Comparative Analyses of Pandemic H1N1 and Seasonal H1N1, H3N2, and Influenza B Infections Depict Distinct Clinical Pictures in Ferrets

Influenza A and B infections are a worldwide health concern to both humans and animals. High genetic evolution rates of the influenza virus allow the constant emergence of new strains and cause illness variation. Since human influenza infections are often complicated by secondary factors such as age and underlying medical conditions, strain or subtype specific clinical features are difficult to assess. Here we infected ferrets with 13 currently circulating influenza strains (including strains of pandemic 2009 H1N1 [H1N1pdm] and seasonal A/H1N1, A/H3N2, and B viruses). The clinical parameters were measured daily for 14 days in stable environmental conditions to compare clinical characteristics. We found that H1N1pdm strains had a more severe physiological impact than all season strains where pandemic A/California/07/2009 was the most clinically pathogenic pandemic strain. The most serious illness among seasonal A/H1N1 and A/H3N2 groups was caused by A/Solomon Islands/03/2006 and A/Perth/16/2009, respectively. Among the 13 studied strains, B/Hubei-Wujiagang/158/2009 presented the mildest clinical symptoms. We have also discovered that disease severity (by clinical illness and histopathology) correlated with influenza specific antibody response but not viral replication in the upper respiratory tract. H1N1pdm induced the highest and most rapid antibody response followed by seasonal A/H3N2, seasonal A/H1N1 and seasonal influenza B (with B/Hubei-Wujiagang/158/2009 inducing the weakest response). Our study is the first to compare the clinical features of multiple circulating influenza strains in ferrets. These findings will help to characterize the clinical pictures of specific influenza strains as well as give insights into the development and administration of appropriate influenza therapeutics

On the possibility of a metallic phase in granular superconducting films

We investigate the possibility of finding a zero-temperature metallic phase in granular superconducting films. We are able to identify the breakdown of the conventional treatment of these systems as dissipative Bose systems. We do not find a metallic state at zero temperature. At finite temperatures, we find that the system exhibit crossover behaviour which may have implications for the analysis of experimental results. We also investigate the effect of vortex dissipation in these systems.Comment: 7 pages, ReVTeX3.0, 3 EPS figure

Prediction of Dengue Incidence Using Search Query Surveillance

Improvements in surveillance, prediction of outbreaks and the monitoring of the epidemiology of dengue virus in countries with underdeveloped surveillance systems are of great importance to ministries of health and other public health decision makers who are often constrained by budget or man-power. Google Flu Trends has proven successful in providing an early warning system for outbreaks of influenza weeks before case data are reported. We believe that there is greater potential for this technique for dengue, as the incidence of this pathogen can vary by a factor of ten in some settings, making prediction all the more important in public health planning. In this paper, we demonstrate the utility of Google search terms in predicting dengue incidence in Singapore and Bangkok, Thailand using several regression techniques. Incidence data were provided by the Singapore Ministry of Health and the Thailand Bureau of Epidemiology. We find our models predict incident cases well (correlation greater than 0.8) and periods of high incidence equally well (AUC greater than 0.95). All data and analysis code used in our study are available free online and can be adapted to other settings

Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model

Anthropogenic activities are causing widespread degradation of ecosystems worldwide, threatening the ecosystem services upon which all human life depends. Improved understanding of this degradation is urgently needed to improve avoidance and mitigation measures. One tool to assist these efforts is predictive models of ecosystem structure and function that are mechanistic: based on fundamental ecological principles. Here we present the first mechanistic General Ecosystem Model (GEM) of ecosystem structure and function that is both global and applies in all terrestrial and marine environments. Functional forms and parameter values were derived from the theoretical and empirical literature where possible. Simulations of the fate of all organisms with body masses between 10 µg and 150,000 kg (a range of 14 orders of magnitude) across the globe led to emergent properties at individual (e.g., growth rate), community (e.g., biomass turnover rates), ecosystem (e.g., trophic pyramids), and macroecological scales (e.g., global patterns of trophic structure) that are in general agreement with current data and theory. These properties emerged from our encoding of the biology of, and interactions among, individual organisms without any direct constraints on the properties themselves. Our results indicate that ecologists have gathered sufficient information to begin to build realistic, global, and mechanistic models of ecosystems, capable of predicting a diverse range of ecosystem properties and their response to human pressures

Fisher Information as a Metric of Locally Optimal Processing and Stochastic Resonance

The origins of Fisher information are in its use as a performance measure for parametric estimation. We augment this and show that the Fisher information can characterize the performance in several other significant signal processing operations. For processing of a weak signal in additive white noise, we demonstrate that the Fisher information determines (i) the maximum output signal-to-noise ratio for a periodic signal; (ii) the optimum asymptotic efficacy for signal detection; (iii) the best cross-correlation coefficient for signal transmission; and (iv) the minimum mean square error of an unbiased estimator. This unifying picture, via inequalities on the Fisher information, is used to establish conditions where improvement by noise through stochastic resonance is feasible or not

Adiposity in early, middle and later adult life and cardiometabolic risk markers in later life; findings from the British regional heart study.

