With whom should I work? Partners' network characteristics and innovation persistence.

In studying the implications of collaboration networks on innovation persistence, previous research has primarily focused on the network characteristics of focal inventors, often overlooking those of their partners. The purpose of this study is to demonstrate what and how partners' network characteristics-specifically, network centrality and structural holes-affect the focal inventor's innovation persistence, by positing and testing the diversity and novelty of knowledge recombination as important mediators. We collected a panel patent dataset in the lithography industry between 2000 and 2023 and conducted data analysis using ordinary least squares (OLS) regression model, robustness test, and endogeneity test. Results indicate that partners' network centrality has an inverted U-shaped impact on innovation persistence, whereas partners' structural holes positively influence innovation persistence. The findings further show how the diversity and novelty of knowledge recombination mediate the relationships between partners' network characteristics and innovation persistence. This paper provides valuable insights for inventors, researchers, and policymakers, emphasizing the crucial role of partnerships and knowledge recombination in promoting innovation persistence.

Advice Network Centrality as a Social Origin of Task Crafting: The Bridging Roles of Basic Psychological Needs.

Little is known about the predictive role of advice networks in task crafting despite the growing academic and practical interest in its antecedents. Accordingly, as centrality in advice networks is expected to have a positive relationship with task crafting, this study develops a research model encompassing the mediating roles of the fulfillment of basic psychological needs to clarify this relationship. The model was tested using a sample composed of 198 employees from various firms in South Korea. The results showed that employees who occupy central positions in the advice network fulfilled their autonomy and competence needs, consequently engaging in task crafting. This study contributes to the literature on social networks, self-determination, and task crafting by discovering hidden antecedents and pivotal mechanisms in determining task crafting.

Diversity, integration, and variability of intergenerational relationships in old age: New insights from personal network research.

Relationships between family members from different generations have long been described as a source of solidarity and support in aging populations and, more recently, as a potential risk factor for COVID-19 contagion. Personal or egocentric network research offers a powerful kit of conceptual and methodological tools to study these relationships, but this has not yet been employed to its full potential in the literature. We investigate the heterogeneity, social integration, and individual correlates of intergenerational relationships in old age analyzing highly granular data on the personal networks of 230 older adults (2747 social ties) from a local survey in one of the areas of the world at the forefront of global aging trends (northern Italy). Using information on different layers in broad egocentric networks and on the structure of connectivity among the social contacts of aging people, we propose multiple conceptualizations and measures of intergenerational connectedness. Results show that intergenerational relationships are strongly integrated, but also highly diverse and variable, in older adults' social networks. Different types of intergenerational ties exist in different network layers, with various relational roles, degrees of tie strength, and patterns of association with individual and tie characteristics. We discuss how new and existing personal network data can be leveraged to consider novel questions and hypotheses about intergenerational relationships in contemporary aging families.

Evolution of system connectivity to support food production in the Indus Basin in Pakistan.

Sustainability challenges related to food production arise from multiple nature-society interactions occurring over long time periods. Traditional methods of quantitative analysis do not represent long-term changes in the networks of system components, including institutions and knowledge that affect system behavior. Here, we develop an approach to study system structure and evolution by combining a qualitative framework that represents sustainability-relevant human, technological, and environmental components, and their interactions, mediated by knowledge and institutions, with network modeling that enables quantitative metrics. We use this approach to examine the water and food system in the Punjab province of the Indus River Basin in Pakistan, exploring how food production has been sustained, despite high population growth, periodic floods, and frequent political and economic disruptions. Using network models of five periods spanning 75 y (1947 to 2022), we examine how quantitative metrics of network structure relate to observed sustainability-relevant outcomes and how potential interventions in the system affect these quantitative metrics. We find that the persistent centrality of some and evolving centrality of other key nodes, coupled with the increasing number and length of pathways connecting them, are associated with sustaining food production in the system over time. Our assessment of potential interventions shows that regulating groundwater pumping and phasing out fossil fuels alters network pathways, and helps identify potential vulnerabilities for future food production.

