From temporal network data to the dynamics of social relationships

Networks are well-established representations of social systems, and temporal networks are widely used to study their dynamics. Temporal network data often consist in a succession of static networks over consecutive time windows whose length, however, is arbitrary, not necessarily corresponding to any intrinsic timescale of the system. Moreover, the resulting view of social network evolution is unsatisfactory: short time windows contain little information, whereas aggregating over large time windows blurs the dynamics. Going from a temporal network to a meaningful evolving representation of a social network therefore remains a challenge. Here we introduce a framework to that purpose: transforming temporal network data into an evolving weighted network where the weights of the links between individuals are updated at every interaction. Most importantly, this transformation takes into account the interdependence of social relationships due to the finite attention capacities of individuals: each interaction between two individuals not only reinforces their mutual relationship but also weakens their relationships with others. We study a concrete example of such a transformation and apply it to several data sets of social interactions. Using temporal contact data collected in schools, we show how our framework highlights specificities in their structure and temporal organization. We then introduce a synthetic perturbation into a data set of interactions in a group of baboons to show that it is possible to detect a perturbation in a social group on a wide range of timescales and parameters. Our framework brings new perspectives to the analysis of temporal social networks

Development and Implementation of the AIDA International Registry for Patients With Undifferentiated Systemic AutoInflammatory Diseases

Objective: This paper points out the design, development and deployment of the AutoInflammatory Disease Alliance (AIDA) International Registry dedicated to pediatric and adult patients affected by Undifferentiated Systemic AutoInflammatory Diseases (USAIDs). Methods: This is an electronic registry employed for real-world data collection about demographics, clinical, laboratory, instrumental and socioeconomic data of USAIDs patients. Data recruitment, based on the Research Electronic Data Capture (REDCap) tool, is designed to obtain standardized information for real-life research. The instrument is endowed with flexibility, and it could change over time according to the scientific acquisitions and potentially communicate with other similar tools; this platform ensures security, data quality and data governance. Results: The focus of the AIDA project is connecting physicians and researchers from all over the world to shed a new light on heterogeneous rare diseases. Since its birth, 110 centers from 23 countries and 4 continents have joined the AIDA project. Fifty-four centers have already obtained the approval from their local Ethics Committees. Currently, the platform counts 290 users (111 Principal Investigators, 179 Site Investigators, 2 Lead Investigators, and 2 data managers). The Registry is collecting baseline and follow-up data using 3,769 fields organized into 23 instruments, which include demographics, history, symptoms, trigger/risk factors, therapies, and healthcare information access for USAIDs patients. Conclusions: The development of the AIDA International Registry for USAIDs patients will facilitate the online collection of real standardized data, connecting a worldwide group of researchers: the Registry constitutes an international multicentre observational groundwork aimed at increasing the patient cohort of USAIDs in order to improve our knowledge of this peculiar cluster of autoinflammatory diseases

Cabbage and fermented vegetables : From death rate heterogeneity in countries to candidates for mitigation strategies of severe COVID-19

Large differences in COVID-19 death rates exist between countries and between regions of the same country. Some very low death rate countries such as Eastern Asia, Central Europe, or the Balkans have a common feature of eating large quantities of fermented foods. Although biases exist when examining ecological studies, fermented vegetables or cabbage have been associated with low death rates in European countries. SARS-CoV-2 binds to its receptor, the angiotensin-converting enzyme 2 (ACE2). As a result of SARS-CoV-2 binding, ACE2 downregulation enhances the angiotensin II receptor type 1 (AT(1)R) axis associated with oxidative stress. This leads to insulin resistance as well as lung and endothelial damage, two severe outcomes of COVID-19. The nuclear factor (erythroid-derived 2)-like 2 (Nrf2) is the most potent antioxidant in humans and can block in particular the AT(1)R axis. Cabbage contains precursors of sulforaphane, the most active natural activator of Nrf2. Fermented vegetables contain many lactobacilli, which are also potent Nrf2 activators. Three examples are: kimchi in Korea, westernized foods, and the slum paradox. It is proposed that fermented cabbage is a proof-of-concept of dietary manipulations that may enhance Nrf2-associated antioxidant effects, helpful in mitigating COVID-19 severity.Peer reviewe