OBJECTIVES: This research investigates the associations between body mass index (BMI) at 21, 40-59, 60-79 years of age on cardiometabolic risk markers at 60-79 years. METHODS: A prospective study of 3464 British men with BMI measured at 40-59 and 60-79 years, when cardiometabolic risk was assessed. BMI at 21 years was ascertained from military records, or recalled from middle-age (adjusted for reporting bias); associations between BMI at different ages and later cardiometabolic risk markers were examined using linear regression. Sensitive period, accumulation and mobility life course models were devised for high BMI (defined as BMI≥75th centile) and compared with a saturated BMI trajectory model. RESULTS: At ages 21, 40-59 and 60-79 years, prevalences of overweight (BMI≥25 kg/m2) were 12%, 53%, 70%, and obesity (≥30 kg/m2) 1.6%, 6.6%, and 17.6%, respectively. BMI at 21 years was positively associated with serum insulin, blood glucose, and HbA1c at 60-79 years, with increases of 1.5% (95%CI 0.8,2.3%), 0.4% (0.1,0.6%), 0.3% (0.1,0.4%) per 1 kg/m2, respectively, but showed no associations with blood pressure or blood cholesterol. However, these associations were modest compared to those between BMI at 60-79 years and serum insulin, blood glucose and HbA1c at 60-79 years, with increases of 8.6% (8.0,9.2%), 0.7% (0.5,0.9%), and 0.5% (0.4,0.7%) per 1 kg/m2, respectively. BMI at 60-79 years was also associated with total cholesterol and blood pressure. Associations for BMI at 40-59 years were mainly consistent with those of BMI at 60-79 years. None of the life course models fitted the data as well as the saturated model for serum insulin. A sensitive period at 50 years for glucose and HbA1c and sensitive period at 70 years for blood pressure were identified. CONCLUSIONS: In this cohort of men who were thin compared to more contemporary cohorts, BMI in later life was the dominant influence on cardiovascular and diabetes risk. BMI in early adult life may have a small long-term effect on diabetes risk

Past, present and future mathematical models for buildings (i)

This is the first of two articles presenting a detailed review of the historical evolution of mathematical models applied in the development of building technology, including conventional buildings and intelligent buildings. After presenting the technical differences between conventional and intelligent buildings, this article reviews the existing mathematical models, the abstract levels of these models, and their links to the literature for intelligent buildings. The advantages and limitations of the applied mathematical models are identified and the models are classified in terms of their application range and goal. We then describe how the early mathematical models, mainly physical models applied to conventional buildings, have faced new challenges for the design and management of intelligent buildings and led to the use of models which offer more flexibility to better cope with various uncertainties. In contrast with the early modelling techniques, model approaches adopted in neural networks, expert systems, fuzzy logic and genetic models provide a promising method to accommodate these complications as intelligent buildings now need integrated technologies which involve solving complex, multi-objective and integrated decision problems

Past, present and future mathematical models for buildings (ii)

This article is the second part of a review of the historical evolution of mathematical models applied in the development of building technology. The first part described the current state of the art and contrasted various models with regard to the applications to conventional buildings and intelligent buildings. It concluded that mathematical techniques adopted in neural networks, expert systems, fuzzy logic and genetic models, that can be used to address model uncertainty, are well suited for modelling intelligent buildings. Despite the progress, the possible future development of intelligent buildings based on the current trends implies some potential limitations of these models. This paper attempts to uncover the fundamental limitations inherent in these models and provides some insights into future modelling directions, with special focus on the techniques of semiotics and chaos. Finally, by demonstrating an example of an intelligent building system with the mathematical models that have been developed for such a system, this review addresses the influences of mathematical models as a potential aid in developing intelligent buildings and perhaps even more advanced buildings for the future

Early Emergence of Ethnic Differences in Type 2 Diabetes Precursors in the UK: The Child Heart and Health Study in England (CHASE Study)

Peter Whincup and colleagues carry out a cross-sectional study examining ethnic differences in precursors of of type 2 diabetes among children aged 9–10 living in three UK cities