Optimal in-vitro expansion of chondroprogenitor cells in monolayer culture

A continuous production of large quantities of chondroprogenitor cells for the manufacture of engineered cartilage tissue products is required. Expansion of the cell population in vitro has become an essential step in the process of tissue engineering of articular cartilage and the optimization of the culture conditions is a fundamental problem that needs to be addressed. The analysis of both seeding density and passage length was considered crucial in the optimization of expansion processes, and their correct selection should be taken as a requisite to establish culture conditions for monolayer systems. The determination of the optimal seeding density and the corresponding passage length for cell expansion in a serial passaging operation was found to be a compromise between growth kinetics and process time. This optimal determination was carried out using a mathematical approach that led to values of 10(4) cell/cm(2) for seeding density and 73 h for passage length. Additional considerations concerning the running cost of the process were introduced. Although the optimal passage length gave the desired expansion factor in a minimum process time, the selection of an alternative value of 120 h was shown to reduce the cost of the expansion process in more than 60%. The optimization approach presented will contribute to the development of feasible large scale expansion operations of chondroprogenitor cells required by the cartilage tissue engineering industry.     

On the effect of a therapy able to modify both the growth rates in a Gompertz stochastic model

A Gompertz-type diffusion process characterized by the presence of exogenous factors in the drift term is considered. Such a process is able to describe the dynamics of populations in which both the intrinsic rates are modified by means of time-dependent terms. In order to quantify the effect of such terms the evaluation of the relative entropy is made. The first passage time problem through suitable boundaries is studied. Moreover, some simulation results are shown in order to capture the dependence of the involved functions on the parameters. Finally, an application to tumor growth is presented and simulation results are shown.     

Lifesaving explains mortality decline with time

Mortality rates of human populations in developed countries are declining with time. We show that this effect can be explained via a 'lifesaving' methodology. Our approach is based on considering a non-homogeneous Poisson process of potentially harmful events. Each of these events can be 'cured' with a given probability or can result in a termination of the Poisson process (death) with a complementary probability. A lifesaving ratio, defining the corresponding relative increase in life expectancy for homogeneous and heterogeneous populations is analyzed. Some generalizations are discussed. Several simple examples are considered.     

THE EVOLUTIONARY GENETICS OF MALE LIFE-HISTORY CHARACTERS IN DROSOPHILA MELANOGASTER

Alternative models of the maintenance of genetic variability, theories of life-history evolution, and theories of sexual selection and mate choice can be tested by measuring additive and nonadditive genetic variances of components of fitness. A quantitative genetic breeding design was used to produce estimates of genetic variances for male life-history traits in Drosophila melanogaster. Additive genetic covariances and correlations between traits were also estimated. Flies from a large, outbred, laboratory population were assayed for age-specific competitive mating ability, age-specific survivorship, body mass, and fertility. Variance-component analysis then allowed the decomposition of phenotypic variation into components associated with additive genetic, nonadditive genetic, and environmental variability. A comparison of dominance and additive components of genetic variation provides little support for an important role for balancing selection in maintaining genetic variance in this suite of traits. The results provide support for the mutation-accumulation theory, but not the antagonistic-pleiotropy theory of senescence. No evidence is found for the positive genetic correlations between mating success and offspring quality or quantity that are predicted by “good genes” models of sexual selection. Additive genetic coefficients of variation for life-history characters are larger than those for body weight. Finally, this set of male life-history characters exhibits a very low correspondence between estimates of genetic and phenotypic correlations.     

Density dependence in northern ungulates: interactions with predation and resources

Variation in the abundance of animals has traditionally been explained as the outcome of endogenous forcing from density dependence and exogenous forcing arising from variation in weather and predation. Emerging evidence suggests that the effects of density dependence interact with external influences on population dynamics. In particular, spatial heterogeneity in resources and the presence of capable predators may weaken feedbacks from density dependence to growth of populations. We used the Kalman filter to analyze 23 time series of estimates of abundance of northern ungulate populations arrayed along a latitudinal gradient (latitude range of 40°–70°N) to evaluate the influence of spatial heterogeneity in resources and predation on density dependence. We also used contingency tables to test whether density dependence was independent of the presence of carnivores (our estimate of predation) and multiple regressions to determine the effects of spatial heterogeneity in resources, predation, and latitude on the strength of density dependence. Our results showed that the strength of density dependence of ungulate populations was low in the presence of large carnivores, particularly at northern latitudes with low primary productivity. We found that heterogeneity in elevation, which we assume acted as a surrogate for spatial heterogeneity in plant phenology, also reduced effects of density dependence. Thus, we show that external forces created by heterogeneity in resources and predation interact with internal feedbacks from population density to shape dynamics of populations of northern ungulates.     

Cross‐protective effect of acid‐adapted Salmonella enterica on resistance to lethal acid and cold stress conditions

Aims: To evaluate the cross‐protected Salmonella enterica cells under acid and cold stress conditions. Methods and Results: The acid‐adapted S. enterica cells were exposed to pH 4·0 at 4 and 20°C. Recovery of sublethally injured cells was estimated by the difference between the counts obtained on trypticase soy agar (TSA) and xylose lysine desoxycholate (XLD) agar. The survival curves of nonadapted and acid‐adapted S. enterica cells at pH 4·0 were fitted with Weibull distribution model. The recovery behaviour of injured S. enterica cells was estimated by the modified Gompertz parameters. Acid‐adapted S. enterica were more resistant to subsequent acid shock than the nonadapted cells. The numbers of nonadapted S. enterica cells were decreased by 4·57 and 7·55 log CFU ml^?1 at 4 and 20°C after 12‐day acid challenge, respectively. The acid adaptation induced cross‐protection and viable nonculturable (VBNC) state against low acid and cold stresses. The 7‐h adaptation showed the least recovery of injured cells. Conclusion: The results suggest that acid‐adapted S. enterica cells induced acid tolerance response and VBNC state. Significance and Impact of the Study: These results provide useful information for understanding the induction of cross‐protected and VBNC pathogens under various stresses, which might be needed in designing new food preservation strategies.     

Bayesian Analysis of Growth Curves Using Mixed Models Defined by Stochastic Differential Equations

Summary Growth curve data consist of repeated measurements of a continuous growth process over time in a population of individuals. These data are classically analyzed by nonlinear mixed models. However, the standard growth functions used in this context prescribe monotone increasing growth and can fail to model unexpected changes in growth rates. We propose to model these variations using stochastic differential equations (SDEs) that are deduced from the standard deterministic growth function by adding random variations to the growth dynamics. A Bayesian inference of the parameters of these SDE mixed models is developed. In the case when the SDE has an explicit solution, we describe an easily implemented Gibbs algorithm. When the conditional distribution of the diffusion process has no explicit form, we propose to approximate it using the Euler–Maruyama scheme. Finally, we suggest validating the SDE approach via criteria based on the predictive posterior distribution. We illustrate the efficiency of our method using the Gompertz function to model data on chicken growth, the modeling being improved by the SDE approach.