Network Analysis Framework Research Articles

DISTINCT FUNCTIONAL CEREBRAL HYPERSENSITIVITY NETWORKS DURING INCISIONAL AND INFLAMMATORY PAIN IN RATS

Although the pathophysiology of pain has been investigated tremendously, there are still many open questions with regard to specific pain entities and their pain-related symptoms. To increase the translational impact of (preclinical) animal neuroimaging pain studies, the use of disease-specific pain models, as well as relevant stimulus modalities, are critical. We developed a comprehensive framework for brain network analysis combining functional magnetic resonance imaging (MRI) with graph-theory (GT) and data classification by linear discriminant analysis. This enabled us to expand our knowledge of stimulus modalities processing under incisional (INC) and pathogen-induced inflammatory (CFA) pain entities compared to acute pain conditions. GT-analysis has uncovered specific features in pain modality processing that align well with those previously identified in humans. These include areas such as S1, M1, CPu, HC, piriform, and cingulate cortex. Additionally, we have identified unique Network Signatures of Pain Hypersensitivity (NSPH) for INC and CFA. This leads to a diminished ability to differentiate between stimulus modalities in both pain models compared to control conditions, while also enhancing aversion processing and descending pain modulation. Our findings further show that different pain entities modulate sensory input through distinct NSPHs. These neuroimaging signatures are an important step toward identifying novel cerebral pain biomarkers for certain diseases and relevant outcomes to evaluate target engagement of novel therapeutic and diagnostic options, which ultimately can be translated to the clinic.

Production network structure, specialization and unemployment: Measuring the structural resilience of national economies

Specialization leads to efficient production structures that generate welfare gains, while the resulting interconnectedness of countries spreads shocks more rapidly, making the global economic system more volatile. This paper applies the framework of ecological network analysis to input–output data and captures resilient economic structures between self-sufficiency (redundancy) and extreme specialization (efficiency). Calculating the structural resilience of 69 countries shows that economies are more redundant than ecological systems and moved towards more efficient structures over time. By regressing economic openness on structural characteristics, we find that economically more open economies on the export side are more efficient and less structurally redundant. This reflects that specialization and integration into global supply chains go hand in hand. We also find a significant empirical relationship between structural resilience and changes in unemployment. This finding suggests that measuring the structural resilience of national production networks helps capture the robustness of economic systems.

Analysis of Factors Affecting the Spatial Association Network of Food Security Level in China

Food security serves as the cornerstone of national security, intricately linked to social stability and economic progress. Currently, with the swift evolutions in social economy, logistics and transport, information dissemination, and technological advancements, there has been a marked increase in the cross-regional flow of food production, distribution, and consumption. Consequently, the spatial interdependence of food security across different regions has grown increasingly salient. This paper investigates the spatial interrelationship of food security levels in China through a network analysis framework, examining its determinants and network dynamics. The findings offer valuable insights for decision-makers aiming to optimize agricultural resource allocation and enhance national food security levels. This research establishes a comprehensive evaluation index system for assessing food security levels in China across four dimensions: production security, distribution security, supply security, and consumption security. Employing data from 30 provinces between 2008 and 2022, the entropy method quantifies food security levels, while a modified gravity model underpins the construction of a spatial association network. This framework subsequently examines the network’s structural characteristics and the factors influencing its formation. The results reveal that: (1) China’s food security levels demonstrate a consistent upward trajectory over the study period, though significant regional disparities persist. The central region surpasses the national average, while the eastern and western regions lag. Recently, the western region has shown accelerated improvements in food security, followed by the central area, with the eastern region maintaining steady growth. (2) A structurally robust spatial correlation network of food security has emerged, characterized by variations in the number of network relationships, fluctuations in network density, and a decline in network efficiency while still exhibiting pronounced small-world characteristics. (3) The network displays a clear core-periphery structure, with Shanghai, Beijing, and Jiangsu positioned centrally, playing pivotal intermediary roles, whereas remote provinces such as Gansu, Ningxia, and Liaoning occupy the periphery. (4) The four major regions demonstrate sparse internal connectivity yet robust inter-regional ties, resulting in pronounced spillover effects. (5) Various factors, including geographic distance, provincial proximity, disparities in economic development levels, variations in marketization, differences in agricultural human capital, and disparities in land productivity, significantly impact the establishment of spatial correlations in food security. The affirmative influences of geographic distance and neighboring relations, along with the beneficial shifts in economic development disparities, suggest that the flow of technology and resources plays a crucial role in reinforcing spatial connections.

Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study.

Most studies assessing the impact of online media and social media use on COVID-19 vaccine hesitancy predominantly rely on survey data, which often fail to capture the clustering of health opinions and behaviors within real-world networks. In contrast, research using social network analysis aims to uncover the diverse communities and discourse themes related to vaccine support and hesitancy within social media platforms. Despite these advancements, there is a gap in the literature on how a person's social circle affects vaccine acceptance, wherein an important part of social influence stems from offline interactions. We aimed to examine how online media consumption influences vaccination decisions within real-world social networks by analyzing unique quantitative network data collected from Romania, an Eastern European state and member of the European Union. We conducted 83 face-to-face interviews with participants from a living lab in Lerești, a small rural community in Romania, using a personal network analysis framework. This approach involved gathering data on both the respondents and individuals within their social circles (referred to as alters). After excluding cases with missing data, our analysis proceeded with 73% (61/83) of the complete personal networks. To examine the hierarchical structure of alters nested within ego networks, we used a mixed multilevel logistic regression model with random intercepts. The model aimed to predict vaccination status among alters, with the focal independent variable being the respondents' preferred source of health and prevention information. This variable was categorized into 3 types: traditional media, online media (including social media), and a combination of both, with traditional media as the reference category. In this study, we analyzed 61 personal networks, encompassing between 15 and 25 alters each, totaling 1280 alters with valid data across all variables of interest. Our primary findings indicate that alters within personal networks, whose respondents rely solely on online media for health information, exhibit lower vaccination rates (odds ratio [OR] 0.37, 95% CI 0.15-0.92; P=.03). Conversely, the transition from exclusive traditional media use to a combination of both traditional and online media does not significantly impact vaccination rate odds (OR 0.75, 95% CI 0.32-1.78; P=.52). In addition, our analysis revealed that alters in personal networks of respondents who received the vaccine are more likely to have received the vaccine themselves (OR 3.75, 95% CI 1.79-7.85; P<.001). Real-world networks combine diverse human interactions and attributes along with consequences on health opinions and behaviors. As individuals' vaccination status is influenced by how their social alters use online media and vaccination behavior, further insights are needed to create tailored communication campaigns and interventions regarding vaccination in areas with low levels of digital health literacy and vaccination rates, as Romania exposes.

Leveraging Network Insights into Positive Emotions and Resilience for Better Life Satisfaction

Introduction/Objective Within a network analysis framework, this study explored the relationships between positive emotions, resilience, and life satisfaction. Positive emotions and resilience are critical to enhancing psychological well-being, but their complex interplay requires further investigation, particularly in a Greek adult population. Methods We conducted a cross-sectional study with 1,230 Greek adults (67.6% females, 32.4% males), using a network analysis to assess the relationships among positive emotions, resilience, and life satisfaction. Data were collected through the Scale of Positive and Negative Experience (SPANE-8), the Brief Resilience Scale (BRS), and the Satisfaction with Life Scale (SWLS). Network structures were constructed using the graphical LASSO technique for partial correlation analysis, and Bayesian networks were applied to generate Directed Acyclic Graphs (DAGs) to identify directional pathways. Centrality metrics were used to determine critical variables in the network. Results Happiness, pleasure, and contentment are examples of positive emotions that are central nodes in the network that are highly correlated with higher levels of life satisfaction. Resilience, primarily as a recovery mechanism from stress, showed weaker direct associations with life satisfaction. The DAG revealed that positive emotions significantly influenced life satisfaction, suggesting their pivotal role in improving well-being. Conclusion The findings underscore the importance of positive emotions in enhancing life satisfaction, suggesting that interventions targeting emotional well-being may be more effective than those focused solely on resilience. These insights offer a foundation for developing psychological interventions to improve life satisfaction.

