different ways of aligning abstract models with the mechanist program. Initially, the discussion centered on whether the Hodgkin-Huxley model is explanatory. Network models provided another complication, as they apply to a wide number of materially diverse systems. In this article, we examine the various attempts to integrate abstract models within the mechanist program, also presenting a further challenge: the Heimburg-Jackson model, which was introduced as an alternative to the Hodgkin-Huxley model. We argue that although the notion of abstraction as the omission of irrelevant mechanistic details appears to give a mechanistic solution for accommodating abstract models, this notion does not suit models whose epistemic strategy is not decompositional. As a result, the mechanist has to choose whether to dilute the mechanistic approach nearly beyond recognition or to claim that many, if not most, abstract theoretical models do not deliver mechanistic explanations, or qualify as explanatory at all. [ABSTRACT FROM AUTHOR]

Non-pharmaceutical intervention measures, such as social distancing, have so far been the only means to slow the spread of SARS-CoV-2. In the United States, strict social distancing during the first wave of virus spread has resulted in different types of infection dynamics. In some states, such as New York, extensive infection spread was followed by a pronounced decline of infection levels. In other states, such as California, less infection spread occurred before strict social distancing, and a different pattern was observed. Instead of a pronounced infection decline, a long-lasting plateau is evident, characterized by similar daily new infection levels. Here we show that network models, in which individuals and their social contacts are explicitly tracked, can reproduce the plateau if network connections are cut due to social distancing measures. The reason is that in networks characterized by a 2D spatial structure, infection tends to spread quadratically with time, but as edges are randomly removed, the infection spreads along nearly one-dimensional infection "corridors", resulting in plateau dynamics. Further, we show that plateau dynamics are observed only if interventions start sufficiently early; late intervention leads to a "peak and decay" pattern. Interestingly, the plateau dynamics are predicted to eventually transition into an infection decline phase without any further increase in social distancing measures. Additionally, the models suggest that a second wave becomes significantly less pronounced if social distancing is only relaxed once the dynamics have transitioned to the decline phase. The network models analyzed here allow us to interpret and reconcile different infection dynamics during social distancing observed in various US states.

Abstract The fractal nature of complex networks has received a great deal of research interest in the last two decades. Similarly to geometric fractals, the fractality of networks can also be defined with the so-called box-covering method. A network is called fractal if the minimum number of boxes needed to cover the entire network follows a power-law relation with the size of the boxes. The fractality of networks has been associated with various network properties throughout the years, for example, disassortativity, repulsion between hubs, long-range-repulsive correlation, and small edge betweenness centralities. However, these assertions are usually based on tailor-made network models and on a small number of real networks, hence their ubiquity is often disputed. Since fractal networks have been shown to have important properties, such as robustness against intentional attacks, it is in dire need to uncover the underlying mechanisms causing fractality. Hence, the main goal of this work is to get a better understanding of the origins of fractality in complex networks. To this end, we systematically review the previous results on the relationship between various network characteristics and fractality. Moreover, we perform a comprehensive analysis of these relations on five network models and a large number of real-world networks originating from six domains. We clarify which characteristics are universally present in fractal networks and which features are just artifacts or coincidences.

Residual symptoms and stress are amongst the most reliable predictors of relapse in remitted depression. Standard methodologies often preclude continuous stress sampling or the evaluation of complex symptom interactions. This limits knowledge acquisition relative to the day-to-day interactions between residual symptoms and stress. The study aims to explore the interactions between physiological stress and residual symptoms network structure in remitted depression. Twenty-two individuals remitted from depression completed baseline, daily diary (DD), and post-DD assessments. Self-reported stress and residual symptoms were measured at baseline and post-DD. Daily diaries required participants to use a wearable electrodermal activity (EDA) device during waking hours and complete residual symptom measures twice daily for 3-weeks. Two-step multilevel vector auto-regression models were used to estimate contemporaneous and dynamic networks. Depressed mood and concentration problems were central across networks. Skin conductance responses (SCRs), suicide, appetite, and sleep problems were central in the temporal and energy loss in the contemporaneous network. Increased SCRs predicted decreased energy loss. Residual symptoms and stress showed bi-directional interactions. Overall, depressed mood and concentration problems were consistently central, thus potentially important intervention targets. Non-obtrusive bio-signal measures should be used to provide the clinical evidence-base for modelling the interactions between depressive residual symptoms and stress. Practical implications are discussed throughout related to focusing on symptom-specific interactions in clinical practice, simultaneously reducing residual symptom and stress occurrences, EDA as pioneering signal for stress detection, and the central role of specific residual symptoms in remitted depression.

The Gaussian graphical model (GGM) has recently grown popular in psychological research, with a large body of estimation methods being proposed and discussed across various fields of study, and several algorithms being identified and recommend as applicable to psychological data sets. Such highdimensional model estimation, however, is not trivial, and algorithms tend to perform differently in different settings. In addition, psychological research poses unique challenges, including placing a strong focus on weak edges (e.g., bridge edges), handling data measured on ordered scales, and relatively limited sample sizes. As a result, there is currently no consensus regarding which estimation procedure performs best in which setting. In this large-scale simulation study, we aimed to overcome this gap in the literature by comparing the performance of several estimation algorithms suitable for Gaussian and skewed ordered categorical data across a multitude of settings, as to arrive at concrete guidelines from applied researchers. In total, we investigated 60 different metrics across 564,000 simulated data sets. We summarized our findings through a platform that allows for manually exploring simulation results. Overall, we found that an exchange between discovery (e.g., sensitivity, edge weight correlation) and caution (e.g., specificity, precision) should always be expected, and achieving both-which is a requirement for perfect replicability-is difficult. Further, we identified that the estimation method is best chosen in light of each research question and have highlighted, alongside desirable asymptotic properties and low sample size discovery, results according to most common research questions in the field. [ABSTRACT FROM AUTHOR]

