Validation of the Centrality of Religiosity Scale for the Portuguese Population.

The main aim of this study was to validate and adapt the Centrality of Religiosity Scale to the Portuguese population. A total of 1018 subjects participated in this study. The metric qualities demonstrated in the analyses suggested that the factor structure was based on five dimensions identical to those proposed by its authors. After analysing its psychometric qualities, we concluded that this instrument can be applied to the Portuguese population and is a valuable tool in studies related to the psychology of religion and spirituality.

Community connectivity and local heterogeneity explain animal species co-occurrences within pond communities.

Metacommunity processes have the potential to determine most features of the community structure. However, species diversity has been the dominant focus of studies. Nestedness, modularity and checkerboard distribution of species occurrences are main components of biodiversity organisation. Within communities, these patterns emerge from the interaction between functional diversity, spatial heterogeneity and resource availability. Additionally, the connectivity determines the pool of species for community assembly and, eventually, the pattern of species co-occurrence within communities. Despite the recognised theoretical expectations, the change in occurrence patterns within communities along ecological gradients has seldom been considered. Here, we analyse the spatial occurrence of animal species along sampling units within 18 temporary ponds and its relationship with pond environments and geographic isolation. Isolated ponds presented a nested organisation of species with low spatial segregation-modularity and checkerboard-and the opposite was found for communities with high connectivity. A pattern putatively explained by high functional diversity in ponds with large connectivity and heterogeneity, which determines that species composition tracks changes in microhabitats. On the contrary, nestedness is promoted in dispersal-limited communities with low functional diversity, where microhabitat filters mainly affect richness without spatial replacement between functional groups. Vegetation biomass promotes nestedness, probably due to the observed increase in spatial variance in biomass with the mean biomass. Similarly, the richness of vegetation reduced the spatial segregation of animals within communities. This result may be due to the high plant diversity of the pond that is observed similarly along all sampling units, which promotes the spatial co-occurrence of species at this scale. In the study system, the spatial arrangement of species within communities is related to local drivers as heterogeneity and metacommunity processes by means of dispersal between communities. Patterns of species co-occurrence are interrelated with community biodiversity and species interactions, and consequently with most functional and structural properties of communities. These results indicate that understanding the interplay between metacommunity processes and co-occurrence patterns is probably more important than previously thought to understand biodiversity assembly and functioning.

Los procesos metacomunitarios tienen el potencial de determinar la mayoría de las características de la estructura de las comunidades. Sin embargo, los trabajos se han enfocado principalmente en los patrones de diversidad de especies. El anidamiento, la modularidad y la distribución en damero de la ocurrencia espacial de las especies son propiedades básicas de las comunidades. Estos patrones surgen de la interacción entre la diversidad funcional, la heterogeneidad espacial y la disponibilidad de recursos dentro de las comunidades. Además, el pool de especies disponibles para el ensamblaje está determinado por la conectividad de la comunidad, afectando así su patrón de co­ocurrencia de especies. A pesar de las reconocidas expectativas teóricas, el cambio en los patrones de ocurrencia dentro de las comunidades a lo largo de gradientes ecológicos ha sido poco considerado. Aquí, analizamos la ocurrencia espacial de especies animales dentro de 18 charcos temporales y su relación con las características ambientales y el aislamiento geográfico de los charcos. Los charcos aislados presentaron alto anidamiento espacial mientras que los charcos de alta conectividad una distribución de ocurrencias modular y en damero. Por un lado, la baja diversidad funcional en charcos aislados, determinaría que los filtros microambientales afecten la riqueza de especies sin reemplazo espacial entre grupos funcionales, promoviendo un arreglo anidado de ocurrencias. Por otro lado, la alta diversidad funcional en charcos con alta conectividad y heterogeneidad permitiría el reemplazo espacial de especies en gradientes microambientales, determinando los patrones de segregación observados. La biomasa vegetal promueve el anidamiento, probablemente debido al aumento observado en la variación espacial de la biomasa con la biomasa media. La riqueza vegetal también redujo la segregación espacial de los animales dentro de las comunidades. Este resultado puede deberse a que la alta diversidad de plantas de los charcos es también observada a nivel de unidades muestreales, favoreciendo esto la coexistencia espacial de especies. El arreglo espacial de especies dentro de las comunidades estudiadas estaría determinado tanto por factores locales como la heterogeneidad, como por procesos regionales operando a través de la dispersión de individuos entre comunidades. Los patrones de co­ocurrencia de especies están interrelacionados con la diversidad comunitaria y las interacciones bióticas, y consecuentemente con la mayoría de las propiedades estructurales y funcionales de las comunidades. Este estudio evidencia la importancia de la conexión entre procesos metacomunitarios y la co­ocurrencia espacial de especies para comprender el ensamblaje y funcionamiento de la biodiversidad.

