Circadian and chemotherapy-related changes in urinary modified nucleosides excretion in patients with metastatic colorectal cancer.

Urinary levels of modified nucleosides reflect nucleic acids turnover and can serve as non-invasive biomarkers for monitoring tumour circadian dynamics, and treatment responses in patients with metastatic colorectal cancer. In 39 patients, median overnight urinary excretion of LC-HRMS determinations of pseudouridine, was ~ tenfold as large as those of 1-methylguanosine, 1-methyladenosine, or 4-acetylcytidine, and ~ 100-fold as large as those of adenosine and cytidine. An increase in any nucleoside excretion after chemotherapy anticipated plasma carcinoembryonic antigen progression 1-2 months later and was associated with poor survival. Ten fractionated urines were collected over 2-days in 29 patients. The median value of the rhythm-adjusted mean of urinary nucleoside excretion varied from 64.3 for pseudouridine down to 0.61 for cytidine. The rhythm amplitudes relative to the 24-h mean of 6 nucleoside excretions were associated with rest duration, supporting a tight link between nucleosides turnover and the rest-activity rhythm. Moreover, the amplitude of the 1-methylguanosine rhythm was correlated with the rest-activity dichotomy index, a significant predictor of survival outcome in prior studies. In conclusion, urinary excretion dynamics of modified nucleosides appeared useful for the characterization of the circadian control of cellular proliferation and for tracking early responses to treatments in colorectal cancer patients.

Modelling antigenic drift in weekly flu incidence.

Since influenza in humans is a major public health threat, the understanding of its dynamics and evolution, and improved prediction of its epidemics are important aims. Underlying its multi-strain structure is the evolutionary process of antigenic drift whereby epitope mutations give mutant virions a selective advantage. While there is substantial understanding of the molecular mechanisms of antigenic drift, until now there has been no quantitative analysis of this process at the population level. The aim of this study is to develop a predictive model that is of a modest-enough structure to be fitted to time series data on weekly flu incidence. We observe that the rate of antigenic drift is highly non-uniform and identify several years where there have been antigenic surges where a new strain substantially increases infective pressure. The SIR-S approach adopted here can also be shown to improve forecasting in comparison to conventional methods.

Common structure in panels of short ecological time-series.

Typically, in many studies in ecology, epidemiology, biomedicine and others, we are confronted with panels of short time-series of which we are interested in obtaining a biologically meaningful grouping. Here, we propose a bootstrap approach to test whether the regression functions or the variances of the error terms in a family of stochastic regression models are the same. Our general setting includes panels of time-series models as a special case. We rigorously justify the use of the test by investigating its asymptotic properties, both theoretically and through simulations. The latter confirm that for finite sample size, bootstrap provides a better approximation than classical asymptotic theory. We then apply the proposed tests to the mink-muskrat data across 81 trapping regions in Canada. Ecologically interpretable groupings are obtained, which serve as a necessary first step before a fuller biological and statistical analysis of the food chain interaction.

Population dynamic interference among childhood diseases.

Epidemiologists usually study the interaction between a host population and one parasitic infection. However, different parasite species effectively compete, in an ecological sense, for the same finite group of susceptible hosts, so there may be an indirect effect on the population dynamics of one disease due to epidemics of another. In human populations, recovery from any serious infection is normally preceded by a period of convalescence, during which infected individuals stay at home and are effectively shielded from exposure to other infectious diseases. We present a model for the dynamics of two infectious diseases, incorporating a temporary removal of susceptibles. We use this model to explore population-level consequences of a temporary insusceptibility in childhood diseases, the dynamics of which are partly driven by differences in contact rates in and out of school terms. Significant population dynamic interference is predicted and cannot be dismissed in the limited case-study data available for measles and whooping cough in England before the vaccination era.

Patterns of density dependence in measles dynamics.

An important question in metapopulation dynamics is the influence of external perturbations on the population's long-term dynamic behaviour. In this paper we address the question of how spatiotemporal variations in demographic parameters affect the dynamics of measles populations in England and Wales. Specifically, we use nonparametric statistical methods to analyse how birth rate and population size modulate the negative density dependence between successive epidemics as well as their periodicity. For the observed spatiotemporal data from 60 cities, and for simulated model data, the demographic variables act as bifurcation parameters on the joint density of the trade-off between successive epidemics. For increasing population size, a transition occurs from an irregular unpredictable pattern in small communities towards a regular, predictable endemic pattern in large places. Variations in the birth rate parameter lead to a bifurcation from annual towards biennial cyclicity in both observed data and model data.

Empirical determinants of measles metapopulation dynamics in England and Wales.

A key issue in metapopulation dynamics is the relative impact of internal patch dynamics and coupling between patches. This problem can be addressed by analysing large spatiotemporal data sets, recording the local and global dynamics of metapopulations. In this paper, we analyse the dynamics of measles meta-populations in a large spatiotemporal case notification data set, collected during the pre-vaccination era in England and Wales. Specifically, we use generalized linear statistical models to quantify the relative importance of local influences (birth rate and population size) and regional coupling on local epidemic dynamics. Apart from the proportional effect of local population size on case totals, the models indicate patterns of local and regional dynamic influences which depend on the current state of epidemics. Birth rate and geographic coupling are not associated with the size of major epidemics. By contrast, minor epidemics--and especially the incidence of local extinction of infection--are influenced both by birth rate and geographical coupling. Birth rate at a lag of four years provides the best fit, reflecting the delayed recruitment of susceptibles to school cohorts. A hierarchical index of spatial coupling to large centres provides the best spatial model. The model also indicates that minor epidemics and extinction patterns are more strongly influenced by this regional effect than the local impact of birth rate.