Altered brain network centrality in patients with orbital fracture: A resting­state functional MRI study.

The present study aimed to investigate potential functional network brain-activity abnormalities in individuals with orbital fracture (OF) using the voxel-wise degree centrality (DC) technique. The present study included 20 patients with OF (12 males and 8 females) and 20 healthy controls (HC; 12 males and 8 females), who were matched for gender, age and educational attainment. Functional magnetic resonance imaging (fMRI) in the resting state has been widely applied in several fields. Receiver operating characteristic (ROC) curves were calculated to distinguish between patients with OF and HCs. In addition, correlation analyses were performed between behavioral performance and average DC values in various locations. The DC technique was used to assess unprompted brain activity. Right cerebellum 9 region (Cerebelum_9_R) and left cerebellar peduncle 2 area (Cerebelum_Crus2_L) DC values of patients with OF were increased compared with those in HCs. Cerebelum_9_R and Cerebelum_Crus2_L had area under the ROC curve values of 0.983 and 1.000, respectively. Patients with OF appear to have several brain regions that exhibited aberrant brain network characteristics, which raises the possibility of neuropathic causes and offers novel therapeutic options.

Well-being is associated with cortical thickness network topology of human brain.

BACKGROUND: Living a happy and meaningful life is an eternal topic in positive psychology, which is crucial for individuals' physical and mental health as well as social functioning. Well-being can be subdivided into pleasure attainment related hedonic well-being or emotional well-being, and self-actualization related eudaimonic well-being or psychological well-being plus social well-being. Previous studies have mostly focused on human brain morphological and functional mechanisms underlying different dimensions of well-being, but no study explored brain network mechanisms of well-being, especially in terms of topological properties of human brain morphological similarity network. METHODS: Therefore, in the study, we collected 65 datasets including magnetic resonance imaging (MRI) and well-being data, and constructed human brain morphological network based on morphological distribution similarity of cortical thickness to explore the correlations between topological properties including network efficiency and centrality and different dimensions of well-being. RESULTS: We found emotional well-being was negatively correlated with betweenness centrality in the visual network but positively correlated with eigenvector centrality in the precentral sulcus, while the total score of well-being was positively correlated with local efficiency in the posterior cingulate cortex of cortical thickness network. CONCLUSIONS: Our findings demonstrated that different dimensions of well-being corresponded to different cortical hierarchies: hedonic well-being was involved in more preliminary cognitive processing stages including perceptual and attentional information processing, while hedonic and eudaimonic well-being might share common morphological similarity network mechanisms in the subsequent advanced cognitive processing stages.

Centrality Analysis of Protein-Protein Interaction Networks Using R.

Proteins are structural and functional components of cells. They interact with each other to drive specific cellular functions. The physical and functional protein interactions are an important feature of cellular organization and regulation. Protein interactions are represented as a network or a graph in which proteins are nodes, and interactions between them are edges. Perturbations in the network affecting essential or central proteins can have pathological consequences. Network or graph theory is a branch of mathematics that provides a conceptual framework to decipher topologically important proteins in the network. These concepts are known as centrality measures. This chapter introduces various centrality metrics and provides a stepwise protocol to quantify protein's strategic positions in the network using an R programming language.

Network centrality, support organizations, exploratory innovation: Empirical analysis of China's integrated circuit industry.

