Hormones,behavior, and social network analysis: Exploring associations between cortisol,testosterone, and network structure

We used a new interdisciplinary paradigm of social network analysis (SNA) to investigate associations between hormones and social network structures. We examine these biobehavioral processes and test hypotheses about how hormones are associated with social network structures using exponential random graph modeling (ERGM) in a cohort of first-year students (n = 74; 93% female; M age = 27 years) from a highly competitive, accelerated nursing program. Participants completed friendship nominations and as a group simultaneously donated saliva (later assayed for cortisol and testosterone). ERGM analyses revealed that salivary cortisol levels were inversely associated with the number of outgoing ties (i.e., network activity). By contrast, testosterone was not related to friendship network structure. Integration of SNA and salivary bioscience creates a novel approach to understanding hormone–behavior relationships within the context of human social ecologies.     

Evidence for social role in a dolphin social network

Social animals have to take into consideration the behaviour of conspecifics when making decisions to go by their daily lives. These decisions affect their fitness and there is therefore an evolutionary pressure to try making the right choices. In many instances individuals will make their own choices and the behaviour of the group will be a democratic integration of everyone’s decision. However, in some instances it can be advantageous to follow the choice of a few individuals in the group if they have more information regarding the situation that has arisen. Here I provide early evidence that decisions about shifts in activity states in a population of bottlenose dolphin follow such a decision-making process. This unshared consensus is mediated by a non-vocal signal, which can be communicated globally within the dolphin school. These signals are emitted by individuals that tend to have more information about the behaviour of potential competitors because of their position in the social network. I hypothesise that this decision-making process emerged from the social structure of the population and the need to maintain mixed-sex schools.     

Environment damage assessment: A literature review using social network analysis

Rapid development of environment science and ecology has witnessed the emerging of economical tools applying to environmental issues, especially environment damage assessment. This study applied bibliometrics tools to the environment damage assessment methods based on on-line SCI-E and SSCI from 2007 to 2016, and described the bibliometric characteristics including publications number, journals, and disciplines. Moreover, the concept of social network was introduced into bibliometrics analysis, and the collaboration relationship network of countries, authors, and institutions are generated. Furthermore, frequency analysis of keywords revealed five most widely used methods in environment damage assessment, including market valuation, contingent valuation method, choice experiment method, travel cost method, and hedonic price method, which are specifically reviewed on developing trend, economic basis, and operating process.     

Metabolomics assisted metabolic network modeling and network wide analysis of metabolites in microbiology

Metabolomics is the science of qualitatively and quantitatively analyzing low molecular weight metabolites occur in a given biological system. It provides valuable information to elucidate the functional roles and relations of different metabolites in a metabolic pathway. In recent years, a large amount of research on microbial metabolomics has been conducted. It has become a useful tool for achieving highly efficient synthesis of target metabolites. At the same time, many studies have been conducted over the years in order to integrate metabolomics data into metabolic network modeling, which has yielded many exciting results. Additionally, metabolomics also shows great advantages in analyzing the relationship of metabolites network wide. Integrating metabolomics data into metabolic network construction and applying it in network wide analysis of cell metabolism would further improve our ability to control cellular metabolism and optimize the design of cell factories for the overproduction of valuable biochemicals. This review will examine recent progress in the application of metabolomics approaches in metabolic network modeling and network wide analysis of microbial cell metabolism.     

The influence of social structure on the propagation of social information in artificial primate groups: a graph-based simulation approach

Observations of primate groups have shown that social learning can lead to the development of temporal stable traditions or even proto-culture. The social structure of primate groups is highly diverse and it has been proposed that differences in the group structure shall influence the patterns of social information transmission. While empirical studies have mainly focused on the psychological mechanisms of social learning in individuals, the phenomenon of information propagation within the group has received relatively little attention. This might be due to the fact that formal theories that allow actual testing have not been formulated, or were kept too simple, ignoring the social dynamics of multi-agent societies. We want to propose a network approach to social information transmission that (1) preserves the complexity of the social structure of primate groups and (2) allows direct application to empirical data. Results from simulation experiments with artificial group structures confirm that association patterns of group-members influence the expected speed of information transmission during the propagation process. Introducing a forgetting rate shows that under certain conditions the proportion of informed individuals will reach a stable rate in some systems while it will drop to zero in others. This suggests that the likelihood to observe temporal stable traditions shall differ between social systems with different structure.     

Spread of two linked social norms on complex interaction networks

In this paper, we study the spread of social norms, such as rules and customs that are components of human cultures. We consider the spread of two social norms, which are linked through individual behaviors. Spreading social norms depend not only on the social network structure, but also on the learning system. We consider four social network structures: (1) complete mixing, in which each individual interacts with the others at random, (2) lattice, in which each individual interacts with its neighbors with some probability and with the others at random, (3) power-law network, in which a few influential people have more social contacts than the others, and (4) random graph network, in which the number of contacts follows a Poisson distribution. Using the lattice model, we also investigate the effect of the small-world phenomenon on the dynamics of social norms. In our models, each individual learns a social norm by trial and error (individual learning) and also imitates the other's social norm (social learning). We investigate how social network structure and learning systems affect the spread of two linked social norms. Our main results are: (1) Social learning does not lead to coexistence of social norms. Individual learning produces coexistence, and the dynamics of coexistence depend on which social norms are learned individually. (2) Social norms spread fastest in the power-law network model, followed by the random graph model, the complete mixing model, the two-dimensional lattice model and the one-dimensional lattice. (3) We see a "small world effect" in the one-dimensional model, but not in two dimensions.     