Alterations in orbitofrontal cortex communication relate to suicidal attempts in patients with major depressive disorder

BackgroundInvestigating how the interaction between the orbitofrontal cortex (OFC) and various brain regions/functional networks in major depressive disorder (MDD) patients with a history of suicide attempt (SA) holds importance for understanding the neurobiology of this population. MethodsWe employed resting-state functional magnetic resonance imaging (rs-fMRI) to analyze the OFC's functional segregation in 586 healthy individuals. A network analysis framework was then applied to rs-fMRI data from 86 MDD-SA patients and 85 MDD-Control patients, utilizing seed mappings of OFC subregions and a multi-connectivity-indicator strategy involving cross-correlation, total interdependencies, Granger causality, and machine learning. ResultsFour functional subregions of left and right OFC, were designated as seed regions of interest. Relative to the MDD-Control group, the MDD-SA group exhibited enhanced functional connectivity (FC) and attenuated interaction between the OFC and the sensorimotor network, imbalanced communication between the OFC and the default mode network, enhanced FC and interaction between the OFC and the ventral attention network, enhanced interaction between the OFC and the salience network, and attenuated FC between the OFC and the frontoparietal network. LimitationsThe medication and treatment condition of patients with MDD was not controlled, so the medication effect on the alteration model cannot be affirmed. ConclusionThe findings suggest an imbalanced interaction pattern between the OFC subregions and a set of cognition- and emotion-related functional networks/regions in the MDD-SA group.

Why “free maternal healthcare” is not entirely free in Ghana: a qualitative exploration of the role of street-level bureaucratic power

BackgroundGhana introduced a free maternal healthcare policy within its National Health Insurance Scheme (NHIS) in 2008 to remove financial barriers to accessing maternal health services. Despite this policy, evidence suggests that women incur substantial out-of-pocket (OOP) payments for maternal health care. This study explores the underlying reasons for these persistent out-of-pocket payments within the context of Ghana’s free maternal healthcare policy.MethodsCross-sectional qualitative data were collected through interviews with a purposive sample of 14 mothers and 8 healthcare providers/administrators in two regions of Ghana between May and September 2022. All interviews were audio-recorded, transcribed and imported into the NVivo 14.0 software for analysis. An iteratively developed codebook guided the coding process. Our thematic data analysis followed the Attride-Sterling framework for network analysis, identifying basic, organising themes and global themes.ResultsWe found that health systems and demand-side factors are responsible for the persistence of OOP payments despite the existence of the free maternal healthcare policy in Ghana. Reasons for these payments arose from health systems factors, particularly, NHIS structural issues – delayed and insufficient reimbursements, inadequate NHIS benefit coverage, stockouts and supply chain challenges and demand-side factors – mothers’ lack of education about the NHIS benefit package, and passing of cost onto patients. Due to structural and system level challenges, healthcare providers, exercising their street-level bureaucratic power, have partly repackaged the policy, enabling the persistence of out-of-pocket payments for maternal healthcare.ConclusionsUrgent measures are required to address the structural and administrative issues confronting Ghana’s free maternal health policy; otherwise, Ghana may not achieve the sustainable development goals targets on maternal and child health.