Does the limelight foster the green transition of listed industrial companies? An empirical analysis from Chinese A-share market.

This paper contributes current research by investigating the extent to which equity linkage networks impact enterprise green transition in sustainable development. By adopting a complex network approach, we constructed a common shareholding network based on the top ten shareholders of listed industrial enterprises in Shanghai and Shenzhen A-shares from 2013 to 2022. The empirical results indicate that enterprises closer to the centre of the equity linkage network tend to have higher degrees of green transition. This impact is facilitated through three mechanism channels of capital flow, information exchange, and knowledge transfer within the network. We find enterprises at the core of the network can effectively reduce their financing constraints on enterprises, mitigate risks associated with external environmental uncertainties and managerial myopia, and promote knowledge exchange and innovation cooperation between enterprises. We have also discovered that the centrality of equity network has a greater impact on promoting transition in state-owned, large-scale enterprises, and enterprises with less heavy pollution and the network effect can be enhanced by strong regional environmental regulations. The above findings not only provide policy makers with policy recommendations to guide the enterprises green transition, but also provide industry practitioners with practical paths and directions, which can help promote the green development process of the whole society.

Alterations in the hub structure of whole-brain functional networks in patients with drug-naïve schizophrenia: Insights from electroencephalography-based research.

Aim: This study aimed to identify atypical hubs in the whole-brain networks of patients with schizophrenia (SZ) and examine the effects of antipsychotic medications, using electroencephalography (EEG) data. Methods: We estimated the functional connectivity across all electrodes by applying the phase lag index to the EEG signals of 21 drug-naïve patients with SZ and 31 age-matched healthy controls. Betweenness centrality (BC), a measure of hub status, was calculated for each electrode and frequency band. Data from 14 patients were re-evaluated after initiating treatment with antipsychotic medications. Results: BC values decreased significantly at the Fz site in the beta band, decreased significantly at Pz in the gamma band, and increased significantly at O1 in the gamma band among patients with SZ. These changes persisted after antipsychotic treatment and were unrelated to clinical symptoms. Conclusion: The abnormal hub topology we observed, especially in the high-frequency band, may reflect the pathophysiology of SZ, and this study highlights the utility of BC analysis of EEG data for detecting alterations in the whole-brain networks of patients with SZ.

Structural and functional neural patterns among sub-threshold bulimia nervosa: Abnormalities in dorsolateral prefrontal cortex and orbitofrontal cortex.

BACKGROUND: Disordered eating behaviors are prevalent among youngsters and highly associated with dysfunction in neurocognitive systems. We aimed to identify the potential changes in individuals with bulimia symptoms (sub-BN) to generate insights to understand developmental pathophysiology of bulimia nervosa. METHODS: We investigated group differences in terms of degree centrality (DC) and gray matter volume (GMV) among 145 undergraduates with bulimia symptoms and 140 matched control undergraduates, with the secondary analysis of the whole brain connectivity in these regions of interest showing differences in static functional connectivity (FC). RESULTS: The sub-BN group exhibited abnormalities of the right dorsolateral prefrontal cortex and right orbitofrontal cortex in both GMV and DC, and displayed decreased FC between these regions and the precuneus. We also observed that sub-BN presented with reduced FC between the calcarine and superior temporal gyrus, middle temporal gyrus and inferior parietal gyrus. Additionally, brain-behavioral associations suggest a distinct relationship between these FCs and psychopathological symptoms in sub-BN group. CONCLUSIONS: Our study demonstrated that individuals with bulimia symptoms present with aberrant neural patterns that mainly involved in cognitive control and reward processing, as well as attentional and self-referential processing, which could provide important insights into the pathology of BN.