Nrf2-interacting nutrients and COVID-19 : time for research to develop adaptation strategies

There are large between- and within-country variations in COVID-19 death rates. Some very low death rate settings such as Eastern Asia, Central Europe, the Balkans and Africa have a common feature of eating large quantities of fermented foods whose intake is associated with the activation of the Nrf2 (Nuclear factor (erythroid-derived 2)-like 2) anti-oxidant transcription factor. There are many Nrf2-interacting nutrients (berberine, curcumin, epigallocatechin gallate, genistein, quercetin, resveratrol, sulforaphane) that all act similarly to reduce insulin resistance, endothelial damage, lung injury and cytokine storm. They also act on the same mechanisms (mTOR: Mammalian target of rapamycin, PPAR gamma:Peroxisome proliferator-activated receptor, NF kappa B: Nuclear factor kappa B, ERK: Extracellular signal-regulated kinases and eIF2 alpha:Elongation initiation factor 2 alpha). They may as a result be important in mitigating the severity of COVID-19, acting through the endoplasmic reticulum stress or ACE-Angiotensin-II-AT(1)R axis (AT(1)R) pathway. Many Nrf2-interacting nutrients are also interacting with TRPA1 and/or TRPV1. Interestingly, geographical areas with very low COVID-19 mortality are those with the lowest prevalence of obesity (Sub-Saharan Africa and Asia). It is tempting to propose that Nrf2-interacting foods and nutrients can re-balance insulin resistance and have a significant effect on COVID-19 severity. It is therefore possible that the intake of these foods may restore an optimal natural balance for the Nrf2 pathway and may be of interest in the mitigation of COVID-19 severity

Analyse de la dynamique de réseaux sociaux animaux: de la collecte de données à la détection d'instabilités

Social interactions are an important fitness component of group living animals.Social network analysis, and in particular temporal networks, provides powerful tools to describe these social interactions and their evolution. The study of networks in their time-varying aspects requires the availability of large volumes of data with high temporal resolution.The use of temporal network formalism and tools is however still limited for non-human animals, because the data on animal interactions are still largely obtained from traditional hand-operated methods.In this thesis, we have produce long-term high-frequency data on non-human animal social interactions and we designed a new temporal network framework that could help in the future to understand ecological and evolutionary processes underlying social network formation and organization, and to study how changes in the environment, composition of the group or single key-relationships influence the entire network structure.Les interactions sociales sont une composante importante de la condition physique des animaux vivants en groupe.L'analyse des réseaux sciaux, et en particulier des réseaux temporels, fournit des outils puissants pour décrire ces interactions sociales et leur évolution. L'étude des réseaux dans leurs aspects temporelles nécessite la disponibilité de grands volumes de données à haute résolution temporelle.L'utilisation du formalisme et des outils du réseau temporel est cependant encore limitée pour les animaux, car les données sur les interactions animales sont encore largement obtenues à partir de méthodes manuelles traditionnelles.Dans cette thèse, nous avons produit des données à haute fréquence à long terme concernant les interactions sociales animales et nous avons conçu un nouveau model de réseau temporel. Ce travail pourrait aider à l'avenir à comprendre les processus écologiques et évolutifs sous-jacents à la formation et à l'organisation des réseaux sociaux, et à étudier comment les changements dans l'environnement, la composition du groupe ou les relations clés uniques influencent l'ensemble de la structure du réseau

Analyse de la dynamique des réseaux sociaux animaux : de la collecte de données à la detection d'(in)stabilité

