The potential for grass-fed livestock: resource constraints.

Using pasture and grazed forest-range for a system of producing live-stock by feeding grass alone reduces the inputs of energy about 60 percent and land resources about 8 percent, but also reduces by about half the production of animal protein in the United States. Under a system in which only grass was fed, livestock would be restricted to beef, milk, and lamb production. The amount of grain fed to U.S. livestock is about 135 million tons (metric) or about ten times the amount consumed by the U.S. population.

Coherent superpositions of three states for phosphorous donors in silicon prepared using THz radiation.

Superposition of orbital eigenstates is crucial to quantum technology utilizing atoms, such as atomic clocks and quantum computers, and control over the interaction between atoms and their neighbours is an essential ingredient for both gating and readout. The simplest coherent wavefunction control uses a two-eigenstate admixture, but more control over the spatial distribution of the wavefunction can be obtained by increasing the number of states in the wavepacket. Here we demonstrate THz laser pulse control of Si:P orbitals using multiple orbital state admixtures, observing beat patterns produced by Zeeman splitting. The beats are an observable signature of the ability to control the path of the electron, which implies we can now control the strength and duration of the interaction of the atom with different neighbours. This could simplify surface code networks which require spatially controlled interaction between atoms, and we propose an architecture that might take advantage of this.

New data and tools for integrating discrete and continuous population modeling strategies.

Realistic population models have interactions between individuals. Such interactions cause populations to behave as systems with nonlinear dynamics. Much population data analysis is done using linear models assuming no interactions between individuals. Such analyses miss strong influences on population behavior and can lead to serious errors--especially for infectious diseases. To promote more effective population system analyses, we present a flexible and intuitive modeling framework for infection transmission systems. This framework will help population scientists gain insight into population dynamics, develop theory about population processes, better analyze and interpret population data, design more powerful and informative studies, and better inform policy decisions. Our framework uses a hierarchy of infection transmission system models. Four levels are presented here: deterministic compartmental models using ordinary differential equations (DE); stochastic compartmental (SC) models that relax assumptions about population size and include stochastic effects; individual event history models (IEH) that relax the SC compartmental structure assumptions by allowing each individual to be unique. IEH models also track each individual's history, and thus, allow the simulation of field studies. Finally, dynamic network (DNW) models relax the assumption of the previous models that contacts between individuals are instantaneous events that do not affect subsequent contacts. Eventually it should be possible to transit between these model forms at the click of a mouse. An example is presented dealing with Cryptosporidium. It illustrates how transiting model forms helps assess water contamination effects, evaluate control options, and design studies of infection transmission systems using nucleotide sequences of infectious agents.

Analysis and simulation of a stochastic, discrete-individual model of STD transmission with partnership concurrency.

Deterministic differential equation models indicate that partnership concurrency and non-homogeneous mixing patterns play an important role in the spread of sexually transmitted infections. Stochastic discrete-individual simulation studies arrive at similar conclusions, but from a very different modeling perspective. This paper presents a stochastic discrete-individual infection model that helps to unify these two approaches to infection modeling. The model allows for both partnership concurrency, as well as the infection, recovery, and reinfection of an individual from repeated contact with a partner, as occurs with many mucosal infections. The simplest form of the model is a network-valued Markov chain, where the network's nodes are individuals and arcs represent partnerships. Connections between the differential equation and discrete-individual approaches are constructed with large-population limits that approximate endemic levels and equilibrium probability distributions that describe partnership concurrency. A more general form of the discrete-individual model that allows for semi-Markovian dynamics and heterogeneous contact patterns is implemented in simulation software. Analytical and simulation results indicate that the basic reproduction number R(0) increases when reinfection is possible, and the epidemic rate of rise and endemic levels are not related by 1-1/R(0), when partnerships are not point-time processes.

Infection transmission system models for microbial risk assessment.

Chemical risk assessments often focus on measuring exposure as if individuals were subject only to exogenous environmental sources of risk. For infectious diseases, exposure might not only depend on exogenous sources of microbes, but also on the infection status of other individuals in the population. For example, waterborne infections from agents such as Cryptosporidium parvum and Escherichia coli: O157:H7 might be transmitted from contaminated water to humans through drinking water; from interpersonal contact; or from infected individuals to the environment, and back to other susceptible individuals. These multiple pathways and the dependency of exposure on the prevalence of infection in a population suggest that epidemiological models are required to complement standard risk assessments in order to quantify the risk of infection. This paper presents new models of infection transmission systems that are being developed for the US Environmental Protection Agency as part of a project to quantify the risk of microbial infection. The models are designed to help inform water treatment system design decisions.

Methods and measures for the description of epidemiologic contact networks.

This article describes new methods to characterize epidemiologic contact networks that involve links that are being dynamically formed and dissolved. The new social network measures are designed with an epidemiologic interpretation in mind. These methods are intended to capture dynamic aspects of networks related to their potential to spread infection. This differs from many social network measures that are based on static networks. The networks are formulated as transmission graphs (TGs), in which nodes represent relationships between two individuals and directed edges (links) represent the potential of an individual in one relationship to carry infection to an individual in another relationship. Network measures derived from transmission graphs include "source counts," which are defined as the number of prior relationships that could potentially transmit infection to a particular node or individual.

