Biomedical subjects

A Kleczkowski

Publications and source records attributed to A Kleczkowski.

At least 19 recordsLinked to original sources

PubMed2005

Correlation between mitotic delay and aberration burden, and their role for the analysis of chromosomal damage.

The aim was to investigate further the relationship between radiation-induced mitotic delay and the expression of chromosome damage in V79 cells. Recently published data on the time-course of chromosome aberrations in V79 first-cycle metaphases after exposure to 10.4 MeV u(-1) Ar ions (LET = 1226 keV microm(-1)) were supplemented and reanalysed. A statistical analysis of the distribution of aberrations among cells was performed. Furthermore, cells were grouped into subpopulations carrying 0, 1 -2, 3-4, 5- 6 and 7 or more aberrations. Then, based on the mitotic index, the flux of each subgroup through the first mitosis was determined and the average entrance time to mitosis was estimated. For comparison, the flux of aberrant V79 cells generated by X-irradiation was analysed. Analysis of the Ar ion data revealed that the flux of each subpopulation through the first mitosis is strongly affected by its aberration burden, i.e. a positive correlation between the mitotic delay and the number of aberrations carried by a cell was observed. The distribution of aberrations among cells could be well described by Neyman-type A statistics; the corresponding fit parameters also reflect the damage-dependent mitotic delay. Interestingly, comparison of the flux of Ar ion and X-ray-irradiated V79 cells through mitosis revealed (1) that a direct correlation exists between the number of aberrations carried by a cell and its average entrance time to mitosis, and (2) that this effect is independent of the linear energy transfer. The role of these observations for radiation cytogenetics is discussed.

Animals↗

PubMed2004

Controlling disease spread on networks with incomplete knowledge.

Models for control of highly infectious diseases on local, small-world, and scale-free networks are considered, with only partial information accessible about the status of individuals and their connections. We consider a case when individuals can be infectious before showing symptoms and thus before detection. For small to moderately severe incidence of infection with a small number of nonlocal links, it is possible to control disease spread by using purely local methods applied in a neighborhood centered around a detected infectious individual. There exists an optimal radius for such a control neighborhood leading to the lowest severity of the epidemic in terms of economic costs associated with disease and treatment. The efficiency of a local control strategy is very sensitive to the choice of the radius. Below the optimal radius, the local strategy is unsuccessful; the disease spreads throughout the system, necessitating treatment of the whole population. At the other extreme, a strategy involving a neighborhood that is too large controls the disease but is wasteful of resources. It is not possible to stop an epidemic on scale-free networks by preventive actions, unless a large proportion of the population is treated.

Journal Article↗

PubMed2004

Bayesian analysis of botanical epidemics using stochastic compartmental models.

A stochastic model for an epidemic, incorporating susceptible, latent, and infectious states, is developed. The model represents primary and secondary infection rates and a time-varying host susceptibility with applications to a wide range of epidemiological systems. A Markov chain Monte Carlo algorithm is presented that allows the model to be fitted to experimental observations within a Bayesian framework. The approach allows the uncertainty in unobserved aspects of the process to be represented in the parameter posterior densities. The methods are applied to experimental observations of damping-off of radish (Raphanus sativus) caused by the fungal pathogen Rhizoctonia solani, in the presence and absence of the antagonistic fungus Trichoderma viride, a biological control agent that has previously been shown to affect the rate of primary infection by using a maximum-likelihood estimate for a simpler model with no allowance for a latent period. Using the Bayesian analysis, we are able to estimate the latent period from population data, even when there is uncertainty in discriminating infectious from latently infected individuals in data collection. We also show that the inference that T. viride can control primary, but not secondary, infection is robust to inclusion of the latent period in the model, although the absolute values of the parameters change. Some refinements and potential difficulties with the Bayesian approach in this context, when prior information on parameters is lacking, are discussed along with broader applications of the methods to a wide range of epidemiological systems.

Algorithms↗

PubMed2001

Mathematical models of radiation-induced mitotic delay: time course analysis and statistics of lesions.

Detailed investigations of high and low LET radiation induced chromosome aberrations in various mammalian cell lines have shown that the registered yield of aberrations depends on cell cycle progression delays. The effect of radiation on the cell kinetics can be analyzed in terms of kinetic growth models. The method yields the number of aberrant cells and the number of aberrations as totals obtained after integration over given time-interval.