Social control is associated with increased reproductive skew in a wild mammal.

In group-living species, reproductive variation among individuals of the same sex is widespread. By identifying the mechanisms underlying this reproductive skew, we gain fundamental insights into the evolution and maintenance of sociality. A common mechanism, social control, is typically studied by quantifying dominance, which is one of many attributes of sociality that describes how individuals exert influence on others and is an incomprehensive measure of social control as it accounts only for direct relationships. Here, we use the global reaching centrality (GRC), which quantifies the degree of hierarchy in a social network by accounting for both direct and indirect social relationships. Using a wild, free-living population of adult female yellow-bellied marmots (Marmota flaviventris), we found a positive relationship between the reproductive skew index and GRC: more despotic social groups have higher reproductive skew. The GRC was stronger predictor for skew than traditional measures of social control (i.e. dominance). This allows deeper insights into the diverse ways individuals control other group members' reproduction, a core component in the evolution of sociality. Future studies of skew across taxa may profit by using more comprehensive, network-based measures of social control.

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.

Seizure onset zone (SOZ) identification using effective brain connectivity of epileptogenic networks.

Objective. To demonstrate the capability of utilizing graph feature-based supervised machine learning (ML) algorithm on intracranial electroencephalogram recordings for the identification of seizure onset zones (SOZs) in individuals with drug-resistant epilepsy.Approach. Utilizing three model-free measures of effective connectivity (EC)-directed information, mutual information-guided Granger causality index (MI-GCI), and frequency-domain convergent cross-mapping (FD-CCM) - directed graphs are generated. Graph centrality measures at different sparsity are used as the classifier's features.Main results. The centrality features achieve high accuracies exceeding 90% in distinguishing SOZ electrodes from non-SOZ electrodes. Notably, a sparse graph representation with just ten features and simple ML models effectively achieves such performance. The study identifies FD-CCM centrality measures as particularly significant, with a mean AUC of 0.93, outperforming prior literature. The FD-CCM-based graph modeling also highlights elevated centrality measures among SOZ electrodes, emphasizing heightened activity relative to non-SOZ electrodes during ictogenesis.Significance. This research not only underscores the efficacy of automated SOZ identification but also illuminates the potential of specific EC measures in enhancing discriminative power within the context of epilepsy research.

Functional network centrality indicates interactions between APOE4 and age across the clinical spectrum of AD.

Advanced age is the most important risk factor for Alzheimer's disease (AD), and carrier-status of the Apolipoprotein E4 (APOE4) allele is the strongest known genetic risk factor. Many studies have consistently shown a link between APOE4 and synaptic dysfunction, possibly reflecting pathologically accelerated biological aging in persons at risk for AD. To test the hypothesis that distinct functional connectivity patterns characterize APOE4 carriers across the clinical spectrum of AD, we investigated 128 resting state functional Magnetic Resonance Imaging (fMRI) datasets from the Alzheimer's Disease Neuroimaging Initiative database (ADNI), representing all disease stages from cognitive normal to clinical dementia. Brain region centralities within functional networks, computed as eigenvector centrality, were tested for multivariate associations with chronological age, APOE4 carrier status and clinical stage (as well as their interactions) by partial least square analysis (PLSC). By PLSC analysis two distinct brain activity patterns could be identified, which reflected interactive effects of age, APOE4 and clinical disease stage. A first component including sensorimotor regions and parietal regions correlated with age and AD clinical stage (p < 0.001). A second component focused on medial-frontal regions and was specifically related to the interaction between age and APOE4 (p = 0.032). Our findings are consistent with earlier reports on altered network connectivity in APOE4 carriers. Results of our study highlight promise of graph-theory based network centrality to identify brain connectivity linked to genetic risk, clinical stage and age. Our data suggest the existence of brain network activity patterns that characterize APOE4 carriers across clinical stages of AD.