Exploratory innovation is critical to the breakthrough of core technologies in the integrated circuit (IC) industry, and cooperative innovation is a promising form of IC industry development. According to the viewpoint of social network, this paper constructs intercity networks of the IC industry by using a data set of cooperation patents from 2011 to 2020 in China. We uncover the evolution characteristics of the innovation networks, explore the relationship between network centrality and exploratory innovation in a city, and consider universities and development zones, named support organizations, as moderating variables. The results of the social network analysis (SNA) and dynamic panel system generalized method of moments model (System-GMM) are given as follows: Cities are increasingly inclined to collaborate with counterparts over time for innovation, but the overall network scale remains small. Beijing occupies core position in the networks. A cooperative innovation model driven by peripheral cities has been formed as the number of the peripheral cities has gradually increased. The network centrality of a city has a positive effect on its exploratory innovation. Both universities and development zones positively moderate the effect of network centrality on exploratory innovation. Based on the characteristics of the network, our study reveals the importance of taking the internal structure of the network and the node support environment into the same framework, which provides guidance for the innovative development of the world IC industry.

The effects of workplace friendship network centrality on deep acting.

We integrated social network theory with conservation of resource theory to predict that workplace friendship network centrality provides service employees with critical psychological resources that foster deep acting: positive affect and positive self-perception. In Study 1, we conducted a survey (N = 105) in a Korean banking firm, revealing that these resources mediate the relationship between workplace friendship network centrality and deep acting. Studies 2 and 3, both experimental studies, investigated the hypothesized causal relationships. In Study 2 (N = 151), we found that workplace friendship network centrality increases the intention toward deep acting. Further, Study 3 (N = 140) confirmed the direct effects of friendship network centrality on positive affect and self-perception. By providing insights into the structural antecedents of emotional labor, we inform managers in service organizations of the value of creating avenues for their employees to form and maintain friendships within the organization.

Genome entropy and network centrality contrast exploration and exploitation in evolution of foodborne pathogens.

Modelling evolution of foodborne pathogens is crucial for mitigation and prevention of outbreaks. We apply network-theoretic and information-theoretic methods to trace evolutionary pathways ofSalmonellaTyphimurium in New South Wales, Australia, by studying whole genome sequencing surveillance data over a five-year period which included several outbreaks. The study derives both undirected and directed genotype networks based on genetic proximity, and relates the network's structural property (centrality) to its functional property (prevalence). The centrality-prevalence space derived for the undirected network reveals a salient exploration-exploitation distinction across the pathogens, further quantified by the normalised Shannon entropy and the Fisher information of the corresponding shell genome. This distinction is also analysed by tracing the probability density along evolutionary paths in the centrality-prevalence space. We quantify the evolutionary pathways, and show that pathogens exploring the evolutionary search-space during the considered period begin to exploit their environment (their prevalence increases resulting in outbreaks), but eventually encounter a bottleneck formed by epidemic containment measures.

Network Biology Analyses and Dynamic Modeling of Gene Regulatory Networks under Drought Stress Reveal Major Transcriptional Regulators in Arabidopsis.

Drought is one of the most serious abiotic stressors in the environment, restricting agricultural production by reducing plant growth, development, and productivity. To investigate such a complex and multifaceted stressor and its effects on plants, a systems biology-based approach is necessitated, entailing the generation of co-expression networks, identification of high-priority transcription factors (TFs), dynamic mathematical modeling, and computational simulations. Here, we studied a high-resolution drought transcriptome of Arabidopsis. We identified distinct temporal transcriptional signatures and demonstrated the involvement of specific biological pathways. Generation of a large-scale co-expression network followed by network centrality analyses identified 117 TFs that possess critical properties of hubs, bottlenecks, and high clustering coefficient nodes. Dynamic transcriptional regulatory modeling of integrated TF targets and transcriptome datasets uncovered major transcriptional events during the course of drought stress. Mathematical transcriptional simulations allowed us to ascertain the activation status of major TFs, as well as the transcriptional intensity and amplitude of their target genes. Finally, we validated our predictions by providing experimental evidence of gene expression under drought stress for a set of four TFs and their major target genes using qRT-PCR. Taken together, we provided a systems-level perspective on the dynamic transcriptional regulation during drought stress in Arabidopsis and uncovered numerous novel TFs that could potentially be used in future genetic crop engineering programs.

