Social and Individual Components of Animal Contact in Preschool Children

ABSTRACT

Humans are generally biophilic. Still, for unknown reasons, interest in animals varies substantially among individuals. Our goal was to investigate how differential interest of children towards animals might be related to social competence and personality. We proposed two alternatives: 1) Children may compensate for potential deficits in social competence by resorting to animals, and 2) Socially well-connected children may show a particular interest in animals. We focused on relationships between age, gender, family background, play behavior, personality components, and contact with rabbits in 50 children (22 boys/28 girls; 3 to 7 years of age) at a preschool in Krems/Austria. Data were analyzed using GLM. We found that each one of these variables had significant impact on intensity of engagement with the rabbits. In general, girls, children with siblings, and children without pets were more oriented towards the rabbits than were boys, children without siblings, or pet-owning children. The older the children, the less frequently they occupied themselves with the rabbits but the longer they remained when they did engage them. Furthermore, we found that the more “Confident/Respected” (PCA factor 1) and less “Patient/Calm,” “Cheerful/Sociable,” and “Solitary” (PCA factors 2–4) the children, the more time they spent in direct occupation with rabbits. Most effects of the investigated variables varied between boys and girls. By and large, our findings support the hypothesis that the “socially competent” children were particularly interested in the animals. Also, children's social styles, as evinced in interactions with peers, were generally reflected in how they interacted with the rabbits.     

Social Contact Networks and Disease Eradicability under Voluntary Vaccination

Certain theories suggest that it should be difficult or impossible to eradicate a vaccine-preventable disease under voluntary vaccination: Herd immunity implies that the individual incentive to vaccinate disappears at high coverage levels. Historically, there have been examples of declining coverage for vaccines, such as MMR vaccine and whole-cell pertussis vaccine, that are consistent with this theory. On the other hand, smallpox was globally eradicated by 1980 despite voluntary vaccination policies in many jurisdictions. Previous modeling studies of the interplay between disease dynamics and individual vaccinating behavior have assumed that infection is transmitted in a homogeneously mixing population. By comparison, here we simulate transmission of a vaccine-preventable SEIR infection through a random, static contact network. Individuals choose whether to vaccinate based on infection risks from neighbors, and based on vaccine risks. When neighborhood size is small, rational vaccinating behavior results in rapid containment of the infection through voluntary ring vaccination. As neighborhood size increases (while the average force of infection is held constant), a threshold is reached beyond which the infection can break through partially vaccinated rings, percolating through the whole population and resulting in considerable epidemic final sizes and a large number vaccinated. The former outcome represents convergence between individually and socially optimal outcomes, whereas the latter represents their divergence, as observed in most models of individual vaccinating behavior that assume homogeneous mixing. Similar effects are observed in an extended model using smallpox-specific natural history and transmissibility assumptions. This work illustrates the significant qualitative differences between behavior–infection dynamics in discrete contact-structured populations versus continuous unstructured populations. This work also shows how disease eradicability in populations where voluntary vaccination is the primary control mechanism may depend partly on whether the disease is transmissible only to a few close social contacts or to a larger subset of the population.     

How the Dynamics and Structure of Sexual Contact Networks Shape Pathogen Phylogenies

The characteristics of the host contact network over which a pathogen is transmitted affect both epidemic spread and the projected effectiveness of control strategies. Given the importance of understanding these contact networks, it is unfortunate that they are very difficult to measure directly. This challenge has led to an interest in methods to infer information about host contact networks from pathogen phylogenies, because in shaping a pathogen''s opportunities for reproduction, contact networks also shape pathogen evolution. Host networks influence pathogen phylogenies both directly, through governing opportunities for evolution, and indirectly by changing the prevalence and incidence. Here, we aim to separate these two effects by comparing pathogen evolution on different host networks that share similar epidemic trajectories. This approach allows use to examine the direct effects of network structure on pathogen phylogenies, largely controlling for confounding differences arising from population dynamics. We find that networks with more heterogeneous degree distributions yield pathogen phylogenies with more variable cluster numbers, smaller mean cluster sizes, shorter mean branch lengths, and somewhat higher tree imbalance than networks with relatively homogeneous degree distributions. However, in particular for dynamic networks, we find that these direct effects are relatively modest. These findings suggest that the role of the epidemic trajectory, the dynamics of the network and the inherent variability of metrics such as cluster size must each be taken into account when trying to use pathogen phylogenies to understand characteristics about the underlying host contact network.     

