The Effect of Hypoxia on Brain Cell Proliferation in Weakly Electric Fish, Petrocephalus degeni

Oxygen levels tend to remain at a steady state concentration in the Earth’s atmosphere, yet in some bodies of water, they can fluctuate and decrease drastically. Many organisms that inhabit the swamps, lakes, streams, and parts of the ocean where this occurs have evolved adaptations to manage this environmental uncertainty and continue normal oxygen consumption. The Lwamunda swamp in Uganda is chronically hypoxic, yet it is home to many species, including the electric fish Petrocephalus degeni. P. degeni are unusual by nature of their immense brain, and the Lwamunda swamp appears ill-suited for maintaining this large, metabolically active organ. To determine the possible mechanisms P. degeni employ for survival and brain maintenance in this hypoxic swamp, 33 individuals were collected aiming to analyze their brain cell proliferation. One-third were immediately sacrificed, and two-thirds were transported to a laboratory and divided into hypoxic and normoxic environments for two weeks. All brains were collected, and new brain cell proliferation was quantified using PCNA immunohistochemistry. P. degeni from the hypoxic lab condition showed significantly fewer PCNA+ cells than their conspecifics in normoxic water, and individuals harvested directly from the field showed the overall highest density of PCNA+ brain cells. Our results suggest that hypoxia and captivity negatively impacted brain cell growth in P. degeni. The activation of hypoxia-inducible factors (HIFs) likely mediated this reduction in brain cell proliferation and the corresponding oxygen demand. Despite showing a reduction in new brain cell growth, P. degeni remains capable of surviving and maintaining their large brain in extremely hypoxic conditions

Redes de associações de grupos camponeses na Amazônia Oriental (Brasil) : fontes de capital social?

Na Amazônia Oriental, tem crescido o número de associações de grupos camponeses, especialmente a partir dos anos noventa do século passado. Elas buscam meios alternativos para lidar com interesses comuns e organizar esforços individuais e coletivos e, desse modo, contribuir com a redução das desigualdades. Este estudo baseia-se em dados de entrevistas com líderes de trinta e seis associações rurais em três municípios do nordeste do Estado do Pará. Examina em que medida essas organizações representavam formas de capital social, isto é, redes capazes de produzir e prover acesso a recursos do ponto de vista dos grupos locais. O ambiente dessas redes é analisado, enfatizando-se os conjuntos de relações que mantinham com associações similares, ONGs, instituições governamentais, movimentos sociais, sindicatos e políticos. Se dispor de conexões sociais era fator crucial para as associações alcançarem os objetivos imediatos, constatou-se que elas não eram em geral suficientes para se sobreporem às restrições dos contextos. Barreiras concretas à comunicação reduziam a habilidade da rede em difundir os recursos imersos ou acessíveis.In the northeastern Amazon region of Brazil there has been a notable proliferation of peasants' associations, particularly from the 1990s. They aim to provide alternative means to deal with common interests and to organize individual and group efforts, thus contributing to reduced inequalities. This study draws on interviews with leaders of thirty-six rural associations in three municipalities in the State of Pará. The paper considers the extent to which these local organizations represented forms of social capital, that is, social networks able to produce and to provide access to valued resources. The network environment of the associations is examined, with particular reference to their external links with similar associations, NGOs, government institutions, social movements, unions and politicians. Social connections appeared significant for the goals of the associations. Nevertheless, concrete barriers to communication reduced the network ability to spread the resources embedded or accessible

Demographic, risk behaviour and personal network variables associated with prevalent hepatitis C, hepatitis B, and HIV infection in injection drug users in Winnipeg, Canada

BACKGROUND: Previous studies have used social network variables to improve our understanding of HIV transmission. Similar analytic approaches have not been undertaken for hepatitis C (HCV) or B (HBV), nor used to conduct comparative studies on these pathogens within a single setting. METHODS: A cross-sectional survey consisting of a questionnaire and blood sample was conducted on injection drug users in Winnipeg between December 2003 and September 2004. Logistic regression analyses were used to correlate respondent and personal network data with HCV, HBV and HIV prevalence. RESULTS: At the multivariate level, pathogen prevalence was correlated with both respondent and IDU risk network variables. Pathogen transmission was associated with several distinct types of high-risk networks formed around specific venues (shooting galleries, hotels) or within users who are linked by their drug use preferences. Smaller, isolated pockets of IDUs also appear to exist within the larger population where behavioural patterns pose a lesser risk, unless or until, a given pathogen enters those networks. CONCLUSION: The findings suggest that consideration of both respondent and personal network variables can assist in understanding the transmission patterns of HCV, HBV, and HIV. It is important to assess these effects for multiple pathogens within one setting as the associations identified and the direction of those associations can differ between pathogens

Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models

Exponential-family random graph models (ERGMs) provide a principled way to model and simulate features common in human social networks, such as propensities for homophily and friend-of-a-friend triad closure. We show that, without adjustment, ERGMs preserve density as network size increases. Density invariance is often not appropriate for social networks. We suggest a simple modification based on an offset which instead preserves the mean degree and accommodates changes in network composition asymptotically. We demonstrate that this approach allows ERGMs to be applied to the important situation of egocentrically sampled data. We analyze data from the National Health and Social Life Survey (NHSLS).Comment: 37 pages, 2 figures, 5 tables; notation revised and clarified, some sections (particularly 4.3 and 5) made more rigorous, some derivations moved into the appendix, typos fixed, some wording change

Bias reduction in traceroute sampling: towards a more accurate map of the Internet

Traceroute sampling is an important technique in exploring the internet router graph and the autonomous system graph. Although it is one of the primary techniques used in calculating statistics about the internet, it can introduce bias that corrupts these estimates. This paper reports on a theoretical and experimental investigation of a new technique to reduce the bias of traceroute sampling when estimating the degree distribution. We develop a new estimator for the degree of a node in a traceroute-sampled graph; validate the estimator theoretically in Erdos-Renyi graphs and, through computer experiments, for a wider range of graphs; and apply it to produce a new picture of the degree distribution of the autonomous system graph.Comment: 12 pages, 3 figure

The spread of epidemic disease on networks

The study of social networks, and in particular the spread of disease on networks, has attracted considerable recent attention in the physics community. In this paper, we show that a large class of standard epidemiological models, the so-called susceptible/infective/removed (SIR) models can be solved exactly on a wide variety of networks. In addition to the standard but unrealistic case of fixed infectiveness time and fixed and uncorrelated probability of transmission between all pairs of individuals, we solve cases in which times and probabilities are non-uniform and correlated. We also consider one simple case of an epidemic in a structured population, that of a sexually transmitted disease in a population divided into men and women. We confirm the correctness of our exact solutions with numerical simulations of SIR epidemics on networks.Comment: 12 pages, 3 figure

Mapping the Distribution of Invasive Staphylococcus aureus across Europe

Franklin Lowy discusses a new study in PLoS Medicine in which the investigators developed an interactive tool for analyzing the spatial distribution of invasive Staphylococcus aureus

Equal Graph Partitioning on Estimated Infection Network as an Effective Epidemic Mitigation Measure

Controlling severe outbreaks remains the most important problem in infectious disease area. With time, this problem will only become more severe as population density in urban centers grows. Social interactions play a very important role in determining how infectious diseases spread, and organization of people along social lines gives rise to non-spatial networks in which the infections spread. Infection networks are different for diseases with different transmission modes, but are likely to be identical or highly similar for diseases that spread the same way. Hence, infection networks estimated from common infections can be useful to contain epidemics of a more severe disease with the same transmission mode. Here we present a proof-of-concept study demonstrating the effectiveness of epidemic mitigation based on such estimated infection networks. We first generate artificial social networks of different sizes and average degrees, but with roughly the same clustering characteristic. We then start SIR epidemics on these networks, censor the simulated incidences, and use them to reconstruct the infection network. We then efficiently fragment the estimated network by removing the smallest number of nodes identified by a graph partitioning algorithm. Finally, we demonstrate the effectiveness of this targeted strategy, by comparing it against traditional untargeted strategies, in slowing down and reducing the size of advancing epidemics

Six challenges in measuring contact networks for use in modelling.

Contact networks are playing an increasingly important role in epidemiology. A contact network represents individuals in a host population as nodes and the interactions among them that may lead to the transmission of infection as edges. New avenues for data collection in recent years have afforded us the opportunity to collect individual- and population-scale information to empirically describe the patterns of contact within host populations. Here, we present some of the current challenges in measuring empirical contact networks. We address fundamental questions such as defining contact; measurement of non-trivial contact properties; practical issues of bounding measurement of contact networks in space, time and scope; exploiting proxy information about contacts; dealing with missing data. Finally, we consider the privacy and ethical issues surrounding the collection of contact network data

Recruiting Injection Drug Users: A Three-Site Comparison of Results and Experiences with Respondent-Driven and Targeted Sampling Procedures

Several recent studies have utilized respondent-driven sampling (RDS) methods to survey hidden populations such as commercial sex-workers, men who have sex with men (MSM) and injection drug users (IDU). Few studies, however, have provided a direct comparison between RDS and other more traditional sampling methods such as venue-based, targeted or time/space sampling. The current study sampled injection drug users in three U.S. cities using RDS and targeted sampling (TS) methods and compared their effectiveness in terms of recruitment efficiency, logistics, and sample demographics. Both methods performed satisfactorily. The targeted method required more staff time per-recruited respondent and had a lower proportion of screened respondents who were eligible than RDS, while RDS respondents were offered higher incentives for participation