Social networks of disease spread in the lower Illinois valley: a simulation approach

This study illustrates the use of disease modeling and simulation techniques to the study of the spread of disease within and between social networks. A Reed-Frost type model of disease spread is used to construct a simulation of the spread of tuberculosis within three prehistoric populations of the Lower Illinois River Valley during Middle Woodland, Late Woodland, and Mississippian times. A high and low population size was modeled for each time period. Late Woodland model 2 (low population estimate) is the only model that experienced pathogen extinction with host survival. The rest of the models experienced rapid and severe host population decline. The results of the simulation suggest that a social network size of between 180 and 440 persons is required under the conditions of this model for host-pathogen coexistence (i.e., endemicity) to occur. The severe population decline experienced by these populations suggests that tuberculosis as modeled here could not have existed in these populations. Future refinements of modeling and simulation techniques can provide additional insights into how disease spreads among social contacts.     

Chronic contamination decreases disease spread: a Daphnia-fungus-copper case study

Chemical contamination and disease outbreaks have increased in many ecosystems. However, connecting pollution to disease spread remains difficult, in part, because contaminants can simultaneously exert direct and multi-generational effects on several host and parasite traits. To address these challenges, we parametrized a model using a zooplankton-fungus-copper system. In individual-level assays, we considered three sublethal contamination scenarios: no contamination, single-generation contamination (hosts and parasites exposed only during the assays) and multi-generational contamination (hosts and parasites exposed for several generations prior to and during the assays). Contamination boosted transmission by increasing contact of hosts with parasites. However, it diminished parasite reproduction by reducing the size and lifespan of infected hosts. Multi-generational contamination further reduced parasite reproduction. The parametrized model predicted that a single generation of contamination would enhance disease spread (via enhanced transmission), whereas multi-generational contamination would inhibit epidemics relative to unpolluted conditions (through greatly depressed parasite reproduction). In a population-level experiment, multi-generational contamination reduced the size of experimental epidemics but did not affect Daphnia populations without disease. This result highlights the importance of multi-generational effects for disease dynamics. Such integration of models with experiments can provide predictive power for disease problems in contaminated environments.     

Modelling disease spread and control in networks: implications for plant sciences

Networks are ubiquitous in natural, technological and social systems. They are of increasing relevance for improved understanding and control of infectious diseases of plants, animals and humans, given the interconnectedness of today's world. Recent modelling work on disease development in complex networks shows: the relative rapidity of pathogen spread in scale-free compared with random networks, unless there is high local clustering; the theoretical absence of an epidemic threshold in scale-free networks of infinite size, which implies that diseases with low infection rates can spread in them, but the emergence of a threshold when realistic features are added to networks (e.g. finite size, household structure or deactivation of links); and the influence on epidemic dynamics of asymmetrical interactions. Models suggest that control of pathogens spreading in scale-free networks should focus on highly connected individuals rather than on mass random immunization. A growing number of empirical applications of network theory in human medicine and animal disease ecology confirm the potential of the approach, and suggest that network thinking could also benefit plant epidemiology and forest pathology, particularly in human-modified pathosystems linked by commercial transport of plant and disease propagules. Potential consequences for the study and management of plant and tree diseases are discussed.     

Phospholipid analysis as a tool to study complex microbial communities in marine sediments.

To complement information on microbial communities in marine sediments that can be obtained using microbiological methods, we developed an analytical procedure to trace microbial lipids in environmental samples. We focused on analyzing intact phospholipids as these membrane constituents are known to be biomarkers for viable cells. Analysis of intact phospholipids from a fractionated and preconcentrated sediment extract was achieved using liquid chromatography-electrospray ionization-mass spectrometry (HPLC-ESI-MS). The combined analysis of phospholipid types and their fatty acid substituents allowed a differentiation between various groups of microorganisms living in the sediment. For comparison three strains of marine sulfate-reducing bacteria (SRB) were analysed for their lipid content.     

Kinetics: a tool to study molecular motors

Molecular motors are enzymes that couple the energy from nucleoside triphosphate hydrolysis to movement along a filament lattice. The three cytoskeletal motor superfamilies include myosin, dynein, and kinesin. However, in the last decade it has become apparent that the nucleic acid-based enzymes (DNA and RNA polymerases as well as the DNA helicases) share a number of mechanistic features in common with the microtubule and actin motors despite the fact that their cellular functions are so different. This review addresses the mechanistic approaches that have been used to study molecular motors. We discuss the basic biochemical techniques used to characterize a protein preparation, including active site determination and steady-state kinetics. In addition, we present the transient-state kinetic approaches used to define a mechanochemical cycle. We attempt to integrate the information obtained from kinetic studies within the context of motility results to provide a better understanding of the contribution of each approach for dissecting unidirectional force generation.     