Animals↗

PubMed2000

Population dynamics of plant-parasite interactions: thresholds for invasion.

Thresholds are derived for the invasion of plant populations by parasites. The theory is developed for a generic model that takes into account two features characteristic of plant-parasite interactions: a dual source of inoculum (infection from primary or externally introduced inoculum and secondary infection from contact between susceptible and infected host tissue) and a host response to infection load. Each of the threshold criteria is shown to be the sum of the individual components for primary and secondary infection. This indicates that if parasite invasion is not possible through primary or secondary infection alone, when the two modes of transmission are combined, the parasite may be able to invade. The invasion criteria demonstrate that there is a threshold population of susceptible hosts below which the parasite is unable to invade. If there are nonlinearities in the population dynamics (arising through either the transmission process or the host response), there are also threshold densities for the infected hosts and parasite populations below which invasion does not occur. The implications of the results for the control of plant disease are discussed.

Animals↗

PubMed1999

Predicting variability in biological control of a plant-pathogen system using stochastic models.

A stochastic model for the dynamics of a plant-pathogen interaction is developed and fitted to observations of the fungal pathogen Rhizoctonia solani (Kühn) in radish (Raphanus sativus L.), in both the presence and absence of the antagonistic fungus Trichoderma viride (Pers ex Gray). The model incorporates parameters for primary and secondary infection mechanisms and for characterizing the time-varying susceptibility of the host population. A parameter likelihood is developed and used to fit the model to data from microcosm experiments. It is shown that the stochastic model accounts well for observed variability both within and between treatments. Moreover, it enables us to describe the time evolution of the probability distribution for the variability among replicate epidemics in terms of the underlying epidemiological parameters for primary and secondary infection and decay in susceptibility. Consideration of profile likelihoods for each parameter provides strong evidence that T. viride mainly affects primary infection. By using the stochastic model to study the dependence of the probability distribution of disease levels on the primary infection rate we are therefore able to predict the effectiveness of a widely used biological control agent.

Models, Biological↗

PubMed1999

The role of sub-milankovitch climatic forcing in the initiation of the northern hemisphere glaciation

Mechanisms responsible for the initiation of major glaciation in the Northern Hemisphere at about 2.75 million years ago are poorly understood. A laminated terrestrial sequence from Pula maar, Hungary, containing about 320,000 years in annual layers between 3.05 and 2. 60 million years ago, provides a detailed record of rates of climatic change across this dramatic transition. An analysis of the record implies that climatic variations at sub-Milankovitch frequencies (less than or equal to 15,000 years) were an important driving force during this transitional interval and that, as the threshold was approached, these increased in frequency and amplitude, possibly providing the final trigger for the amplification of Northern Hemisphere ice sheets.

Journal Article↗

PubMed1995

Spatial heterogeneity, nonlinear dynamics and chaos in infectious diseases.

There is currently considerable interest in the role of nonlinear phenomena in the population dynamics of infectious diseases. Childhood diseases such as measles are particularly well documented dynamically, and have recently been the subject of analyses (of both models and notification data) to establish whether the pattern of epidemics is chaotic. Though the spatial dynamics of measles have also been extensively studied, spatial and nonlinear dynamics have only recently been brought together. The present review concentrates mainly on describing this synthesis. We begin with a general review of the nonlinear dynamics of measles models, in a spatially homogeneous environment. Simple compartmental models (specifically the SEIR model) can behave chaotically, under the influence of strong seasonal 'forcing' of infection rate associated with patterns of schooling. However, adding observed heterogeneities such as age structure can simplify the deterministic dynamics back to limit cycles. By contrast all current strongly seasonally forced stochastic models show large amplitude irregular fluctuations, with many more 'fadeouts' of infection that is observed in real communities of similar size. This indicates that (social and/or geographical) spatial heterogeneity is needed in the models. We review the exploration of this problem with nonlinear spatiotemporal models. The few studies to date indicate that spatial heterogeneity can help to increase the realism of models. However, a review of nonlinear analyses of spatially subdivided measles data show that more refinements of the models (particularly in representing the impact of human demographic changes on infection dynamics) are required. We conclude with a discussion of the implication of these results for the dynamics of infectious diseases in general and, in particular, the possibilities of cross fertilization between human disease epidemiology and the study of plant and animal diseases.

Communicable Diseases↗

PubMed1972