Evil and allies: Opportunistic gulls as both spreaders and sentinels of antibiotic‐resistant bacteria in human‐transformed landscapes

Abstract Human‐transformed residuals, especially those derived from human waste (dumps), farmland, and livestock are involved in the emergence of antibiotic‐resistant bacteria (ARB) in the environment. Wildlife can act as vectors of ARB dispersal through different environments, but also as sentinels to detect the early spread and determine ARB sources. The development of integrated monitoring programmes focused on wildlife would help to anticipate the risks of ARB to humans and livestock. We used the yellow‐legged gull (Larus michahellis) as a model species to investigate and monitor the spatial patterns of ARB dispersal across an extensive farmland region located in northeastern Spain (Lleida). By integrating GPS tracking data and ARB clinical testing for 26 individuals within a network analysis framework, we modelled the risk of spatial pathogen spread through faeces during the bacteria‐transmission latency period (16 days after sample collection). Additionally, we created a connectivity network to determine the main sources of ARB in the area, focusing on three main habitats of special risk for infection: dumps, livestock facilities, and irrigation ponds. Seven individuals were infected by Escherichia coli, with one also co‐infected with Listeria monocytogenes and Salmonella spp. Potential pathogen dispersal distances ranged from 1.13 km to 23.13 km from the breeding colony. Our network analyses revealed 54 main nodes (i.e. high‐risk habitats recurrently visited by tracked gulls) and 1182 links among them. Our findings revealed a high degree of connectivity between the breeding area, located in a shallow lake, and nearby dumps, highlighting them as significant contributors to ARB dispersal. Synthesis and applications: The integration of GPS data, pathogen testing and network analyses can shed further light on pathogen dynamics by creating spatial risk maps and identifying ARB sources. In combination with complementary molecular epidemiology techniques within a One Health framework, our approach can emerge as an important tool for monitoring ARB dynamics within highly human‐transformed ecosystems. This may empower managers for the development of targeted ARB monitoring programmes and effective mitigation strategies, ultimately improving both animal and public health.

Navigating median and extreme volatility in stock markets: Implications for portfolio strategies

This study explores the interdependencies among developed stock markets using the LASSO technique with quantile regression within a network analysis framework. Traditional forecasting methods often fail during volatile market conditions, necessitating innovative approaches that blend interconnectedness and factor modeling. By employing quantile regression, which examines financial assets across various distribution quantiles, this study addresses tail risk, a critical aspect of market behavior during crises. The network analysis framework provides insights into the relationships between stock markets, highlighting how variables interact within a complex system. The study assesses market behaviors at different quantile levels, considering clustering coefficients to analyze cycles, middlemen, ins, and outs. Additionally, it examines the impact of several factors on market interconnectedness, offering insights into the interplay of individual stocks with broader market conditions. Key findings demonstrate that incorporating interconnectedness factors into forecasting models enhances accuracy and informs better decision-making, leading to portfolios that can withstand extreme market conditions and provide superior risk-adjusted returns.

A novel hierarchical network-based approach to unveil the complexity of functional microbial genome

Biological networks serve a crucial role in elucidating intricate biological processes. While interspecies environmental interactions have been extensively studied, the exploration of gene interactions within species, particularly among individual microorganisms, is less developed. The increasing amount of microbiome genomic data necessitates a more nuanced analysis of microbial genome structures and functions. In this context, we introduce a complex structure using higher-order network theory, “Solid Motif Structures (SMS)”, via a hierarchical biological network analysis of genomes within the same genus, effectively linking microbial genome structure with its function. Leveraging 162 high-quality genomes of Microcystis, a key freshwater cyanobacterium within microbial ecosystems, we established a genome structure network. Employing deep learning techniques, such as adaptive graph encoder, we uncovered 27 critical functional subnetworks and their associated SMSs. Incorporating metagenomic data from seven geographically distinct lakes, we conducted an investigation into Microcystis’ functional stability under varying environmental conditions, unveiling unique functional interaction models for each lake. Our work compiles these insights into an extensive resource repository, providing novel perspectives on the functional dynamics within Microcystis. This research offers a hierarchical network analysis framework for understanding interactions between microbial genome structures and functions within the same genus.