Discovering Link Communities in Complex Networks by an Integer Programming Model and a Genetic Algorithm

Identification of communities in complex networks is an important topic and issue in many fields such as sociology, biology, and computer science. Communities are often defined as groups of related nodes or links that correspond to functional subunits in the corresponding complex systems. While most conventional approaches have focused on discovering communities of nodes, some recent studies start partitioning links to find overlapping communities straightforwardly. In this paper, we propose a new quantity function for link community identification in complex networks. Based on this quantity function we formulate the link community partition problem into an integer programming model which allows us to partition a complex network into overlapping communities. We further propose a genetic algorithm for link community detection which can partition a network into overlapping communities without knowing the number of communities. We test our model and algorithm on both artificial networks and real-world networks. The results demonstrate that the model and algorithm are efficient in detecting overlapping community structure in complex networks.     

A Possible Genetic Link between MTHFR Genotype and Smoking Behavior

Background

Hyperhomocysteinemia is an independent risk factor for stroke and other vascular events. The variant methylenetetrahydrofolate reductase (MTHFR) C677T is associated with elevated homocysteine levels, cardiovascular disease and stroke, which supports a causal relationship between hyperhomocysteinemia and vascular disease. However, MTHFR variants have also been reported to be associated with smoking behavior, which could be an important confounder.

Methodology/Principal Findings

We analyzed the MTHFR variants C677T and A1298C in two independent samples of 525 and 535 individuals, respectively. 21% of the non-smokers, but only 12% of the smokers were homozygous carriers of both MTHFR wildtype alleles, i.e. 677CC and 1298AA (Chi² = 15.8; p<0.001; binary regression). Plasma homocysteine levels were higher in smokers (13.9±4.1 µmol/L) than in non-smokers (12.6±4.0 µmol/L; F = 11.4; p = 0.001; ANOVA). Smoking MTHFR 677TT individuals had the highest plasma homocysteine levels (16.2±5.2 µmol/L), non-smoking 677CC individuals had the lowest (12.2±13.6 µmol/L).

Conclusions/Significance

In our study samples, MTHFR variants and smoking behaviour were associated with homocysteine plasma levels. In addition, the MTHFR variants were associated with smoking behaviour. Such an association may be a relevant confounder between MTHFR variants, homocysteine plasma levels and vascular diseases.     

Incorporating Disease and Population Structure into Models of SIR Disease in Contact Networks

We consider the recently introduced edge-based compartmental models (EBCM) for the spread of susceptible-infected-recovered (SIR) diseases in networks. These models differ from standard infectious disease models by focusing on the status of a random partner in the population, rather than a random individual. This change in focus leads to simple analytic models for the spread of SIR diseases in random networks with heterogeneous degree. In this paper we extend this approach to handle deviations of the disease or population from the simplistic assumptions of earlier work. We allow the population to have structure due to effects such as demographic features or multiple types of risk behavior. We allow the disease to have more complicated natural history. Although we introduce these modifications in the static network context, it is straightforward to incorporate them into dynamic network models. We also consider serosorting, which requires using dynamic network models. The basic methods we use to derive these generalizations are widely applicable, and so it is straightforward to introduce many other generalizations not considered here. Our goal is twofold: to provide a number of examples generalizing the EBCM method for various different population or disease structures and to provide insight into how to derive such a model under new sets of assumptions.     

Perceived Social Support Moderates the Link between Attachment Anxiety and Health Outcomes

Two literatures have explored some of the effects intimate relationships can have on physical and mental health outcomes. Research investigating health through the lens of attachment theory has demonstrated that more anxiously attached individuals in particular consistently report poorer health. Separate research on perceived social support (e.g., partner or spousal support) suggests that higher support has salutary influences on various health outcomes. Little to no research, however, has explored the interaction of attachment anxiety and perceived social support on health outcomes. The present study examined the attachment-health link and the moderating role of perceived social support in a community sample of married couples. Results revealed that more anxious persons reported poorer overall physical and mental health, more bodily pain, more medical symptoms, and impaired daily functioning, even after controlling for age, relationship length, neuroticism, and marital quality. Additionally, perceived social support interacted with attachment anxiety to influence health; more anxious individuals'' health was poorer even when perceived social support was high, whereas less anxious individuals'' health benefited from high support. Possible mechanisms underlying these findings and the importance of considering attachment anxiety in future studies of poor health in adulthood are discussed.     

