Since the emergence of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and Middle East Respiratory Syndrom Coronavirus (MERS-CoV) it has become increasingly clear that bats are important reservoirs of CoVs. Despite this, only 6% of all CoV sequences in GenBank are from bats. The remaining 94% largely consist of known pathogens of public health or agricultural significance, indicating that current research effort is heavily biased towards describing known diseases rather than the ‘pre-emergent’ diversity in bats. Our study addresses this critical gap, and focuses on resource poor countries where the risk of zoonotic emergence is believed to be highest. We surveyed the diversity of CoVs in multiple host taxa from twenty countries to explore the factors driving viral diversity at a global scale. We identified sequences representing 100 discrete phylogenetic clusters, ninety-one of which were found in bats, and used ecological and epidemiologic analyses to show that patterns of CoV diversity correlate with those of bat diversity. This cements bats as the major evolutionary reservoirs and ecological drivers of CoV diversity. Co-phylogenetic reconciliation analysis was also used to show that host switching has contributed to CoV evolution, and a preliminary analysis suggests that regional variation exists in the dynamics of this process. Overall our study represents a model for exploring global viral diversity and advances our fundamental understanding of CoV biodiversity and the potential risk factors associated with zoonotic emergence.

Most human infectious diseases, especially recently emerging pathogens, originate from animals, and ongoing disease transmission from animals to people presents a significant global health burden. Recognition of the epidemiologic circumstances involved in zoonotic spillover, amplification, and spread of diseases is essential for prioritizing surveillance and predicting future disease emergence risk. We examine the animal hosts and transmission mechanisms involved in spillover of zoonotic viruses to date, and discover that viruses with high host plasticity (i.e. taxonomically and ecologically diverse host range) were more likely to amplify viral spillover by secondary human-to-human transmission and have broader geographic spread. Viruses transmitted to humans during practices that facilitate mixing of diverse animal species had significantly higher host plasticity. Our findings suggest that animal-to-human spillover of new viruses that are capable of infecting diverse host species signal emerging disease events with higher pandemic potential in that these viruses are more likely to amplify by human-to-human transmission with spread on a global scale.

Researchers and wildlife managers increasingly find themselves in situations where they must deal with infectious wildlife diseases such as chronic wasting disease, brucellosis, tuberculosis, and West Nile virus. Managers are often charged with designing and implementing control strategies, and researchers often seek to determine factors that influence and control the disease process. All of these activities require the ability to measure some indication of a disease's foothold in a population and evaluate factors affecting that foothold. The most common type of data available to managers and researchers is apparent prevalence data. Apparent disease prevalence, the proportion of animals in a sample that are positive for the disease, might seem like a natural measure of disease's foothold, but several properties, in particular, its dependency on age structure and the biasing effects of disease-associated mortality, make it less than ideal. In quantitative epidemiology, the "force of infection," or infection hazard, is generally the preferred parameter for measuring a disease's foothold, and it can be viewed as the most appropriate way to "adjust" apparent prevalence for age structure. The typical ecology curriculum includes little exposure to quantitative epidemiological concepts such as cumulative incidence, apparent prevalence, and the force of infection. The goal of this paper is to present these basic epidemiological concepts and resulting models in an ecological context and to illustrate how they can be applied to understand and address basic epidemiological questions. We demonstrate a practical approach to solving the heretofore intractable problem of fitting general force-of-infection models to wildlife prevalence data using a generalized regression approach. We apply the procedures to Mycobacterium bovis (bovine tuberculosis) prevalence in bison (Bison bison) in Wood Buffalo National Park, Canada, and demonstrate strong age dependency in the force of infection as well as an increased mortality hazard in positive animals.