Resting-state brain plasticity is associated with the severity in cervical spondylotic myelopathy.

OBJECTIVE: To investigate the brain mechanism of non-correspondence between imaging presentations and clinical symptoms in cervical spondylotic myelopathy (CSM) patients and to test the utility of brain imaging biomarkers for predicting prognosis of CSM. METHODS: Forty patients with CSM (22 mild-moderate CSM, 18 severe CSM) and 25 healthy controls (HCs) were recruited for rs-fMRI and cervical spinal cord diffusion tensor imaging (DTI) scans. DTI at the spinal cord (level C2/3) with fractional anisotropy (FA) and degree centrality (DC) were recorded. Then one-way analysis of covariance (ANCOVA) was conducted to detect the group differences in the DC and FA values across the three groups. Pearson correlation analysis was then separately performed between JOA with FA and DC. RESULTS: Among them, degree centrality value of left middle temporal gyrus exhibited a progressive increase in CSM groups compared with HCs, the DC value in severe CSM group was higher compared with mild-moderate CSM group. (P < 0.05), and the DC values of the right superior temporal gyrus and precuneus showed a decrease after increase. Among them, DC values in the area of precuneus in severe CSM group were significantly lower than those in mild-moderate CSM and HCs. (P < 0.05). The fractional anisotropy (FA) values of the level C2/3 showed a progressive decrease in different clinical stages, that severe CSM group was the lowest, significantly lower than those in mild-moderate CSM and HCs (P < 0.05). There was negative correlation between DC value of left middle temporal gyrus and JOA scores (P < 0.001), and the FA values of dorsal column in the level C2/3 positively correlated with the JOA scores (P < 0.001). CONCLUSION: Structural and functional changes have taken place in the cervical spinal cord and brain of CSM patients. The Brain reorganization plays an important role in maintaining the symptoms and signs of CSM, aberrant DC values in the left middle temporal gyrus may be the possible mechanism of inconsistency between imaging findings and clinical symptoms. Degree centrality is a potentially useful prognostic functional biomarker in cervical spondylotic myelopathy.

Link Prediction in Complex Networks Using Average Centrality-Based Similarity Score.

Link prediction plays a crucial role in identifying future connections within complex networks, facilitating the analysis of network evolution across various domains such as biological networks, social networks, recommender systems, and more. Researchers have proposed various centrality measures, such as degree, clustering coefficient, betweenness, and closeness centralities, to compute similarity scores for predicting links in these networks. These centrality measures leverage both the local and global information of nodes within the network. In this study, we present a novel approach to link prediction using similarity score by utilizing average centrality measures based on local and global centralities, namely Similarity based on Average Degree (SACD), Similarity based on Average Betweenness (SACB), Similarity based on Average Closeness (SACC), and Similarity based on Average Clustering Coefficient (SACCC). Our approach involved determining centrality scores for each node, calculating the average centrality for the entire graph, and deriving similarity scores through common neighbors. We then applied centrality scores to these common neighbors and identified nodes with above average centrality. To evaluate our approach, we compared proposed measures with existing local similarity-based link prediction measures, including common neighbors, the Jaccard coefficient, Adamic-Adar, resource allocation, preferential attachment, as well as recent measures like common neighbor and the Centrality-based Parameterized Algorithm (CCPA), and keyword network link prediction (KNLP). We conducted experiments on four real-world datasets. The proposed similarity scores based on average centralities demonstrate significant improvements. We observed an average enhancement of 24% in terms of Area Under the Receiver Operating Characteristic (AUROC) compared to existing local similarity measures, and a 31% improvement over recent measures. Furthermore, we witnessed an average improvement of 49% and 51% in the Area Under Precision-Recall (AUPR) compared to existing and recent measures. Our comprehensive experiments highlight the superior performance of the proposed method.

Unveiling Influence in Networks: A Novel Centrality Metric and Comparative Analysis through Graph-Based Models.

