Times from Infection to Disease-Induced Death and their Influence on Final Population Sizes After Epidemic Outbreaks.

For epidemic models, it is shown that fatal infectious diseases cannot drive the host population into extinction if the incidence function is upper density-dependent. This finding holds even if a latency period is included and the time from infection to disease-induced death has an arbitrary length distribution. However, if the incidence function is also lower density-dependent, very infectious diseases can lead to a drastic decline of the host population. Further, the final population size after an epidemic outbreak can possibly be substantially affected by the infection-age distribution of the initial infectives if the life expectations of infected individuals are an unbounded function of infection age (time since infection). This is the case for lognormal distributions, which fit data from infection experiments involving tiger salamander larvae and ranavirus better than gamma distributions and Weibull distributions.

Do fatal infectious diseases eradicate host species?

In simple SI epidemic and endemic models, three classes of incidence functions are identified for their potential to be associated with host extinction: weakly upper density-dependent incidences are never associated with host extinction. Power incidences that depend on the number of susceptibles and infectives by powers strictly between 0 and 1 are associated with initial-constellation-dependent host extinction for all parameter values. Homogeneous incidences, of which frequency-dependent incidence is a very particular case, and power incidences are associated with global host extinction for certain parameter constellations and with host survival for others. Laboratory infection experiments with salamander larvae are equally well fitted by power incidences and certain upper density-dependent incidences such as the negative binomial incidence and do not rule out homogeneous incidences such as an asymmetric frequency-dependent incidence either.

Vacuum Furnace for Degassing Stainless-Steel Vacuum Components.

Ultra-high vacuum systems must often be constructed of materials with ultra-low outgassing rates to achieve pressure of 10-6 Pa and below. Any component placed into the ultra-high vacuum system must also be constructed of materials with ultra-low outgassing rates. Baking stainless steel vacuum components to a temperature range of 400 °C to 450 °C while under vacuum is an effective method to reduce the outgassing rate of vacuum components for use in ultra-high vacuum systems. The design, construction, and operation of a vacuum furnace capable of baking vacuum components to a temperature of 450° C while maintaining a pressure of 10-3 Pa or lower is described. The furnace has been used for extended bakes at 450 °C while maintaining pressures below 10-5 Pa. As an example, we obtained an outgassing rate of 1.2 × 10-9 Pa L s-1 for a gate valve baked for 20 days at a temperature of 420 °C.

How the concavity of reproduction/survival trade-offs impacts the evolution of life history strategies.

Previous works using different mathematical techniques, however, show that the concavity of the trade-off relationship can alter the expected life history strategies. Thus we developed a model and found that the concavity of the reproduction-survival curve can still have a large impact on life history strategies in an ecological model with Darwinian evolution.

Within-host infectious disease models accommodating cellular coinfection, with an application to influenza.

Within-host models are useful tools for understanding the processes regulating viral load dynamics. While existing models have considered a wide range of within-host processes, at their core these models have shown remarkable structural similarity. Specifically, the structure of these models generally consider target cells to be either uninfected or infected, with the possibility of accommodating further resolution (e.g. cells that are in an eclipse phase). Recent findings, however, indicate that cellular coinfection is the norm rather than the exception for many viral infectious diseases, and that cells with high multiplicity of infection are present over at least some duration of an infection. The reality of these cellular coinfection dynamics is not accommodated in current within-host models although it may be critical for understanding within-host dynamics. This is particularly the case if multiplicity of infection impacts infected cell phenotypes such as their death rate and their viral production rates. Here, we present a new class of within-host disease models that allow for cellular coinfection in a scalable manner by retaining the low-dimensionality that is a desirable feature of many current within-host models. The models we propose adopt the general structure of epidemiological 'macroparasite' models that allow hosts to be variably infected by parasites such as nematodes and host phenotypes to flexibly depend on parasite burden. Specifically, our within-host models consider target cells as 'hosts' and viral particles as 'macroparasites', and allow viral output and infected cell lifespans, among other phenotypes, to depend on a cell's multiplicity of infection. We show with an application to influenza that these models can be statistically fit to viral load and other within-host data, and demonstrate using model selection approaches that they have the ability to outperform traditional within-host viral dynamic models. Important in vivo quantities such as the mean multiplicity of cellular infection and time-evolving reassortant frequencies can also be quantified in a straightforward manner once these macroparasite models have been parameterized. The within-host model structure we develop here provides a mathematical way forward to address questions related to the roles of cellular coinfection, collective viral interactions, and viral complementation in within-host viral dynamics and evolution.