The relationship between topological explanation and mechanistic explanation is unclear. Most philosophers agree that at least some topological explanations are mechanistic explanations. The crucial question is how to make sense of this claim. Zednik (Philos Psychol 32(1):23–51, 2019, ) argues that topological explanations are mechanistic if they (i) describe mechanism sketches that (ii) pick out organizational properties of mechanisms. While we agree with Zednik’s conclusion, we critically discuss Zednik’s account and show that it fails as a general account of how and when topological explanations are mechanistic. First, if topological explanations were just mechanism sketches, this implies that they could be enriched by replacing topological terms with mechanistic detail. This, however, conflicts how topological explanations are used in scientific practice. Second, Zednik’s account fails to show how topological properties can be organizational properties of mechanisms that have a place in mechanistic explanation. The core issue is that Zednik’s account ignores that topological properties often are global properties while mechanistic explanantia refer to local properties. We demonstrate how these problems can be solved by a recent account of mechanistic completeness (Craver and Kaplan in Br J Philos Sci 71(1):287–319, 2020, ; Kohár and Krickel in Calzavarini and Viola (eds) Neural mechanisms—new challenges in the philosophy of neuroscience, Springer, New York, 2021, ) and use a multilayer network model of Alzheimer’s Disease to illustrate this. [ABSTRACT FROM AUTHOR]

Ever since Clausius in 1865 and Boltzmann in 1877, the concepts of entropy and of its maximization have been the foundations for predicting how material equilibria derive from microscopic properties. But, despite much work, there has been no equally satisfactory general variational principle for nonequilibrium situations. However, in 1980, a new avenue was opened by E.T. Jaynes and by Shore and Johnson. We review here maximum caliber, which is a maximum-entropy-like principle that can infer distributions of flows over pathways, given dynamical constraints. This approach is providing new insights, particularly into few-particle complex systems, such as gene circuits, protein conformational reaction coordinates, network traffic, bird flocking, cell motility, and neuronal firing.

Over the past few decades, surgeon training has changed dramatically. Surgical skills are now taught in a surgical skills laboratory instead of the operating room. Simulation-based training helps medical students improve their skills, but it has not revolutionized clinical education. One critical barrier to reaching such a desired goal is the lack of reliable, robust, and objective methods for assessing the effectiveness of training sessions and the development of students. In this paper, we will develop a new comparative analysis approach that employs network models as the central concept in establishing a new assessment tool for the evaluation of the surgical skills of trainees as well as the training processes. The model is populated using participants electromyography data while performing a simulation task. Furthermore, using NASA Task Load Index score, participants’ subjective overload levels are analyzed to examine the impact of participants’ perception of their mental demand, physical demand, temporal demand, performance, effort, and frustration on how participants perform each simulation task. Obtained results indicate that the proposed approach enables us to extract useful information from the raw data and provides an objective method for assessment the of surgical simulation tasks and how the participants’ perception of task impacts their performance.

Abstract Restructuring the power system with higher penetration of distributed energy resources (DERs) and intelligent devices offers the potential for more efficient, reliable, and better resource utilisation of power systems through the transactive energy framework (TEF). This article provides a general overview of the mathematical models and formulations of the TEF reported in the literature. TEF concepts can be applied to various levels of the power system. Here, the TEF‐related literature is divided into individual DER‐, building‐, microgrid‐, and macrogrid‐level TEF. The mathematical models of transactive agents corresponding to each level and power system network models are presented. Furthermore, TEF models for energy management and trading of integrated multi‐energy systems are analysed. Finally, the potential challenges and future research directions for transactive energy are discussed.

Abstract Researchers have paid a lot of attention to complex networks in recent decades. Due to their rapid evolution, they turn into a major scientific and innovative field. Several studies on complex networks are carried out, and other subjects are evolving every day such as the challenge of detecting influential nodes. In this study, we provide a brief overview of complex networks, as well as several concepts key related to measurements, the structure of complex network and social influence, an important state of the art on complex networks including basic metrics on complex networks, the evolution of their topology over the years as well as the dynamic of networks. A detailed literature about influential finding approaches is also provided to indicate their strength and shortcomings. We aim that our contribution of literature can be an interesting base of information for beginners’ scientists in this field. At the end of this paper, some conclusions are drawn and some future perspectives are mentioned to be studied as new directions in the future. More detailed references are provided to go further and deep in this area.

Many pathogens spread via environmental transmission, without requiring host-to-host direct contact. While models for environmental transmission exist, many are simply constructed intuitively with structures analogous to standard models for direct transmission. As model insights are generally sensitive to the underlying model assumptions, it is important that we are able understand the details and consequences of these assumptions. We construct a simple network model for an environmentally-transmitted pathogen and rigorously derive systems of ordinary differential equations (ODEs) based on different assumptions. We explore two key assumptions, namely homogeneity and independence, and demonstrate that relaxing these assumptions can lead to more accurate ODE approximations. We compare these ODE models to a stochastic implementation of the network model over a variety of parameters and network structures, demonstrating that with fewer restrictive assumptions we are able to achieve higher accuracy in our approximations and highlighting more precisely the errors produced by each assumption. We show that less restrictive assumptions lead to more complicated systems of ODEs and the potential for unstable solutions. Due to the rigour of our derivation, we are able to identify the reason behind these errors and propose potential resolutions.