Metabolic switch in the aging astrocyte supported via integrative approach comprising network and transcriptome analyses.

Dysregulated central-energy metabolism is a hallmark of brain aging. Supplying enough energy for neurotransmission relies on the neuron-astrocyte metabolic network. To identify genes contributing to age-associated brain functional decline, we formulated an approach to analyze the metabolic network by integrating flux, network structure and transcriptomic databases of neurotransmission and aging. Our findings support that during brain aging: (1) The astrocyte undergoes a metabolic switch from aerobic glycolysis to oxidative phosphorylation, decreasing lactate supply to the neuron, while the neuron suffers intrinsic energetic deficit by downregulation of Krebs cycle genes, including mdh1 and mdh2 (Malate-Aspartate Shuttle); (2) Branched-chain amino acid degradation genes were downregulated, identifying dld as a central regulator; (3) Ketone body synthesis increases in the neuron, while the astrocyte increases their utilization, in line with neuronal energy deficit in favor of astrocytes. We identified candidates for preclinical studies targeting energy metabolism to prevent age-associated cognitive decline.

Interpretable Drug-to-Drug Network Features for Predicting Adverse Drug Reactions.

Recent years have witnessed booming data on drugs and their associated adverse drug reactions (ADRs). It was reported that these ADRs have resulted in a high hospitalisation rate worldwide. Therefore, a tremendous amount of research has been carried out to predict ADRs in the early phases of drug development, with the goal of reducing possible future risks. The pre-clinical and clinical phases of drug research can be time-consuming and cost-ineffective, so academics are looking forward to more extensive data mining and machine learning methods to be applied in this field of study. In this paper, we try to construct a drug-to-drug network based on non-clinical data sources. The network presents underlying relationships between drug pairs according to their common ADRs. Then, multiple node-level and graph-level network features are extracted from this network, e.g., weighted degree centrality, weighted PageRanks, etc. After concatenating the network features to the original drug features, they were fed into seven machine learning models, e.g., logistic regression, random forest, support vector machine, etc., and were compared to the baseline, where there were no network-based features considered. These experiments indicate that all the tested machine-learning methods would benefit from adding these network features. Among all these models, logistic regression (LR) had the highest mean AUROC score (82.1%) across all ADRs tested. Weighted degree centrality and weighted PageRanks were identified to be the most critical network features in the LR classifier. These pieces of evidence strongly indicate that the network approach can be vital in future ADR prediction, and this network-based approach could also be applied to other health informatics datasets.

Computational prediction of protein folding rate using structural parameters and network centrality measures.

Protein folding is a complex physicochemical process whereby a polymer of amino acids samples numerous conformations in its unfolded state before settling on an essentially unique native three-dimensional (3D) structure. To understand this process, several theoretical studies have used a set of 3D structures, identified different structural parameters, and analyzed their relationships using the natural logarithmic protein folding rate (ln(kf)). Unfortunately, these structural parameters are specific to a small set of proteins that are not capable of accurately predicting ln(kf) for both two-state (TS) and non-two-state (NTS) proteins. To overcome the limitations of the statistical approach, a few machine learning (ML)-based models have been proposed using limited training data. However, none of these methods can explain plausible folding mechanisms. In this study, we evaluated the predictive capabilities of ten different ML algorithms using eight different structural parameters and five different network centrality measures based on newly constructed datasets. In comparison to the other nine regressors, support vector machine was found to be the most appropriate for predicting ln(kf) with mean absolute differences of 1.856, 1.55, and 1.745 for the TS, NTS, and combined datasets, respectively. Furthermore, combining structural parameters and network centrality measures improves the prediction performance compared to individual parameters, indicating that multiple factors are involved in the folding process.

An effective distance-based centrality approach for exploring the centrality of maritime shipping network.