AFLP markers as a tool to reconstruct complex relationships: A case study in Rosa (Rosaceae)

The genus Rosa has a complex evolutionary history caused by several factors, often in conjunction: extensive hybridization, recent radiation, incomplete lineage sorting, and multiple events of polyploidy. We examined the applicability of AFLP markers for reconstructing (species) relationships in Rosa, using UPGMA clustering, Wagner parsimony, and Bayesian inference. All trees were well resolved, but many of the deeper branches were weakly supported. The cluster analysis showed that the rose cultivars can be separated into a European and an Oriental cluster, each being related to different wild species. The phylogenetic analyses showed that (1) two of the four subgenera (Hulthemia and Platyrhodon) do not deserve subgeneric status; (2) section Carolinae should be merged with sect. Cinnamomeae; (3) subsection Rubigineae is a monophyletic group within sect. Caninae, making sect. Caninae paraphyletic; and (4) there is little support for the distinction of the five other subsections within sect. Caninae. Comparison of the trees with morphological classifications and with previous molecular studies showed that all methods yielded reliable trees. Bayesian inference proved to be a useful alternative to parsimony analysis of AFLP data. Because of their genome-wide sampling, AFLPs are the markers of choice to reconstruct (species) relationships in evolutionary complex groups.     

The dynamics of sexual contact networks: effects on disease spread and control

Sexually transmitted pathogens persist in populations despite the availability of biomedical interventions and knowledge of behavioural changes that would reduce individual-level risk. While behavioural risk factors are shared between many sexually transmitted infections, the prevalence of these diseases across different risk groups varies. Understanding this heterogeneity and identifying better control strategies depends on an improved understanding of the complex social contact networks over which pathogens spread. To date, most efforts to study the impact of sexual network structure on disease dynamics have focused on static networks. However, the interaction between the dynamics of partnership formation and dissolution and the dynamics of transmission plays a role, both in restricting the effective network accessible to the pathogen, and in modulating the transmission dynamics. We present a simple method to simulate dynamical networks of sexual partnerships. We inform the model using survey data on sexual attitudes and lifestyles, and investigate how the duration of infectiousness changes the effective contact network over which disease may spread. We then simulate several control strategies: screening, vaccination and behavioural interventions. Previous theory and research has advanced the importance of core groups for spread and control of STD. Our work is consistent with the importance of core groups, but extends this idea to consider how the duration of infectiousness associated with a particular pathogen interacts with host behaviours to define these high risk subpopulations. Characteristics of the parts of the network accessible to the pathogen, which represent the network structure of sexual contacts from the “point of view” of the pathogen, are substantially different from those of the network as a whole. The pathogen itself plays an important role in determining this effective network structure; specifically, we find that if the pathogen’s duration of infectiousness is short, infection is more concentrated in high-activity, high-concurrency individuals even when all other factors are held constant. Widespread screening programmes would be enhanced by follow-up interventions targeting higher-risk individuals, because screening shortens the expected duration of infectiousness and causes a greater relative decrease in prevalence among lower-activity than in higher-activity individuals. Even for pathogens with longer durations of infectiousness, our findings suggest that targeting vaccination and behavioural interventions towards high-activity individuals provides comparable benefits to population-wide interventions.     

Replicating disease spread in empirical cattle networks by adjusting the probability of infection in random networks

Comparisons between mass-action or “random” network models and empirical networks have produced mixed results. Here we seek to discover whether a simulated disease spread through randomly constructed networks can be coerced to model the spread in empirical networks by altering a single disease parameter — the probability of infection. A stochastic model for disease spread through herds of cattle is utilised to model the passage of an SEIR (susceptible–latent–infected–resistant) through five networks. The first network is an empirical network of recorded contacts, from four datasets available, and the other four networks are constructed from randomly distributed contacts based on increasing amounts of information from the recorded network. A numerical study on adjusting the value of the probability of infection was conducted for the four random network models. We found that relative percentage reductions in the probability of infection, between 5.6% and 39.4% in the random network models, produced results that most closely mirrored the results from the empirical contact networks. In all cases tested, to reduce the differences between the two models, required a reduction in the probability of infection in the random network.     