Identifying influential actors within social networks is pivotal for optimizing information flow and mitigating the spread of both rumors and viruses. Several methods have emerged to pinpoint these influential entities in networks, represented as graphs. In these graphs, nodes correspond to individuals and edges indicate their connections. This study focuses on centrality measures, prized for their straightforwardness and effectiveness. We divide structural centrality into two categories: local, considering a node's immediate vicinity, and global, accounting for overarching path structures. Some techniques blend both centralities to highlight nodes influential at both micro and macro levels. Our paper presents a novel centrality measure, accentuating node degree and incorporating the network's broader features, especially paths of different lengths. Through Spearman and Pearson correlations tested on seven standard datasets, our method proves its merit against traditional centrality measures. Additionally, we employ the susceptible-infected-recovered (SIR) model, portraying virus spread, to further validate our approach. The ultimate influential node is gauged by its capacity to infect the most nodes during the SIR model's progression. Our results indicate a notable correlative efficacy across various real-world networks relative to other centrality metrics.

Degree centrality-based resting-state functional magnetic resonance imaging explores central mechanisms in lumbar disc herniation patients with chronic low back pain.

Objective: To investigate the central mechanism of lumbar disc herniation in patients with chronic low back pain (LDHCP) using resting-state functional magnetic resonance imaging (rs-fMRI) utilizing the Degree Centrality (DC) method. Methods: Twenty-five LDHCP and twenty-two healthy controls (HCs) were enrolled, and rs-fMRI data from their brains were collected. We compared whole-brain DC values between the LDHCP and HC groups, and examined correlations between DC values within the LDHCP group and the Visual Analogue Score (VAS), Oswestry Dysfunction Index (ODI), and disease duration. Diagnostic efficacy was evaluated using receiver operating characteristic (ROC) curve analysis. Results: LDHCP patients exhibited increased DC values in the bilateral cerebellum and brainstem, whereas decreased DC values were noted in the left middle temporal gyrus and right post-central gyrus when compared with HCs. The DC values of the left middle temporal gyrus were positively correlated with VAS (r = 0.416, p = 0.039) and ODI (r = 0.405, p = 0.045), whereas there was no correlation with disease duration (p > 0.05). Other brain regions showed no significant correlations with VAS, ODI, or disease duration (p > 0.05). Furthermore, the results obtained from ROC curve analysis demonstrated that the Area Under the Curve (AUC) for the left middle temporal gyrus was 0.929. Conclusion: The findings indicated local abnormalities in spontaneous neural activity and functional connectivity in the bilateral cerebellum, bilateral brainstem, left middle temporal gyrus, and right postcentral gyrus among LDHCP patients.

Predicting associations between CircRNA and diseases through structure-aware graph transformer and path-integral convolution.

A series of biological experiments has demonstrated that circular RNAs play a crucial regulatory role in cellular processes and may be potentially associated with diseases. Uncovering these connections helps in understanding potential disease mechanisms and advancing the development of treatment strategies. However, in biology, traditional experiments face limitations in terms of efficiency and cost, especially when enumerating possible associations. To address these limitations, several computational methods have been proposed, but existing methods only measure from a nodal perspective and cannot capture structural similarities between edges. In this study, we introduce an advanced computational method called SATPIC2CD for analyzing potential associations between circular RNAs and diseases. Specifically, we first employ an Structure-Aware Graph Transformer (SAT), which extracts five predefined metapath representations before calculating attention. This adaptive network integrates structural information into the original self-attention by aggregating information within and between paths. Subsequently, we use Path Integral Convolutional Networks (PACN) to integrate feature information for all path weights between two nodes. Afterward, we complement the network node features with feature loss and feature smoothing using Gated Recurrent Units (GRU) and node centrality. Finally, a Multi-Layer Perceptron (MLP) is employed to obtain the ultimate prediction scores for each circular RNA-disease pair. SATPIC2CD performs remarkably well, with an accuracy of up to 0.9715 measured by the Area Under the Curve (AUC) in a 5-fold cross-validation, surpassing other comparative models. Case studies further emphasize the high precision of our method in identifying circular RNA-disease associations, laying a solid foundation for guiding future biological research efforts.

The neural compensation phenomenon in schizophrenia with mild attention deficits during working memory task.

