Evolution of cooperation on large networks with community structure

Cooperation is a major factor in the evolution of human societies. The structure of social networks, which affects the dynamics of cooperation and other interpersonal phenomena, have common structural signatures. One of these signatures is the tendency to organize as groups. This tendency gives rise to networks with community structure, which are composed of distinct modules. In this paper, we study analytically the evolutionary game dynamics on large modular networks in the limit of weak selection. We obtain novel analytical conditions such that natural selection favours cooperation over defection. We calculate the transition point for each community to favour cooperation. We find that a critical inter-community link creation probability exists for given group density, such that the overall network supports cooperation even if individual communities inhibit it. As a byproduct, we present solutions for the critical benefit-to-cost ratio which perform with remarkable accuracy for diverse generative network models, including those with community structure and heavy-tailed degree distributions. We also demonstrate the generalizability of the results to arbitrary two-player games.

Statistical methods for constructing disease comorbidity networks from longitudinal inpatient data

Tools from network science can be utilized to study relations between diseases. Different studies focus on different types of inter-disease linkages. One of them is the comorbidity patterns derived from large-scale longitudinal data of hospital discharge records. Researchers seek to describe comorbidity relations as a network to characterize pathways of disease progressions and to predict future risks. The first step in such studies is the construction of the network itself, which subsequent analyses rest upon. There are different ways to build such a network. In this paper, we provide an overview of several existing statistical approaches in network science applicable to weighted directed networks. We discuss the differences between the null models that these models assume and their applications. We apply these methods to the inpatient data of approximately one million people, spanning approximately 17 years, pertaining to the Montreal Census Metropolitan Area. We discuss the differences in the structure of the networks built by different methods, and different features of the comorbidity relations that they extract. We also present several example applications of these methods.

Urokinase-plasminogen activator protects periodontal ligament fibroblast from oxidative induced-apoptosis and DNA damage

BACKGROUND AND OBJECTIVE

Urokinase-plasminogen activator (uPA) is a serine protease expressed at high basal level in normal gingival cervical fluid. Despite its known pathologic role in tissue proteolysis in periodontitis, little is known concerning uPA physiological function in oral tissue. Recent evidence in cancer cells has implicated the uPA system in DNA repair and anti-apoptotic pathways. This study is aimed to evaluate the protective function of urokinase against oxidative DNA damage in periodontal ligament (PDL) fibroblast, and to propose a new biological role for uPA in oral cavity.

MATERIAL AND METHODS

PDL cells were isolated from human wisdom teeth obtained from healthy donors. An oxidative stress model was created in which PDL cells were incubated with 20, 30, 40 and 60 μmol/L hydrogen peroxide. Twenty-four hours before and after peroxide treatment, cells were treated with uPA and amiloride. Cell viability was assessed by 3-(4,5-dimethylthiazol-2-yl)-2,5diphenyltetrazolium bromide assay, apoptosis by DAPI-staining and annexin V/propidium iodide assay, and DNA breaks by alkaline comet assay. For estimating DNA damage level, γ-H2AX expression was studied using flow cytometry and immunostaining.

RESULTS

The incubation of the peroxide-treated cells with uPA significantly increased cell viability and decreased apoptosis. A significant decrease in the number of γ-H2AX foci was seen at 30 μmol/L hydrogen peroxide in uPA-treated cells. uPA inhibition as a result of amiloride treatment, in turn, induced a reduction in cell viability. In addition, there was a significant decrease in the levels of DNA damage in uPA-treated groups as measured by the comet assay.

CONCLUSION

The present study brings support to the theory that uPA may have a protective role for periodontal tissue and could protect PDL fibroblasts from oxidative DNA damage and apoptosis.