Hairpin formation in DNA computation presents limits for large NP-complete problems

Recently, several DNA computing paradigms for NP-complete problems were presented, especially for the 3-SAT problem. Can the present paradigms solve more than just trivial instances of NP-complete problems? In this paper we show that with high probability potentially deleterious features such as severe hairpin loops would be likely to arise. If DNA strand x of length n and the 'complement' of the reverse of x have l match bases, then x forms a hairpin loop and is called a (n,l)-hairpin format. Let gamma=2l/n. Then gamma can be considered as a measurement of the stability of hairpin loops. Let p(n,l) be the probability that a n-mer DNA strand is a (n,l)-hairpin format, and q(n,l)((m)) be the probability that m ones are chosen at random from 4(n) n-mer oligonucleotides such that at least one of the m ones is a (n,l)-hairpin format. Then, q(n,l)((m))=1-(1-p(n,l))(m)=mp(n,l). If we require q(n,l)((m))相似文献   

Developmental stress predicts social network position

The quantity and quality of social relationships, as captured by social network analysis, can have major fitness consequences. Various studies have shown that individual differences in social behaviour can be due to variation in exposure to developmental stress. However, whether these developmental differences translate to consistent differences in social network position is not known. We experimentally increased levels of the avian stress hormone corticosterone (CORT) in nestling zebra finches in a fully balanced design. Upon reaching nutritional independence, we released chicks and their families into two free-flying rooms, where we measured daily social networks over five weeks using passive integrated transponder tags. Developmental stress had a significant effect on social behaviour: despite having similar foraging patterns, CORT chicks had weaker associations to their parents than control chicks. Instead, CORT chicks foraged with a greater number of flock mates and were less choosy with whom they foraged, resulting in more central network positions. These findings highlight the importance of taking developmental history into account to understand the drivers of social organization in gregarious species.     

Domestic pigs alter their social strategy in response to social group size

In a series of studies, we investigated the concept that domesticated pigs may adopt cost efficient social strategies, in which individuals become less aggressive due to a higher number of potential competitors, in large social groups. Six blocks, each comprising four pens of 18 pigs (small group, SG) and two pens of 108 pigs (large group, LG) were used. Pigs were 11 weeks old at the initial group formation. Weeks 1, 6 and 12 following SG and LG formation, two randomly selected pigs with SG or LG social experience were incorporated into another SG or LG for a period of 2 h and the aggressive behaviour of pigs was observed. Four test combinations were used (SG to SG (SS), SG to LG (SL), LG to SG (LS) and LG to LG (LL)). There was less aggression in the LL compared to SS, SL and LS combinations (1.6% versus 2.5, 2.3, and 2.5% of time, respectively, P = 0.009). Furthermore, 8 weeks following SG and LG formation, a total of 200 pigs were regrouped for 2 h in groups of four in a neutral test arena to assess the effect of prior social experience (SG vs. LG) on aggressive behaviour. Pigs were regrouped with their own group members (familiar), or with unfamiliar non-group members either from SG or LG. Five test combinations were used (four pigs from the same SG (S), four pigs from the same LG (L), two pigs from a SG and two pigs from a LG (SL), two pigs each from two different LG (LL) and two pigs each from two different SG (SS)). Pigs derived from SG fought aggressively (duration, s) with unfamiliar pigs, compared to those derived from LG (4.9, 6.7, 16.1, 12.1 and 9.5 s for S, L, SS, SL and LL test combinations respectively, P < 0.001). In addition, the latency to initiate first aggressive interaction was shorter (duration, s) in SS than LL treatment combination (23.3, 22.5, 10.8, 11.8 and 19.2 s for S, L, SS, SL and LL test combinations respectively, P = 0.008). Taken together, our results suggest that pigs become less aggressive and may shift to a low-aggressive social strategy in large social groups. This may provide potential benefits for welfare of pigs under commercial production situations.     

Applying a neural network to predict the thermodynamic parameters for an expanded nearest-neighbor model

Predicting the secondary and tertiary structure of RNAs largely depends on our capabilities in estimating the thermodynamics of RNA duplexes. In this work, an expanded nearest-neighbor model, designated INN-48, is established. The thermodynamic parameters of this model are predicted using both multiple linear regression analysis and neural network analysis. It is suggested that due to the increase in the number of parameters and the insufficiency of the existing data, neural network analysis results in more reliable predictions. Furthermore, it is suggested that INN-48 can be used to estimate the thermodynamics of RNA duplex formation for longer sequences, whereas INN-HB, the previous model on which INN-48 is based, can be used for short sequences.     

