Inhibiting diffusion of complex contagions in social networks: theoretical and experimental results.

We consider the problem of inhibiting undesirable contagions (e.g. rumors, spread of mob behavior) in social networks. Much of the work in this context has been carried out under the 1-threshold model, where diffusion occurs when a node has just one neighbor with the contagion. We study the problem of inhibiting more complex contagions in social networks where nodes may have thresholds larger than 1. The goal is to minimize the propagation of the contagion by removing a small number of nodes (called critical nodes) from the network. We study several versions of this problem and prove that, in general, they cannot even be efficiently approximated to within any factor ρ ≥ 1, unless P = NP. We develop efficient and practical heuristics for these problems and carry out an experimental study of their performance on three well known social networks, namely epinions, wikipedia and slashdot. Our results show that these heuristics perform significantly better than five other known methods. We also establish an efficiently computable upper bound on the number of nodes to which a contagion can spread and evaluate this bound on many real and synthetic networks.

Establishment of cell suspension culture in Marchantia linearis Lehm & Lindenb. for the optimum production of flavonoids.

Bryophytes are the second largest group in the plant kingdom, but studies conducted to better understand their chemical composition are limited and scattered. Axenically grown bryophytes expressed potential in biotechnological processes. The present study was designed to investigate the in vitro cell growth, culture parameters and their effect on flavonoid synthesis. Chlorophyll-containing callus cells of Marchantia linearis Lehm & Lindenb. is able to grow under low light in the presence of organic carbon source and retain the ability to produce flavonoids. Highest flavonoid production was achieved using 2,4-dichlorophenoxyacetic acid as growth hormone. Inoculum size, light intensity, organic carbon source and cations are the culture parameters affecting flavonoid productivity. Maximum flavonoid productivity is observed under low light intensity, with a photon flux density ca. 20 µmol/m2/s. Optimal inoculum size and glucose concentration for flavonoid production are 10-14 and 2-3 %, respectively. Cations like ferrous trigger flavonoid synthesis by increasing its intracellular concentrations. Flavonoid production in the cell culture is shown to be significantly growth related. Osmotic stress is ineffective in triggering flavonoid synthesis. Methyl jasmonate and 2-(2-fluoro-6-nitrobenzylsulfanyl) pyridine-4-carbothioamide elicitors showed positive effect on intracellular flavonoid content in cultured cells. Using the standard plot of quercetin (y = 0.0148x, R2 = 0.975), the flavonoid contents of in vitro samples were found ranging from 4.0 to 17.7 mg quercetin equivalent/g tissue. Flavonoids are fractionated by HPLC-PAD revealed the presence of quercetin (182.5 µg/g), luteolin (464.5 µg/g) and apigenin (297.5 µg/g). Further studies are warranted to analyze the therapeutic potentiality of the flavonoids in the liverwort.

Inferring population-level contact heterogeneity from common epidemic data.

Models of infectious disease spread that incorporate contact heterogeneity through contact networks are an important tool for epidemiologists studying disease dynamics and assessing intervention strategies. One of the challenges of contact network epidemiology has been the difficulty of collecting individual and population-level data needed to develop an accurate representation of the underlying host population's contact structure. In this study, we evaluate the utility of common epidemiological measures (R0, epidemic peak size, duration and final size) for inferring the degree of heterogeneity in a population's unobserved contact structure through a Bayesian approach. We test the method using ground truth data and find that some of these epidemiological metrics are effective at classifying contact heterogeneity. The classification is also consistent across pathogen transmission probabilities, and so can be applied even when this characteristic is unknown. In particular, the reproductive number, R0, turns out to be a poor classifier of the degree heterogeneity, while, unexpectedly, final epidemic size is a powerful predictor of network structure across the range of heterogeneity. We also evaluate our framework on empirical epidemiological data from past and recent outbreaks to demonstrate its application in practice and to gather insights about the relevance of particular contact structures for both specific systems and general classes of infectious disease. We thus introduce a simple approach that can shed light on the unobserved connectivity of a host population given epidemic data. Our study has the potential to inform future data-collection efforts and study design by driving our understanding of germane epidemic measures, and highlights a general inferential approach to learning about host contact structure in contemporary or historic populations of humans and animals.

From biological and social network metaphors to coupled bio-social wireless networks.

Biological and social analogies have been long applied to complex systems. Inspiration has been drawn from biological solutions to solve problems in engineering products and systems, ranging from Velcro to camouflage to robotics to adaptive and learning computing methods. In this paper, we present an overview of recent advances in understanding biological systems as networks and use this understanding to design and analyse wireless communication networks. We expand on two applications, namely cognitive sensing and control and wireless epidemiology. We discuss how our work in these two applications is motivated by biological metaphors. We believe that recent advances in computing and communications coupled with advances in health and social sciences raise the possibility of studying coupled bio-social communication networks. We argue that we can better utilise the advances in our understanding of one class of networks to better our understanding of the other.