Centrality has always been used in transportation networks to estimate the status and importance of a node in the networks, especially in the shipping networks. However, most of the studies only take the shipping network as an unweighted network or only considering the tie weights in the weighted networks, ignoring the truth that both the number of ties and tie weights contribute to the centrality in weighted shipping networks. Therefore, we proposed a new method combining both the number of ties and tie weights to assess the node centrality based on effective distance by integrating the studies of Opsahl et al., (2010) and Du et al., (2015). An empirical analysis of shipping network at the country level for the 21st-centrtury Maritime Silk Road (MSR) was performed. The result of correlation analysis between country's degree centrality and the Liner Shipping Connectivity Index (LSCI) published by the United Nations Conference on Trade and Development (UNCTAD) proved the superiority of our method compared to the traditional centrality metrics. In weighted networks, both the number of ties the tie weights should be considered by adjusting the parameters. The method proposed in this study can also be used to nodes' status and importance estimation of various networks in other fields.

ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19.

Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the current pandemic of COVID-19 disease in global health issues. It is highly desirable to identify potential human drug targets for COVID-19 using a machine learning approach since it saves time and labor compared to traditional experimental methods. Structure-based drug discovery where druggability is determined by molecular docking is only appropriate for the protein whose three-dimensional structures are available. With machine learning algorithms, differentiating relevant features for predicting targets and non-targets can be used for the proteins whose 3-D structures are unavailable. In this research, a Machine Learning-based Drug Target Discovery (ML-DTD) approach is proposed where a machine learning model is initially built up and tested on the curated dataset consisting of COVID-19 human drug targets and non-targets formed by using the Therapeutic Target Database (TTD) and human interactome using several classifiers like XGBBoost Classifier, AdaBoost Classifier, Logistic Regression, Support Vector Classification, Decision Tree Classifier, Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbour Classifier (KNN). In this method, protein features include Gene Set Enrichment Analysis (GSEA) ranking, properties derived from the protein sequence, and encoded protein network centrality-based measures. Among all these, XGBBoost, KNN, and Random Forest models are satisfactory and consistent. This model is further used to predict novel COVID-19 human drug targets, which are further validated by target pathway analysis, the emergence of allied repurposed drugs, and their subsequent docking study.

Individual Dietary Specialization in a Generalist Bee Varies across Populations but Has No Effect on the Richness of Associated Microbial Communities.

AbstractDespite the increasingly documented occurrence of individual specialization, the relationship between individual consumer interactions and diet-related microbial communities in wild populations is still unclear. Using data from nests of Ceratina australensis from three different wild bee populations, we combine metabarcoding and network approaches to explore the existence of individual variation in resource use within and across populations and whether dietary specialization affects the richness of pollen-associated microbes. We reveal the existence of marked dietary specialization. In the most specialized population, we also show that individuals' diet breadth was positively related to the richness of fungi but not bacteria. Overall, individual specialization appeared to have a weak or negligible effect on the microbial richness of nests, suggesting that different mechanisms beyond environmental transmission may be at play regarding microbial acquisition in wild bees.

A link model approach to identify congestion hotspots.

Congestion emerges when high demand peaks put transportation systems under stress. Understanding the interplay between the spatial organization of demand, the route choices of citizens and the underlying infrastructures is thus crucial to locate congestion hotspots and mitigate the delay. Here we develop a model where links are responsible for the processing of vehicles, which can be solved analytically before and after the onset of congestion, and provide insights into the global and local congestion. We apply our method to synthetic and real transportation networks, observing a strong agreement between the analytical solutions and the Monte Carlo simulations, and a reasonable agreement with the travel times observed in 12 cities under congested phase. Our framework can incorporate any type of routing extracted from real trajectory data to provide a more detailed description of congestion phenomena, and could be used to dynamically adapt the capacity of road segments according to the flow of vehicles, or reduce congestion through hotspot pricing.