Among the new photovoltaic technologies, the Dye-Sensitized Solar Cell (DSC) is becoming a realistic approach towards energy markets such as BIPV (Building Integrated PhotoVoltaics). In order to improve the performances of DSCs and to increase their commercial attractiveness, cheap, colourful, stable and highly efficient ruthenium-free dyes must be developed. Here we report the synthesis and complete characterization of a new purely organic sensitizer (RK1) that can be prepared and synthetically upscaled rapidly. Solar cells containing this orange dye show a power conversion efficiency of 10.2% under standard conditions (AM 1.5G, 1000 Wm−2) using iodine/iodide as the electrolyte redox shuttle in the electrolyte, which is among the few examples of DSC using an organic dyes and iodine/iodide red/ox pair to overcome the 10% efficiency barrier. We demonstrate that the combination of this dye with an ionic liquid electrolyte allows the fabrication of solar cells that show power conversion efficiencies of up to 7.36% that are highly stable with no measurable degradation of initial performances after 2200 h of light soaking at 65°C under standard irradiation conditions. RK1 achieves one of the best output power conversion efficiencies for a solar cell based on the iodine/iodide electrolyte, combining high efficiency and outstanding stability.

Underlying dynamic event processes unfolding in continuous time give rise to spatiotemporal patterns that are sometimes observable at only a few discrete times. Such event processes may be modulated simultaneously over several spatial (e.g., latitude and longitude) and temporal (e.g., age, calendar time, and cohort) dimensions. The ecological challenge is to understand the dynamic latent processes that were integrated over several dimensions (space and time) to produce the observed pattern: a so-called inverse problem. An example of such a problem is characterizing epidemiological rate processes from spatially referenced age-specific prevalence data for a wildlife disease such as chronic wasting disease (CWD). With age-specific prevalence data, the exact infection times are not observed, which complicates the direct estimation of rates. However, the relationship between the observed data and the unobserved rate variables can be described with likelihood equations. Typically, for problems with multiple timescales, the likelihoods are integral equations without closed forms. The complexity of the likelihoods often makes traditional maximum-likelihood approaches untenable. Here, using seven years of hunter-harvest prevalence data from the CWD epidemic in white-tailed deer (Odocoileus virginianus) in Wisconsin, USA, we develop and explore a Bayesian approach that allows for a detailed examination of factors modulating the infection rates over space, age, and time, and their interactions. Our approach relies on the Bayesian ability to borrow strength from neighbors in both space and time. Synthesizing a number of areas of event time analysis (current-status data, age/period/cohort models, Bayesian spatial shared frailty models), our general framework has very broad ecological applicability beyond disease prevalence data to a number of important ecological event time analyses, including general survival studies with multiple time dimensions for which existing methodology is limited. We observed strong associations of infection rates with age, gender, and location. The infection rate appears to be increasing with time. We could not detect growth hotspots, or location by time interactions, which suggests that spatial variation in infection rates is determined primarily by when the disease arrives locally, rather than how fast it grows. We emphasize assumptions and the potential consequences of their violations.

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ABSTRACT:Chronic wasting disease (CWD) is a fatal, emerging disease of cervids associated with transmissible protease-resistant prion proteins. The potential for CWD to cause dramatic declines in deer and elk populations and perceived human health risks associated with consuming CWDcontaminated venison have led wildlife agencies to embark on extensive CWD control programs, typically involving culling to reduce deer populations. We characterized the spatial distribution of CWD in white-tailed deer (Odocoileus virginianus) in Wisconsin to facilitate CWD management. We found that CWD prevalence declined with distance from a central location, was locally correlated at a scale of 3.6 km, and was correlated with deer habitat abundance. The latter result is consistent with patterns expected for a positive relationship between density and prevalence of CWD. We recommend management activities focused on culling in geographic areas with high prevalence to have the greatest probability of removing infected individuals. Further research is needed to elucidate the factors involved in CWD spread and infection rates, especially the role of density-dependent transmission.

International audienceThis review presents the main class of organophosphorus compounds (triaryl phosphines, phosphazenes, phospholes, phosphetes and diphosphacyclobutanes), which have been used in electronic devices (organic light-emitting diodes (OLEDs), organic photovoltaic cells (OPV cells), dye-sensitized solar cells (DSSCs), organic field-effect transistors (OFETs), and electrochromic cells)