Analyzing sensitivity to model form assumptions of infection transmission system models.

PURPOSE: Transmission system models make restrictive assumptions that might distort the conclusions of model analyses. We propose methods to progressively relax the following assumptions of classical deterministic compartmental models: 1) that the population has an effectively infinite size 2) that contact is instantaneous with no duration, 3) that mixing in this large population is instantaneously thorough after contact.METHODS: Analyses of contact patterns between high and low risk groups on gonorrhea transmission were performed. Initial models were similar to those analyzed by Hethcote and Yorke with compartments corresponding to sets of individuals. The instantaneous contact assumption in these models was relaxed by using continuous deterministic pairing models in the style of models presented by Dietz and Hadelar. That model makes restrictive assumptions about concurrent contacts, population sizes, and instantaneously random mixing. To relax these assumptions, we simulated our GERMS model of discrete individuals forming pairings and transmitting infection in continuous time.RESULTS: Relaxing the instantaneous contact assumption demonstrated a progressively decreased effect of mixing between high and low risk groups as the duration of contact was increased. The GERMS model simulations were shown to effectively reproduce pairing model behavior given the same restrictive assumptions as the pairing model. Further GERMS model analysis then demonstrated that concurrency assumptions alter the effects of contact rates between risk groups in ways that are dependent upon contact parameters. Finally GERMS models were used to structure mixing into four local areas. This affected the dynamics of reaching equilibrium but not the equilibrium value.CONCLUSIONS: Assessing the effects of assumptions in continuous compartmental models of transmission systems is feasible and important.

Bias reduction for risk ratio and vaccine effect estimators.

We examine the structural bias for established estimators of vaccine effects on susceptibility and for newer estimates of vaccine effects on infectiousness. We then propose and analyse new bias corrections for vaccine effect estimators of both susceptibility and infectiousness, as well as their combined effect on infection transmission. Each estimator is evaluated empirically with computer simulations. Of the estimators examined in this paper, those with the least bias and root mean squared error are computed by adding one to the positive count in the placebo population. We also identify a source of bias for a standard Bayesian estimator of risk ratios.

Lack of adherence to epithelial cells by freshly isolated urinary pathogens.

Urinary pathogens isolated from twenty patients who had urinary tract infections (UTI) with or without symptoms were tested for adhesion to normal buccal and uroepithelial cells, and for their ability to agglutinate guinea pig and human erythrocytes. The tests were done on initial isolation and after repeated subculture in urine and nutrient broth. Of the fresh isolates all but one were non-fimbriate and all but one were non-adherent in each test system, but after subculture in broth fourteen of the twenty strains developed fimbriae and seventeen became adherent to buccal cells. Five strains remained non-adherent to uroepithelial cells despite repeated subculture, and there was no correlation between adhesion of the subcultured organisms and clinical severity of UTI. These observations suggest that adherence is not a virulence factor for bacteria once they have entered the urinary tract, and that previous claims for the existence of a correlation between the adherence properties of uropathogens and clinical severity of UTI are unfounded.

Modified method for studying bacterial adhesion to isolated uroepithelial cells and uromucoid.

Several problems have been encountered with the application of published methods for the study of bacterial adherence to isolated uroepithelial cells. Of particular importance is the observation that urinary mucus traps some organisms but not others. Established techniques have been modified to overcome these difficulties and so allow a distinction to be made between adherence of bacteria to uromucoid and adherence to uroepithelial cells per se. The modified method was used to assess the ability of 34 urinary isolates of Escherichia coli to adhere to uroepithelial cells, uromucoid, or both after serial subculture in nutrient broth. The ability of the organisms to produce mannose-sensitive (MS) agglutination of guinea pig erythrocytes and mannose-resistant (MR) agglutination of human erythrocytes was tested simultaneously and taken to indicate possession of MS type 1 fimbriae andated MR fimbriae, respectively. Results revealed that only MS-positive organisms adhered to uromucoid (P less than 0.001), whereas MR-positive strains showed significantly greater attachment to uroepithelial cells than did MR-negative strains (P less than 0.05). These observations demand that published data derived from the use of a methodology in which no differentiation can be made between adherence to uromucoid and adherence to cells should be interpreted with caution.

Modeling contact networks and infection transmission in geographic and social space using GERMS.

BACKGROUND: Stochastic models of discrete individuals and deterministic models of continuous populations may give different answers to questions about infectious diseases. GOAL: Discrete individual model formulations are sought that extend deterministic models of infection transmission systems so that both model forms contribute cooperatively to model-based decision making. STUDY DESIGN: GERMS models are defined as stochastic processes in continuous time with parameters analogous to those in deterministic models. A GERMS model simulator was developed that insured that the rate of events depended only on the current state of model. RESULTS: The confidence intervals of long-term averages of infection level in simulated GERMS models were shown to contain the deterministic model means. CONCLUSION: GERMS models provide a convenient framework for testing the sensitivity of model-based decisions to a variety of unrealistic assumptions that are characteristic of differential equation models. GERMS especially facilitates making more realistic assumptions about contact patterns in geographic and social space.