Generalist diets allows opportunism behavior to engage in cleaning interactions among birds and large mammals

Cleaning interactions are interspecific associations in which cleaners  benefit from the hosts by feeding on parasites, injured tissues, or blood. All around the globe there is a remarkable diversity of birds that behave as cleaners of large mammals. Here we investigated the drivers shaping the organization of networks formed by cleaning birds and host mammals. We used two cleaner-host networks, one from Brazilian openlands and the other one from African openlands, to explore the relationship between diet generalism and cleaning behavior. We hypothesize that cleaning interactions are often opportunistic and, as a consequence, we expect that generalist species are the main components of cleaner-host networks. We first contrast the diet diversity of cleaner species with their closed related species. For 18 of 26 bird families, cleaners show higher diet diversity than closely-related, non-cleaning species.  Then we explored if birds with higher diversity diets are the central species of the cleaner-host networks. The results show that there is no apparent correlation between species centrality in the networks and their diet diversity. We suggest that generalism allows opportunist species to engage in cleaning interactions, but the importance of a cleaner species is affected by other attributes, such as abundance and behavioral traits associated with cleaning behavior. In a broader perspective, these results suggest that the factors that may allow species to participate in ecological networks are not the same that modulate their role in the same networks.

Interações de limpeza são associações interespecíficas em que as espécies limpadoras se beneficiam das espécies hospedeiras ao se alimentarem de parasitas, pele machucada ou sangue. Por todo o mundo existe uma notável diversidade de aves que se comportam como limpadoras de grandes mamíferos, mas ainda são interações pouco xploradas. Assim, nós investigamos os aspectos que moldam a organização das redes de interações de limpeza, formadas entre aves limpadoras e grandes mamíferos.  Nós utilizamos duas redes limpador-hospedeiro, uma das planíces do Brasil e outra das planíces Africanas, para explorar a relação entre a dieta generalista e o comportamento de limpeza. Nossa principal hipótese é que as espécies limpadoras exibem um comportamento oportunista, e por isso, esperaríamos que as espécies com uma ampla diversidade de dieta são também aquelas mais centrais nas redes de interação de limpeza entre aves e mamíferos. Nós primeiro comparamos a diversidade de dieta das espécies limpadoras com a diversidade de dieta das aves da mesma família. Em 18 das 26 famílias de aves analisadas, limpadores demonstraram uma maior diversidade de dieta do que espécies da mesma família que não participam das interações de limpeza. Então, nós exploramos o papel topológico dentro das redes das espécies de aves limpadoras. Analisamos se as espécies limpadoras com maior diversidade de dieta eram também as espécies centrais das redes de interações de limpeza. Os resultados demonstram que não existe uma correlação aparente entre a centralidade das espécies e a sua diversidade de dieta. Sugerimos que o generalismo de dieta permite que espécies oportunistas engajem em interações de limpeza, mas a importância das espécies limpadoras na rede é afetada por outros atributos, tais como a abundância e carácteres comportamentais associados com o comportamento de limpeza. Em uma perspectiva masi ampla, esses resultados sugerem que os fatores que podem permitir que espécies participem de redes ecológicas não são os mesmos que modulam o papel dessas espécies nessas redes.  

Hidden effects of habitat restoration on the persistence of pollination networks.

Past and recent studies have focused on the effects of global change drivers such as species invasions on species extinction. However, as we enter the United Nations Decade of Ecosystem Restoration the aim must switch to understanding how invasive-species management affects the persistence of the remaining species in a community. Focusing on plant-pollinator interactions, we test how species persistence is affected by restoration via the removal of invasive plant species. Restoration had a clear positive effect on plant persistence, whereas there was no difference between across treatments for pollinator persistence in the early season, but a clear effect in late season, with higher persistence in unrestored sites. Network structure affected only pollinator persistence, while centrality had a strong positive effect on both plants and pollinators. Our results suggest a hidden effect of invasive plants-although they may compete with native plant species, invasive plants may provide important resources for pollinators, at least in the short term.