Evolutionary dynamics on any population structure

Evolution occurs in populations of reproducing individuals. The structure of a population can affect which traits evolve. Understanding evolutionary game dynamics in structured populations remains difficult. Mathematical results are known for special structures in which all individuals have the same number of neighbours. The general case, in which the number of neighbours can vary, has remained open. For arbitrary selection intensity, the problem is in a computational complexity class that suggests there is no efficient algorithm. Whether a simple solution for weak selection exists has remained unanswered. Here we provide a solution for weak selection that applies to any graph or network. Our method relies on calculating the coalescence times of random walks. We evaluate large numbers of diverse population structures for their propensity to favour cooperation. We study how small changes in population structure-graph surgery-affect evolutionary outcomes. We find that cooperation flourishes most in societies that are based on strong pairwise ties.

Effect of node attributes on the temporal dynamics of network structure

Many natural and social networks evolve in time and their structures are dynamic. In most networks, nodes are heterogeneous, and their roles in the evolution of structure differ. This paper focuses on the role of individual attributes on the temporal dynamics of network structure. We focus on a basic model for growing networks that incorporates node attributes (which we call "quality"), and we focus on the problem of forecasting the structural properties of the network in arbitrary times for an arbitrary initial network. That is, we address the following question: If we are given a certain initial network with given arbitrary structure and known node attributes, then how does the structure change in time as new nodes with given distribution of attributes join the network? We solve the model analytically and obtain the quality-degree joint distribution and degree correlations. We characterize the role of individual attributes in the position of individual nodes in the hierarchy of connections. We confirm the theoretical findings with Monte Carlo simulations.

Growing multiplex networks with arbitrary number of layers

This paper focuses on the problem of growing multiplex networks. Currently, the results on the joint degree distribution of growing multiplex networks present in the literature pertain to the case of two layers and are confined to the special case of homogeneous growth and are limited to the state state (that is, the limit of infinite size). In the present paper, we first obtain closed-form solutions for the joint degree distribution of heterogeneously growing multiplex networks with arbitrary number of layers in the steady state. Heterogeneous growth means that each incoming node establishes different numbers of links in different layers. We consider both uniform and preferential growth. We then extend the analysis of the uniform growth mechanism to arbitrary times. We obtain a closed-form solution for the time-dependent joint degree distribution of a growing multiplex network with arbitrary initial conditions. Throughout, theoretical findings are corroborated with Monte Carlo simulations. The results shed light on the effects of the initial network on the transient dynamics of growing multiplex networks and takes a step towards characterizing the temporal variations of the connectivity of growing multiplex networks, as well as predicting their future structural properties.

Factors influencing analysis of prolyl endopeptidase in human blood and cerebrospinal fluid: increase in assay sensitivity

Prolyl endopeptidase (EC 3.4.21.26) (PEP) is present in nearly all investigated mammalian cells and biological fluids and might be involved in the degradation of physiologically important neuropeptides. To be able to investigate the variation of PEP in blood and cerebrospinal fluid (CSF) in human disease, the factors influencing analysis of PEP in these body fluids must be determined. The purpose of the present work was to study the influence of storage conditions, anticoagulation additives, freezing and thawing and substrate solvent on determination of PEP in blood plasma/serum and CSF. It was found that the PEP activity was about 10% higher in plasma (with EDTA and heparinate for anticoagulation) than in serum. Storage at room temperature (20 degrees C) caused a rapid decline in enzyme activity, which was smaller but still considerable at 4 degrees C. Storage at -20 degrees C and -70 degrees C did not decrease the PEP activity. Freezing and thawing of plasma/serum samples showed that the first freeze-thawing cycle produced a 20% reduction in enzyme activity but little further decrease was observed during subsequent cycles of freeze-thawing. In conclusion, PEP activity should preferably be measured within one hour after sampling using EDTA- or heparinate plasma. For long-term storage, samples should be immediately frozen and stored at -20 degrees C or colder. The selection and amount of the organic solvent used to dissolve the fluorogenic substrate strongly influenced the sensitivity of the assay. By developing an optimal solvent system an increase in assay sensitivity of about 400% could be obtained, which for the first time allowed measurement of the PEP activity in CSF.