Social Contact Networks and Mixing among Students in K-12 Schools in Pittsburgh,PA

Students attending schools play an important role in the transmission of influenza. In this study, we present a social network analysis of contacts among 1,828 students in eight different schools in urban and suburban areas in and near Pittsburgh, Pennsylvania, United States of America, including elementary, elementary-middle, middle, and high schools. We collected social contact information of students who wore wireless sensor devices that regularly recorded other devices if they are within a distance of 3 meters. We analyzed these networks to identify patterns of proximal student interactions in different classes and grades, to describe community structure within the schools, and to assess the impact of the physical environment of schools on proximal contacts. In the elementary and middle schools, we observed a high number of intra-grade and intra-classroom contacts and a relatively low number of inter-grade contacts. However, in high schools, contact networks were well connected and mixed across grades. High modularity of lower grades suggests that assumptions of homogeneous mixing in epidemic models may be inappropriate; whereas lower modularity in high schools suggests that homogenous mixing assumptions may be more acceptable in these settings. The results suggest that interventions targeting subsets of classrooms may work better in elementary schools than high schools. Our work presents quantitative measures of age-specific, school-based contacts that can be used as the basis for constructing models of the transmission of infections in schools.     

Time Development in the Early History of Social Networks: Link Stabilization,Group Dynamics,and Segregation

Studies of the time development of empirical networks usually investigate late stages where lasting connections have already stabilized. Empirical data on early network history are rare but needed for a better understanding of how social network topology develops in real life. Studying students who are beginning their studies at a university with no or few prior connections to each other offers a unique opportunity to investigate the formation and early development of link patterns and community structure in social networks. During a nine week introductory physics course, first year physics students were asked to identify those with whom they communicated about problem solving in physics during the preceding week. We use these students'' self reports to produce time dependent student interaction networks. We investigate these networks to elucidate possible effects of different student attributes in early network formation. Changes in the weekly number of links show that while roughly half of all links change from week to week, students also reestablish a growing number of links as they progress through their first weeks of study. Using the Infomap community detection algorithm, we show that the networks exhibit community structure, and we use non-network student attributes, such as gender and end-of-course grade to characterize communities during their formation. Specifically, we develop a segregation measure and show that students structure themselves according to gender and pre-organized sections (in which students engage in problem solving and laboratory work), but not according to end-of-coure grade. Alluvial diagrams of consecutive weeks'' communities show that while student movement between groups are erratic in the beginnning of their studies, they stabilize somewhat towards the end of the course. Taken together, the analyses imply that student interaction networks stabilize quickly and that students establish collaborations based on who is immediately available to them and on observable personal characteristics.     

Competition between Homophily and Information Entropy Maximization in Social Networks

In social networks, it is conventionally thought that two individuals with more overlapped friends tend to establish a new friendship, which could be stated as homophily breeding new connections. While the recent hypothesis of maximum information entropy is presented as the possible origin of effective navigation in small-world networks. We find there exists a competition between information entropy maximization and homophily in local structure through both theoretical and experimental analysis. This competition suggests that a newly built relationship between two individuals with more common friends would lead to less information entropy gain for them. We demonstrate that in the evolution of the social network, both of the two assumptions coexist. The rule of maximum information entropy produces weak ties in the network, while the law of homophily makes the network highly clustered locally and the individuals would obtain strong and trust ties. A toy model is also presented to demonstrate the competition and evaluate the roles of different rules in the evolution of real networks. Our findings could shed light on the social network modeling from a new perspective.     

Emergence of Scale-Free Close-Knit Friendship Structure in Online Social Networks

Although the structural properties of online social networks have attracted much attention, the properties of the close-knit friendship structures remain an important question. Here, we mainly focus on how these mesoscale structures are affected by the local and global structural properties. Analyzing the data of four large-scale online social networks reveals several common structural properties. It is found that not only the local structures given by the indegree, outdegree, and reciprocal degree distributions follow a similar scaling behavior, the mesoscale structures represented by the distributions of close-knit friendship structures also exhibit a similar scaling law. The degree correlation is very weak over a wide range of the degrees. We propose a simple directed network model that captures the observed properties. The model incorporates two mechanisms: reciprocation and preferential attachment. Through rate equation analysis of our model, the local-scale and mesoscale structural properties are derived. In the local-scale, the same scaling behavior of indegree and outdegree distributions stems from indegree and outdegree of nodes both growing as the same function of the introduction time, and the reciprocal degree distribution also shows the same power-law due to the linear relationship between the reciprocal degree and in/outdegree of nodes. In the mesoscale, the distributions of four closed triples representing close-knit friendship structures are found to exhibit identical power-laws, a behavior attributed to the negligible degree correlations. Intriguingly, all the power-law exponents of the distributions in the local-scale and mesoscale depend only on one global parameter, the mean in/outdegree, while both the mean in/outdegree and the reciprocity together determine the ratio of the reciprocal degree of a node to its in/outdegree. Structural properties of numerical simulated networks are analyzed and compared with each of the four real networks. This work helps understand the interplay between structures on different scales in online social networks.     