Contrastive voxel clustering for multiscale modeling of brain network

Resting-state functional magnetic resonance imaging (fMRI) provides an efficient way to analyze the functional connectivity between brain regions. A comprehensive understanding of brain functionality requires a unified description of multi-scale layers of neural structure. However, existing brain network modeling methods often simplify this property by averaging Blood oxygen level dependent (BOLD) signals at the brain region level for fMRI-based analysis with the assumption that BOLD signals are homogeneous within each brain region, which ignores the heterogeneity of voxels within each Region of Interest (ROI). This study introduces a novel multi-stage self-supervised learning framework for multiscale brain network analysis, which effectively delineates brain functionality from voxel to ROIs and up to sample level. A Contrastive Voxel Clustering (CVC) module is proposed to simultaneously learn the voxel-level features and clustering assignments, which ensures the retention of informative clustering features at the finest voxel-level and concurrently preserves functional connectivity characteristics. Additionally, based on the extracted features and clustering assignments at the voxel level by CVC, a Brain ROI-based Graph Neural Network (BR-GNN) is built to extract functional connectivity features at the brain ROI-level and used for sample-level prediction, which integrates the functional clustering maps with the pre-established structural ROI maps and creates a more comprehensive and effective analytical tool. Experiments are performed on two datasets, which illustrate the effectiveness and generalization ability of the proposed method by analyzing voxel-level clustering results and brain ROIs-level functional characteristics. The proposed method provides a multiscale modeling framework for brain functional connectivity analysis, which will be further used for other brain disease identification. Code is available at https://github.com/yanliugroup/fmri-cvc.

A Note on Ising Network Analysis with Missing Data

The Ising model has become a popular psychometric model for analyzing item response data. The statistical inference of the Ising model is typically carried out via a pseudo-likelihood, as the standard likelihood approach suffers from a high computational cost when there are many variables (i.e., items). Unfortunately, the presence of missing values can hinder the use of pseudo-likelihood, and a listwise deletion approach for missing data treatment may introduce a substantial bias into the estimation and sometimes yield misleading interpretations. This paper proposes a conditional Bayesian framework for Ising network analysis with missing data, which integrates a pseudo-likelihood approach with iterative data imputation. An asymptotic theory is established for the method. Furthermore, a computationally efficient Pólya–Gamma data augmentation procedure is proposed to streamline the sampling of model parameters. The method’s performance is shown through simulations and a real-world application to data on major depressive and generalized anxiety disorders from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC).

Complex Governance Network Analysis Between Brazilian Public Policies

Objective: The objective of this article is to create a Complex Governance Network Analysis Framework between two public policies. To prove the predictions of this study, the Theoretical Framework proposed here was applied between the National School Feeding Program (PNAE) and the School Health Program (PSE) in two locations. Theoretical Framework: To discuss the objective of this article, the approaches of Complex Governance Network (Morçöl, 2023) and Individual and Social Learning (Habermas, 1984, 1987; Lankester, 2013; Maarleveld and Dangbenonpag, 1999) were used. Method: This research is of a qualitative-descriptive nature and abductive logic, using as a strategy a study of multiple embedded cases and collection of primary and secondary data. Results and Discussion: By proposing a complex governance network analysis framework between two public policies, this study made a theoretical contribution by adding more dimensions to this theoretical lens. The study of learning contributes to understanding the cognitive evolution of the population, including adult learning, a responsible individual active in public policies. Research Implications: As practical contributions, this research helps in monitoring and structuring strategies in public management. Because, by understanding the learning level of each public servant, it is possible to observe the creativity of each actor involved in solving problems. Originality/Value: The study of learning contributes to understanding the cognitive evolution of the population, including adult learning, a responsible individual active in public policies.