Metabolic-flux and network analysis in fourteen hemiascomycetous yeasts

In a quantitative comparative study, we elucidated the glucose metabolism in fourteen hemiascomycetous yeasts from the Genolevures project. The metabolic networks of these different species were first established by (13)C-labeling data and the inventory of the genomes. This information was subsequently used for metabolic-flux ratio analysis to quantify the intracellular carbon flux distributions in these yeast species. Firstly, we found that compartmentation of amino acid biosynthesis in most species was identical to that in Saccharomyces cerevisiae. Exceptions were the mitochondrial origin of aspartate biosynthesis in Yarrowia lipolytica and the cytosolic origin of alanine biosynthesis in S. kluyveri. Secondly, the control of flux through the TCA cycle was inversely correlated with the ethanol production rate, with S. cerevisiae being the yeast with the highest ethanol production capacity. The classification between respiratory and respiro-fermentative metabolism, however, was not qualitatively exclusive but quantitatively gradual. Thirdly, the flux through the pentose phosphate (PP) pathway was correlated to the yield of biomass, suggesting a balanced production and consumption of NADPH. Generally, this implies the lack of active transhydrogenase-like activities in hemiascomycetous yeasts under the tested growth condition, with Pichia angusta as the sole exception. In the latter case, about 40% of the NADPH was produced in the PP pathway in excess of the requirements for biomass production, which strongly suggests the operation of a yet unidentified mechanism for NADPH reoxidation in this species. In most yeasts, the PP pathway activity appears to be driven exclusively by the demand for NADPH.     

Behavioural phenotype affects social interactions in an animal network

Animal social networks can be extremely complex and are characterized by highly non-random interactions between group members. However, very little is known about the underlying factors affecting interaction preferences, and hence network structure. One possibility is that behavioural differences between individuals, such as how bold or shy they are, can affect the frequency and distribution of their interactions within a network. We tested this using individually marked three-spined sticklebacks (Gasterosteus aculeatus), and found that bold individuals had fewer overall interactions than shy fish, but tended to distribute their interactions more evenly across all group members. Shy fish, on the other hand, tended to associate preferentially with a small number of other group members, leading to a highly skewed distribution of interactions. This was mediated by the reduced tendency of shy fish to move to a new location within the tank when they were interacting with another individual; bold fish showed no such tendency and were equally likely to move irrespective of whether they were interacting or not. The results show that animal social network structure can be affected by the behavioural composition of group members and have important implications for understanding the spread of information and disease in social groups.     

A social network analysis of treatment discoveries in cancer

Controlled clinical trials are widely considered to be the vehicle to treatment discovery in cancer that leads to significant improvements in health outcomes including an increase in life expectancy. We have previously shown that the pattern of therapeutic discovery in randomized controlled trials (RCTs) can be described by a power law distribution. However, the mechanism generating this pattern is unknown. Here, we propose an explanation in terms of the social relations between researchers in RCTs. We use social network analysis to study the impact of interactions between RCTs on treatment success. Our dataset consists of 280 phase III RCTs conducted by the NCI from 1955 to 2006. The RCT networks are formed through trial interactions formed i) at random, ii) based on common characteristics, or iii) based on treatment success. We analyze treatment success in terms of survival hazard ratio as a function of the network structures. Our results show that the discovery process displays power law if there are preferential interactions between trials that may stem from researchers' tendency to interact selectively with established and successful peers. Furthermore, the RCT networks are "small worlds": trials are connected through a small number of ties, yet there is much clustering among subsets of trials. We also find that treatment success (improved survival) is proportional to the network centrality measures of closeness and betweenness. Negative correlation exists between survival and the extent to which trials operate within a limited scope of information. Finally, the trials testing curative treatments in solid tumors showed the highest centrality and the most influential group was the ECOG. We conclude that the chances of discovering life-saving treatments are directly related to the richness of social interactions between researchers inherent in a preferential interaction model.     

Increased functional connectivity with puberty in the mentalising network involved in social emotion processing

This article is part of a Special Issue “Puberty and Adolescence”.     

Detecting social transmission in networks

In recent years researchers have drawn attention to a need for new methods with which to identify the spread of behavioural innovations through social transmission in animal populations. Network-based analyses seek to recognise diffusions mediated by social learning by detecting a correspondence between patterns of association and the flow of information through groups. Here we introduce a new order of acquisition diffusion analysis (OADA) and develop established time of acquisition diffusion analysis (TADA) methods further. Through simulation we compare the merits of these and other approaches, demonstrating that OADA and TADA have greater power and lower Type I error rates than available alternatives, and specifying when each approach should be deployed. We illustrate the new methods by applying them to reanalyse an established dataset corresponding to the diffusion of foraging innovations in starlings, where OADA and TADA detect social transmission that hitherto had been missed. The methods are potentially widely applicable by researchers wishing to detect social learning in natural and captive populations of animals, and to facilitate this we provide code to implement OADA and TADA in the statistical package R.