A Genetic Link between an mRNA-Specific Translational Activator and the Translation System in Yeast Mitochondria

Translation of the Saccharomyces cerevisiae mitochondrial mRNA encoding cytochrome c oxidase subunit III (coxIII) specifically requires the products of at least three nuclear genes, PET122, PET494 and PET54. pet122 mutations that remove 24-67 amino acid residues from the carboxyterminus of the gene product were found to be suppressed by unlinked nuclear mutations. These unlinked suppressors fail to suppress both a pet122 missense mutation and a complete pet122 deletion. One of the suppressor mutations causes a heat-sensitive nonrespiratory growth phenotype in an otherwise wild-type strain and reduces translation of all mitochondrial gene products in cells grown at high temperature. This suppressor maps to a newly identified gene on chromosome XV termed PET123. The sequence of a DNA fragment carrying PET123 contains one major open reading frame encoding a basic protein of 318 amino acids. Inactivation of the chromosomal copy of PET123 by interruption of this open reading frame causes cells to become rho- (sustain large deletions in their mtDNA). This phenotype is characteristic for null alleles of genes whose products are essential for general mitochondrial protein synthesis. Thus our data strongly suggest that the PET123 protein is a component of the mitochondrial translation apparatus that interacts directly with the coxIII-mRNA-specific translational activator PET122.     

Genes as Cues of Relatedness and Social Evolution in Heterogeneous Environments

There are many situations where relatives interact while at the same time there is genetic polymorphism in traits influencing survival and reproduction. Examples include cheater-cooperator polymorphism and polymorphic microbial pathogens. Environmental heterogeneity, favoring different traits in nearby habitats, with dispersal between them, is one general reason to expect polymorphism. Currently, there is no formal framework of social evolution that encompasses genetic polymorphism. We develop such a framework, thus integrating theories of social evolution into the evolutionary ecology of heterogeneous environments. We allow for adaptively maintained genetic polymorphism by applying the concept of genetic cues. We analyze a model of social evolution in a two-habitat situation with limited dispersal between habitats, in which the average relatedness at the time of helping and other benefits of helping can differ between habitats. An important result from the analysis is that alleles at a polymorphic locus play the role of genetic cues, in the sense that the presence of a cue allele contains statistical information for an organism about its current environment, including information about relatedness. We show that epistatic modifiers of the cue polymorphism can evolve to make optimal use of the information in the genetic cue, in analogy with a Bayesian decision maker. Another important result is that the genetic linkage between a cue locus and modifier loci influences the evolutionary interest of modifiers, with tighter linkage leading to greater divergence between social traits induced by different cue alleles, and this can be understood in terms of genetic conflict.     

Insects Represent a Link between Food Animal Farms and the Urban Environment for Antibiotic Resistance Traits

Antibiotic-resistant bacterial infections result in higher patient mortality rates, prolonged hospitalizations, and increased health care costs. Extensive use of antibiotics as growth promoters in the animal industry represents great pressure for evolution and selection of antibiotic-resistant bacteria on farms. Despite growing evidence showing that antibiotic use and bacterial resistance in food animals correlate with resistance in human pathogens, the proof for direct transmission of antibiotic resistance is difficult to provide. In this review, we make a case that insects commonly associated with food animals likely represent a direct and important link between animal farms and urban communities for antibiotic resistance traits. Houseflies and cockroaches have been shown to carry multidrug-resistant clonal lineages of bacteria identical to those found in animal manure. Furthermore, several studies have demonstrated proliferation of bacteria and horizontal transfer of resistance genes in the insect digestive tract as well as transmission of resistant bacteria by insects to new substrates. We propose that insect management should be an integral part of pre- and postharvest food safety strategies to minimize spread of zoonotic pathogens and antibiotic resistance traits from animal farms. Furthermore, the insect link between the agricultural and urban environment presents an additional argument for adopting prudent use of antibiotics in the food animal industry.     

Predicting Human Preferences Using the Block Structure of Complex Social Networks

With ever-increasing available data, predicting individuals'' preferences and helping them locate the most relevant information has become a pressing need. Understanding and predicting preferences is also important from a fundamental point of view, as part of what has been called a “new” computational social science. Here, we propose a novel approach based on stochastic block models, which have been developed by sociologists as plausible models of complex networks of social interactions. Our model is in the spirit of predicting individuals'' preferences based on the preferences of others but, rather than fitting a particular model, we rely on a Bayesian approach that samples over the ensemble of all possible models. We show that our approach is considerably more accurate than leading recommender algorithms, with major relative improvements between 38% and 99% over industry-level algorithms. Besides, our approach sheds light on decision-making processes by identifying groups of individuals that have consistently similar preferences, and enabling the analysis of the characteristics of those groups.