Revealing mechanisms of infectious disease spread through empirical contact networks

The spread of pathogens fundamentally depends on the underlying contacts between individuals. Modeling the dynamics of infectious disease spread through contact networks, however, can be challenging due to limited knowledge of how an infectious disease spreads and its transmission rate. We developed a novel statistical tool, INoDS (Identifying contact Networks of infectious Disease Spread) that estimates the transmission rate of an infectious disease outbreak, establishes epidemiological relevance of a contact network in explaining the observed pattern of infectious disease spread and enables model comparison between different contact network hypotheses. We show that our tool is robust to incomplete data and can be easily applied to datasets where infection timings of individuals are unknown. We tested the reliability of INoDS using simulation experiments of disease spread on a synthetic contact network and find that it is robust to incomplete data and is reliable under different settings of network dynamics and disease contagiousness compared with previous approaches. We demonstrate the applicability of our method in two host-pathogen systems: Crithidia bombi in bumblebee colonies and Salmonella in wild Australian sleepy lizard populations. INoDS thus provides a novel and reliable statistical tool for identifying transmission pathways of infectious disease spread. In addition, application of INoDS extends to understanding the spread of novel or emerging infectious disease, an alternative approach to laboratory transmission experiments, and overcoming common data-collection constraints.     

HBV转基因小鼠--乙型肝炎研究的重要工具

HBV感染具有严格的种属特异性和组织亲嗜性,所以可用于研究的动物模型很少。随着转基因技术的出现和发展,人们通过研制转基因动物模型来研究病毒致病机理。把HBV的DNA或其片段转入小鼠受精卵建立起来的HBV转基因小鼠,已被广泛地应用于乙型肝炎的研究中,主要体现在以下3个方面：研究HBV的致病机制、与肝细胞癌的关系及寻找、评价治疗乙肝的方法。     

HuC:Kaede, a useful tool to label neural morphologies in networks in vivo

A simple and efficient procedure for labeling neurons is a prerequisite for investigating the development of neural networks in zebrafish. To label neurons we used Kaede, a fluorescent protein with a photoconversion property allowing conversion from green to red fluorescence following irradiation with UV or violet light. We established a zebrafish stable transgenic line, Tg(HuC:Kaede), expressing Kaede in neurons under the control of the HuC promoter. This transgenic line was used to label a small number of neurons in the trigeminal ganglion. Also, using embryos injected with the transgene, we labeled peripheral axon arbors of a Rohon-Beard neuron at 4 days postfertilization and observed the dendrite development of a tectal neuron for 3 days. These data indicate that Kaede is a useful tool to selectively label neural networks in zebrafish.     

BioPP: a tool for web-publication of biological networks

Background  

Cellular processes depend on the function of intracellular molecular networks. The curation of the literature relevant to specific biological pathways is important for many theoretical and experimental research teams and communities. No current tool supports web publication or hosting of user-developed large scale annotated pathway diagrams. Sharing via web publication is needed to allow real-time access to the current literature pathway knowledgebase, both privately within a research team or publicly among the outside research community. Web publication also facilitates team and/or community input into the curation process while allowing centralized control of the curation and validation process. We have developed new tool to address these needs. Biological Pathway Publisher (BioPP) is a software suite for converting CellDesigner Systems Biology Markup Language (CD-SBML) formatted pathways into a web viewable format. The BioPP suite is available for private use and for depositing knowledgebases into a newly created public repository.     

Disrupted small-world brain networks in moderate Alzheimer's disease: a resting-state FMRI study

The small-world organization has been hypothesized to reflect a balance between local processing and global integration in the human brain. Previous multimodal imaging studies have consistently demonstrated that the topological architecture of the brain network is disrupted in Alzheimer's disease (AD). However, these studies have reported inconsistent results regarding the topological properties of brain alterations in AD. One potential explanation for these inconsistent results lies with the diverse homogeneity and distinct progressive stages of the AD involved in these studies, which are thought to be critical factors that might affect the results. We investigated the topological properties of brain functional networks derived from resting functional magnetic resonance imaging (fMRI) of carefully selected moderate AD patients and normal controls (NCs). Our results showed that the topological properties were found to be disrupted in AD patients, which showing increased local efficiency but decreased global efficiency. We found that the altered brain regions are mainly located in the default mode network, the temporal lobe and certain subcortical regions that are closely associated with the neuropathological changes in AD. Of note, our exploratory study revealed that the ApoE genotype modulates brain network properties, especially in AD patients.