Les interactions sociales sont une composante importante de la condition physique des animaux vivants en groupe.L'analyse des réseaux sciaux, et en particulier des réseaux temporels, fournit des outils puissants pour décrire ces interactions sociales et leur évolution. L'étude des réseaux dans leurs aspects temporelles nécessite la disponibilité de grands volumes de données à haute résolution temporelle.L'utilisation du formalisme et des outils du réseau temporel est cependant encore limitée pour les animaux, car les données sur les interactions animales sont encore largement obtenues à partir de méthodes manuelles traditionnelles.Dans cette thèse, nous avons produit des données à haute fréquence à long terme concernant les interactions sociales animales et nous avons conçu un nouveau model de réseau temporel. Ce travail pourrait aider à l'avenir à comprendre les processus écologiques et évolutifs sous-jacents à la formation et à l'organisation des réseaux sociaux, et à étudier comment les changements dans l'environnement, la composition du groupe ou les relations clés uniques influencent l'ensemble de la structure du réseau.Social interactions are an important fitness component of group living animals.Social network analysis, and in particular temporal networks, provides powerful tools to describe these social interactions and their evolution. The study of networks in their time-varying aspects requires the availability of large volumes of data with high temporal resolution.The use of temporal network formalism and tools is however still limited for non-human animals, because the data on animal interactions are still largely obtained from traditional hand-operated methods.In this thesis, we have produce long-term high-frequency data on non-human animal social interactions and we designed a new temporal network framework that could help in the future to understand ecological and evolutionary processes underlying social network formation and organization, and to study how changes in the environment, composition of the group or single key-relationships influence the entire network structure

Analyse de la dynamique de réseaux sociaux animaux: de la collecte de données à la détection d'instabilités

Social interactions are an important fitness component of group living animals.Social network analysis, and in particular temporal networks, provides powerful tools to describe these social interactions and their evolution. The study of networks in their time-varying aspects requires the availability of large volumes of data with high temporal resolution.The use of temporal network formalism and tools is however still limited for non-human animals, because the data on animal interactions are still largely obtained from traditional hand-operated methods.In this thesis, we have produce long-term high-frequency data on non-human animal social interactions and we designed a new temporal network framework that could help in the future to understand ecological and evolutionary processes underlying social network formation and organization, and to study how changes in the environment, composition of the group or single key-relationships influence the entire network structure.Les interactions sociales sont une composante importante de la condition physique des animaux vivants en groupe.L'analyse des réseaux sciaux, et en particulier des réseaux temporels, fournit des outils puissants pour décrire ces interactions sociales et leur évolution. L'étude des réseaux dans leurs aspects temporelles nécessite la disponibilité de grands volumes de données à haute résolution temporelle.L'utilisation du formalisme et des outils du réseau temporel est cependant encore limitée pour les animaux, car les données sur les interactions animales sont encore largement obtenues à partir de méthodes manuelles traditionnelles.Dans cette thèse, nous avons produit des données à haute fréquence à long terme concernant les interactions sociales animales et nous avons conçu un nouveau model de réseau temporel. Ce travail pourrait aider à l'avenir à comprendre les processus écologiques et évolutifs sous-jacents à la formation et à l'organisation des réseaux sociaux, et à étudier comment les changements dans l'environnement, la composition du groupe ou les relations clés uniques influencent l'ensemble de la structure du réseau

Data from: Detecting social (in)stability in primates from their temporal co-presence network

The stability of social relationships is essential to animals living in groups, and social network analysis provides a powerful tool to help characterize and understand their (in)stability and the consequences at the group level. However, the use of dynamic social networks is still limited because it requires long-term social data and new analytical tools. Here, we used a dataset of automatically collected cognitive tests comprising more than 16M records collected over 3 years in a group of 29 Guinea baboons (Papio papio) to study the dynamic evolution of their social structure. We first built a monthly aggregated temporal network describing the baboon’s co-presence in the cognitive testing booths. We then used a null model, considering the heterogeneity in the baboons' activity, to define both positive (association) and negative (avoidance) monthly networks. Combining positive and negative networks, we obtained monthly signed social networks whose study revealed, in accordance with earlier studies, that they were structurally balanced and that newly created edges tended to preserve the social balance. We then investigated several network metrics to gain insights into the individual's and the group's social networks long-term temporal evolutions. Interestingly, a measure of similarity between successive monthly networks was able to pinpoint periods of stability and instability and to show how some baboons' ego-networks remained stable while others changed radically. Our study confirms the prediction of social balance theory but also shows that it may be of limited applicability to study the dynamic evolution of animal social networks. In contrast, the use of the similarity measure proved to be very versatile and sensitive in detecting relationships' (in)stabilities at different levels. The changes we identified can be linked, at least in some cases, to females changing primary male, as observed in the wild