Is Self-Reported Obstructive Sleep Apnea Associated with Cardiac Distress? A Network Analysis

Abstract Introduction: The relationship between obstructive sleep apnea (OSA), obesity, various metabolic variables, and psychosocial outcomes is complex. No studies have examined the association between these predictors and disease-specific distress related to heart disease (cardiac distress). We aimed to study the association between OSA and cardiac distress using a network analysis framework. Methods: This secondary analysis of an observational cross-sectional study conducted in 2021 consisted of 405 hospital- and community-sourced adults from Australia and the United States who reported an acute coronary event (such as a myocardial infarction, or procedures such as coronary artery bypass graft surgery, or percutaneous coronary intervention) in the previous 12 months. Participants were surveyed in relation to sociodemographic variables, clinical risk factors, comorbidities (including time since event, OSA, obesity, diabetes, hypertension, and hyperlipidemia), and cardiac distress (reported by the Cardiac Distress Inventory Short-Form). These data were subjected to bootstrapped exploratory graph analysis (EGA), which identifies the dimensions of variables that cluster together. Variables that contributed to the EGA dimensions were used to predict cardiac distress using multivariable logistic regression. Results: Three distinct dimensions were identified by the EGA: Dimension 1 – clinical risk factors and conditions including OSA, Dimension 2 – variables related to the heart event, and Dimension 3 – variables closely related to cardiac distress. For Dimension 1, only OSA was a significant predictor of cardiac distress in the fully adjusted model (adjusted odds ratio = 2.08, 95% confidence interval = 1.02–4.25, P = 0.044). Further analysis indicated that OSA was associated with physical challenges and changes in roles and relationships. Conclusions: This study identified that self-reported OSA is associated with cardiac distress, particularly distress that was associated with physical challenges and changes to roles and relationships. These findings imply that OSA could potentially increase stress in a relationship; however, distress was only assessed from the perspective of the participant with OSA in this study. EGA is a useful method for describing complex associations between diverse predictor variables such as OSA and cardiac distress. Owing to the self-reported aspect of the data, further investigation to confirm study outcomes is warranted.

Meta-Analytic Gene-Clustering Algorithm for Integrating Multi-Omics and Multi-Study Data.

Gene pathways and gene-regulatory networks are used to describe the causal relationship between genes, based on biological experiments. However, many genes are still to be studied to define novel pathways. To address this, a gene-clustering algorithm has been used to group correlated genes together, based on the similarity of their gene expression level. The existing methods cluster genes based on only one type of omics data, which ignores the information from other types. A large sample size is required to achieve an accurate clustering structure for thousands of genes, which can be challenging due to the cost of multi-omics data. Meta-analysis has been used to aggregate the data from multiple studies and improve the analysis results. We propose a computationally efficient meta-analytic gene-clustering algorithm that combines multi-omics datasets from multiple studies, using the fixed effects linear models and a modified weighted correlation network analysis framework. The simulation study shows that the proposed method outperforms existing single omic-based clustering approaches when multi-omics data and/or multiple studies are available. A real data example demonstrates that our meta-analytic method outperforms single-study based methods.

BrainDAS: Structure-aware domain adaptation network for multi-site brain network analysis

In the medical field, datasets are mostly integrated across sites due to difficult data acquisition and insufficient data at a single site. The domain shift problem caused by the heterogeneous distribution among multi-site data makes autism spectrum disorder (ASD) hard to identify. Recently, domain adaptation has received considerable attention as a promising solution. However, domain adaptation on graph data like brain networks has not been fully studied. It faces two major challenges: (1) complex graph structure; and (2) multiple source domains. To overcome the issues, we propose an end-to-end structure-aware domain adaptation framework for brain network analysis (BrainDAS) using resting-state functional magnetic resonance imaging (rs-fMRI). The proposed approach contains two stages: supervision-guided multi-site graph domain adaptation with dynamic kernel generation and graph classification with attention-based graph pooling. We evaluate our BrainDAS on a public dataset provided by Autism Brain Imaging Data Exchange (ABIDE) which includes 871 subjects from 17 different sites, surpassing state-of-the-art algorithms in several different evaluation settings. Furthermore, our promising results demonstrate the interpretability and generalization of the proposed method. Our code is available at https://github.com/songruoxian/BrainDAS.