BACKGROUND: Working memory (WM) and attention are essential cognitive processes, and their interplay is critical for efficient information processing. Schizophrenia often exhibits deficits in both WM and attention, contributing to function impairments. This study aims to investigate the neural mechanisms underlying the relationship between WM impairments and attention deficits in schizophrenia. METHODS: We assessed the functional-MRI scans of the 184 schizophrenias with different attention deficits (mild=133; severe=51) and 146 controls during an N-back WM task. We explored their whole-brain functional connectome profile by adopting the voxel-wise degree centrality (DC). Linear analysis was conducted to explore the associations among attention deficit severity, altered DC, and WM performance in patients. RESULTS: We observed that all patients showed decreased DC in the pre-supplementary area (pre-SMA), and posterior cerebellum compared to the controls, and schizophrenia patients with mild attention deficits showed decreased DC in the supramarginal gyrus, insula, and precuneus compared with the other 2 groups. DC values of the detected brain regions displayed U-shaped or inverted U-shaped curves, rather than a linear pattern, in response to increasing attention deficits. The linear analysis indicated that altered DC of the pre-SMA can modulate the relationship between attention deficits and WM performance. CONCLUSION: The U-shaped or inverted U-shaped pattern in response to increasing attention deficits may reflect a compensation mechanism in schizophrenia with mild attention deficits. This notion is also supported by the linear analysis that schizophrenia patients with mild attention deficits can improve their WM performance by increasing the DC value of the pre-SMA.

Does identity as a drinker predict problem recognition motivation in harmful drinkers?

Harmful drinking is associated with significant negative health and social outcomes, but drinkers are reticent to recognise personal drinking problems, hindering natural recovery or help-seeking. Recent evidence suggests that social identity as a drinker is associated with various drinking-related factors but has not been examined in relation to likelihood of problem recognition. In a group of ninety-six harmful drinkers (61 females, M age = 34 years) we explored how identity components associated with ingroup self-investment and ingroup self-definition in combination with implicit identity as a drinker accounted for degrees of problem recognition. In addition to demographic information, addiction experience and drinking behaviour (AUDIT), respondents completed measures of ingroup self-investment (identity centrality, solidarity, and satisfaction), ingroup self-definition (ingroup homogeneity and self-stereotyping), a "self as drinker" identity implicit association test and problem recognition (four items from the Stages of Change Readiness and Treatment Eagerness Scale). After controlling for possible covariates (age, gender and alcohol addiction experience) increased problem recognition was accounted for by explicit and not implicit identity components. More specifically, increasing perceived chronic saliency of one's drinker identity (self-investment in the drinker ingroup) and not an implicit association between the self and being a drinker was related to increased likelihood of problem recognition. This suggests that how chronically and explicitly accessible the identity of the drinker is for individuals might operate to stimulate the willingness or motivation to recognise potential drinking related harm.

Related metacognitions, desire thinking and identity differentially predict compulsion and withdrawal symptoms in problematic Instagram use.

Work has identified that metacognitive thought results in desire-based thinking and perpetuates the magnitude and severity of maladaptive behaviour including problematic social media use, and also that one's ingroup identity is related to increasing problematic behaviour. No evidence has ascertained the relative contribution of these as related differential factors in the experience of problematic social media use. The current study explored the comparative importance of components of desire thinking, positive and negative metacognitions and dimensions of ingroup identity on degree of problematic use among 147 current Instagram users. Results showed that for predicting general problematic Instagram use negative metacognitive beliefs and the verbal perseverance component of desire-based thinking were significant. Importantly, however, different factors appeared to be important for predicting distinct aspects of problematic Instagram. For compulsivity indicators, negative metacognitions and verbal perseveration were essential, whereas for the withdrawal component identity centrality (and no other dimensions of identity) and imaginal prefiguration emerge as the sole independent predictors.

Effects of Food Depictions in Entertainment Media on Children's Unhealthy Food Preferences: Content Analysis Linked With Panel Data.

