Differential effects of multiplex and uniplex affiliative relationships on biomarkers of inflammation

Social relationships profoundly impact health in social species. Much of what we know regarding the impact of affiliative social relationships on health in nonhuman primates (NHPs) has focused on the structure of connections or the quality of relationships. These relationships are often quantified by comparing different types of affiliative behaviors (e.g., contact sitting, grooming, alliances, proximity) or pooling affiliative behaviors into an overall measure of affiliation. The influence of the breadth of affiliative behaviors (e.g., how many different types or which ones) a dyad engages in on health and fitness outcomes remains unknown. Here we employed a social network approach to explicitly explore whether the integration of different affiliative behaviors within a relationship can point to the potential function of those relationships and their impact on health-related biomarkers (i.e., pro-inflammatory cytokines) in a commonly studied non-human primate model system, the rhesus m..., Data collection: Data were collected on a four groups of Rhesus macaques. Behavioural observations were conducted all adult individuals (3+ years) in the group and serum samples were collected. Affiliative and agonistic interactions were recorded. Serum samples were assay for inflammatory cytokines. Data processing: Behavioural observations were used to construct a weighted, directed behavioural network for each of the following behaviours: (1) all grooming, (2) all contact sitting, (3) multiplex grooming (dyads that both groomed and contact sat, edge-wieghts reflect grooming frequency), (4) uniplex grooming (dyads that only groomed and never contact sat, edge-wieghts reflect grooming frequency), (5) multiplex contact sitting (dyads that both groomed and contact sat, edge-wieghts reflect contact sitting frequency), and (6) uniplex grooming (dyads that only contact sat and never groomed, edge-wieghts reflect contact sitting frequency). Whole network metrics including density, modularit..., , # Data from: Differential effects of multiplex and uniplex affiliative relationships on biomarkers of inflammation [https://doi.org/10.5061/dryad.866t1g1xq](https://doi.org/10.5061/dryad.866t1g1xq) We have included ***2 data files and 3 R code files***. The R code describes the processing of data that generated the two data files which form the basis for analyses presented in the manuscript. ## Description of the data and file structure **Dataset_IndividualLevel**: Produced using the function contained in the file “*CalculateIndividualCentralityFunction.txt*” and R code file “*IndividualLevelAnalysis.txt*” and used for running individual level analyses presented. ID = Unique animal identifier      Cage = Social group identifier Sex = Subject sex Age = subject age in years Mat = Identifier for subject matriline IL60 = Concentration of serum IL-6 in pg/mL TNFa0 = Concentration of serum Tumor necrosis factor alpha in pg/mL DominanceCertainty = Subject dominance certainty as c...社会关系对群居物种的健康具有深远影响。目前我们关于非人类灵长类（nonhuman primates, NHPs）的亲和社会关系对健康的认知，大多聚焦于联结结构或关系质量。这类关系通常通过比较不同类型的亲和行为（如接触坐姿、梳理毛发、联盟行为、空间接近），或将各类亲和行为整合为整体亲和度指标来量化。但成对个体所参与的亲和行为广度（例如，涉及多少种不同行为或具体是哪些行为）对健康与适合度结果的影响仍不明确。本研究采用社会网络（social network）方法，旨在明确探讨：在常用的非人灵长类模式生物——恒河猴（rhesus macaque）中，某一关系内不同亲和行为的整合是否能够揭示这些关系的潜在功能，以及它们对健康相关生物标志物（即促炎细胞因子（pro-inflammatory cytokines））的影响。 ### 数据采集 本研究对四群恒河猴开展数据采集。针对群体内所有3岁及以上的成年个体进行行为观察，并采集血清样本。记录亲和与争斗互动行为，并对血清样本进行炎症细胞因子检测。 ### 数据处理 基于行为观察结果，针对以下各类行为分别构建加权有向行为网络：(1) 全部梳理行为；(2) 全部接触坐姿行为；(3) 多模式梳理行为（同时发生梳理与接触坐姿行为的二元组，边权重反映梳理行为发生频率）；(4) 单模式梳理行为（仅发生梳理行为、从未发生接触坐姿行为的二元组，边权重反映梳理行为发生频率）；(5) 多模式接触坐姿行为（同时发生梳理与接触坐姿行为的二元组，边权重反映接触坐姿行为发生频率）；(6) 单模式接触坐姿行为（仅发生接触坐姿行为、从未发生梳理行为的二元组，边权重反映接触坐姿行为发生频率）。整体网络指标包括密度、模块度（modularity）…… # 数据集来源：多模式与单模式亲和关系对炎症生物标志物的差异化影响 [https://doi.org/10.5061/dryad.866t1g1xq](https://doi.org/10.5061/dryad.866t1g1xq) 本数据集包含2个数据文件与3个R代码文件。R代码用于说明生成这两个数据文件的处理流程，而这两个数据文件为本论文呈现的分析提供了基础。 ## 数据与文件结构说明 **个体水平数据集（Dataset_IndividualLevel）**：通过文件"CalculateIndividualCentralityFunction.txt"中的函数以及R代码文件"IndividualLevelAnalysis.txt"生成，用于执行本研究呈现的个体水平分析。 - ID：唯一动物标识符 - Cage：社会群体标识符 - Sex：受试个体性别 - Age：受试个体年龄（单位：年） - Mat：受试个体母系群标识符 - IL60：血清白细胞介素-6（IL-6）浓度，单位pg/mL - TNFa0：血清肿瘤坏死因子-α（TNF-α）浓度，单位pg/mL - DominanceCertainty：受试个体支配确定性……