The rise of syndicates: Social network analyses of construction waste haulers in Hong Kong using a novel InfoMap-XGBoost method

In areas with a low entry barrier, small businesses tend to organize into informal syndicates to survive or thrive. However, there is little empirical evidence to show how these syndicates evolve, and ultimately, impact business and environment. This research investigates syndicates evolving among construction waste haulers in Hong Kong and their broad impacts. It does so by applying a novel social network analysis (SNA) framework named InfoMap-XGBoost to a big data set covering over 10 million payload trips conducted by waste haulers over the past 10 years. Our analyses show that (a) syndicates spontaneously emerge as the whole social network expands by merging with each other; (b) occupying a higher centrality within the syndicate is more significant than joining a larger syndicate in helping actors receive more hauling orders with bigger values; (c) larger and denser syndicates help alleviate adverse environmental impact by improving the overall business efficiency.

Identifying Candidate Polyphenols Beneficial for Oxidative Liver Injury through Multiscale Network Analysis.

Oxidative stress, a driver of liver pathology, remains a challenge in clinical management, necessitating innovative approaches. In this research, we delved into the therapeutic potential of polyphenols for oxidative liver injury using a multiscale network analysis framework. From the Phenol-Explorer database, we curated a list of polyphenols along with their corresponding PubChem IDs. Verified target information was then collated from multiple databases. We subsequently measured the propagative effects of these compounds and prioritized a ranking based on their correlation scores for oxidative liver injury. This result underwent evaluation to discern its effectiveness in differentiating between known and unknown polyphenols, demonstrating superior performance over chance level in distinguishing these compounds. We found that lariciresinol and isopimpinellin yielded high correlation scores in relation to oxidative liver injury without reported evidence. By analyzing the impact on a multiscale network, we found that lariciresinol and isopimpinellin were predicted to offer beneficial effects on the disease by directly acting on targets such as CASP3, NR1I2, and CYP3A4 or by modulating biological functions related to the apoptotic process and oxidative stress. This study not only corroborates the efficacy of identified polyphenols in liver health but also opens avenues for future investigations into their mechanistic actions.

Proof of concept for quantitative adverse outcome pathway modeling of chronic toxicity in repeated exposure

Adverse Outcome Pathway (AOP) is a useful tool to glean mode of action (MOE) of a chemical. However, in order to use it for the purpose of risk assessment, an AOP needs to be quantified using in vitro or in vivo data. Majority of quantitative AOPs developed so far, were for single exposure to progressively higher doses. Limited attempts were made to include time in the modeling. Here as a proof-of concept, we developed a hypothetical AOP, and quantified it using a virtual dataset for six repeated exposures using a Bayesian Network Analysis (BN) framework. The virtual data was generated using realistic assumptions. Effects of each exposure were analyzed separately using a static BN model and analyzed in combination using a dynamic BN (DBN) model. Our work shows that the DBN model can be used to calculate the probability of adverse outcome when other upstream KEs were observed earlier. These probabilities can help in identification of early indicators of AO. In addition, we also developed a data driven AOP pruning technique using a lasso-based subset selection, and show that the causal structure of AOP is itself dynamic and changes over time. This proof-of-concept study revealed the possibility for expanding the applicability of the AOP framework to incorporate biological dynamism in toxicity appearance by repeated insults.

How do inventors overcome the relational inertia after inter‐firm mobility?

This study aims to explore strategies for overcoming relational inertia and enhancing their creativity after inter‐firm mobility of inventors. The research sample comprises 591 mobile inventors identified using a patent data‐based model from Chinese communication technology companies. Employing the framework of dynamic network analysis, this study examines the impact of network closure at different time on overcoming relational inertia and investigates the influence of knowledge structure. The findings suggest that a lower level of past and current network closure is more conducive to knowledge sharing and technological innovation after inter‐firm mobility of inventors. Furthermore, mobile inventors with broad and in‐depth knowledge structures demonstrate significantly improved knowledge sharing and technological innovation, effectively mitigating relational inertia. These conclusions offer a systematic research perspective and comprehensive theoretical guidance, emphasizing the importance of optimizing network resources and knowledge structures to help inventors overcome relational inertia after inter‐firm mobility.