Background: Entertainment media content is often mentioned as one of the roots of children's unhealthy food consumption. This might be due to the high quantity of unhealthy foods presented in children's media environments. However, less is known about the role of the centrality of food placement, that is, whether foods are interacted with, consumed, verbally mentioned, or appear unobtrusively. We also lack longitudinal research measuring both children's unhealthy and healthy food consumption behaviors as outcomes. Objective: The aim is to connect content analytical data based on children's actual media diet with panel data in order to explain children's food preferences. Moreover, this study not only focuses on the amount of healthy and unhealthy foods children are exposed to, but also on how these foods are presented (ie, centrally or not). Furthermore, we looked at the question of how parental coviewing can diminish (or enhance) the effects of unhealthy (or healthy) food depictions, and we measured healthy and unhealthy consumption as dependent variables. Methods: We conducted a 2-wave panel study with children and one of their parents (of 2250 parents contacted, 829 responded, for a response rate of 36.84%; 648 valid cases, ie, parent-child pairs, were used for analysis), with 6 months between the 2 panel waves. We linked the 2-wave panel data for the children and their parents to content analytical data for movies (n=113) and TV series (n=134; 3 randomly chosen episodes per TV series were used) that children were exposed to over the course of 6 months. Results: There was no significant relationship between exposure to unhealthy food presentation and unhealthy (b=0.008; P=.07) or healthy (b=-0.003; P=.57) food consumption over time. Also, healthy food presentation was unrelated to unhealthy (b=0.009; P=.18) or healthy (b=0.000; P=.99) food consumption over time. However, there was a significant, positive interaction between unhealthy food presentation and presentation centrality on unhealthy food consumption (b=0.000; P=.03), suggesting that the effects of unhealthy food presentation rise with increasing levels of centrality. There was no interaction between unhealthy food presentation and presentation centrality on the consumption of healthy foods (b=0.000; P=.10). Also, exposure to healthy food presentation interacted with centrality (b=-0.001; P=.003). That is, when a healthy product was presented at maximum centrality, it led to less unhealthy food consumption in children. Coviewing did not interact with exposure to unhealthy foods when explaining unhealthy (b=0.003; P=.08) or healthy (b=-0.001; P=.70) food consumption. Conclusions: We conclude that simply presenting more healthy foods is not sufficient to combat children's unhealthy food preferences. Further regulations may be necessary with respect to representations of unhealthy foods in children's media.

A generalized eigenvector centrality for multilayer networks with inter-layer constraints on adjacent node importance.

We present a novel approach for computing a variant of eigenvector centrality for multilayer networks with inter-layer constraints on node importance. Specifically, we consider a multilayer network defined by multiple edge-weighted, potentially directed, graphs over the same set of nodes with each graph representing one layer of the network and no inter-layer edges. As in the standard eigenvector centrality construction, the importance of each node in a given layer is based on the weighted sum of the importance of adjacent nodes in that same layer. Unlike standard eigenvector centrality, we assume that the adjacency relationship and the importance of adjacent nodes may be based on distinct layers. Importantly, this type of centrality constraint is only partially supported by existing frameworks for multilayer eigenvector centrality that use edges between nodes in different layers to capture inter-layer dependencies. For our model, constrained, layer-specific eigenvector centrality values are defined by a system of independent eigenvalue problems and dependent pseudo-eigenvalue problems, whose solution can be efficiently realized using an interleaved power iteration algorithm. We refer to this model, and the associated algorithm, as the Constrained Multilayer Centrality (CMLC) method. The characteristics of this approach, and of standard techniques based on inter-layer edges, are demonstrated on both a simple multilayer network and on a range of random graph models. An R package implementing the CMLC method along with example vignettes is available at https://hrfrost.host.dartmouth.edu/CMLC/.

Unveiling the influence of global innovation networks on corporate innovation: evidence from the international semiconductor industry.

In this study, we investigate the influence of global innovation networks (GINs) on the innovation output of semiconductor firms. Utilizing negative binomial regression and network analysis, we assess how network positions, specifically degree, betweenness, and closeness centrality, affect firms' innovation performance, revealing significant positive impacts. Moreover, our results identify a positive U-shaped relationship between structural holes in GINs and innovation performance, suggesting that while moderate network engagement aids innovation, too much can be detrimental. This research provides key insights into optimizing GIN participation for better innovation results in the competitive semiconductor sector.