Census timing alters stage duration distributions in matrix population models

Matrix population models are widely used to study the dynamics of stage‐structured populations. A census in these models is an event monitoring the number of individuals in each stage and occurs at discrete time intervals. The two most common methods used in building matrix population models are the prebreeding census and postbreeding census. Models using the prebreeding and postbreeding censuses assume that breeding occurs immediately before or immediately after the censuses, respectively. In some models such as age‐structured models, the results are identical regardless of the method used, rendering the choice of method a matter of preference. However, in stage‐structured models, where the duration of the first stage of life varies among newborns, a choice between the prebreeding and postbreeding censuses may result in different conclusions. This is attributed to the different first‐stage duration distributions assumed by the two methods. This study investigated the difference emerging in the structures of these models and its consequence on conclusions of eigenvalue and elasticity analyses using two‐stage models. Considerations required in choosing a modeling method are also discussed.     

Nonparametric estimation of stage occupation probabilities in a multistage model with current status data

Multistage models are used to describe individuals (or experimental units) moving through a succession of "stages" corresponding to distinct states (e.g., healthy, diseased, diseased with complications, dead). The resulting data can be considered to be a form of multivariate survival data containing information about the transition times and the stages occupied. Traditional survival analysis is the simplest example of a multistage model, where individuals begin in an initial stage (say, alive) and move irreversibly to a second stage (death). In this article, we consider general multistage models with a directed tree structure (progressive models) in which individuals traverse through stages in a possibly non-Markovian manner. We construct nonparametric estimators of stage occupation probabilities and marginal cumulative transition hazards. Empirical calculations of these quantities are not possible due to the lack of complete data. We consider current status information which represents a more severe form of censoring than the commonly used right censoring. Asymptotic validity of our estimators can be justified using consistency results for nonparametric regression estimators. Finite-sample behavior of our estimators is studied by simulation, in which we show that our estimators based on these limited data compare well with those based on complete data. We also apply our method to a real-life data set arising from a cardiovascular diseases study in Taiwan.     

Calculating spatial statistics for velocity jump processes with experimentally observed reorientation parameters

Mathematical modelling of the directed movement of animals, microorganisms and cells is of great relevance in the fields of biology and medicine. Simple diffusive models of movement assume a random walk in the position, while more realistic models include the direction of movement by assuming a random walk in the velocity. These velocity jump processes, although more realistic, are much harder to analyse and an equation that describes the underlying spatial distribution only exists in one dimension. In this communication we set up a realistic reorientation model in two dimensions, where the mean turning angle is dependent on the previous direction of movement and bias is implicitly introduced in the probability distribution for the direction of movement. This model, and the associated reorientation parameters, is based on data from experiments on swimming microorganisms. Assuming a transport equation to describe the motion of a population of random walkers using a velocity jump process, together with this realistic reorientation model, we use a moment closure method to derive and solve a system of equations for the spatial statistics. These asymptotic equations are a very good match to simulated random walks for realistic parameter values.     

The estimation of mean duration from stage frequency data

Summary A simple method of estimating duration from stage frequency data is derived. A simulation model of the passage of individuals through a particular stage in the life-cycle is presented, together with results from the model on the influence of recruitment, development and mortality on the parameters used in the estimation of stage duration. The application of the method to field data is described and a test example, using simulated data, is given.     

Ethanol production in multistage continuous, single stage continuous, Lactobacillus-contaminated continuous, and batch fermentations

Saccharomyces cerevisiae-based ethanol fermentations were conducted in batch culture, in a single stage continuous stirred tank reactor (CSTR), a multistage CSTR, and in a fermentor contaminated with Lactobacillus that corresponded to the first fermentor of the multistage CSTR system. Using a glucose concentration of 260 g l^–1 in the medium, the highest ethanol concentration reached was in batch (116gl^–1), followed by the multistage CSTR (106gl^–1), and the single stage CSTR continuous production system (60gl^–1). The highest ethanol productivity at this sugar concentration was achieved in the multistage CSTR system where a productivity of 12.7gl^–1h^–1 was seen. The other fermentation systems in comparison did not exceed an ethanol productivity of 3gl^–1h^–1. By performing a continuous ethanol fermentation in multiple stages (having a total equivalent working volume of the tested single stage), a 4-fold higher ethanol productivity was achieved as compared to either the single stage CSTR, or the batch fermentation.     

A comparison of different linkage statistics in small to moderate sized pedigrees with complex diseases

ABSTRACT: BACKGROUND: In the last years GWA studies have successfully identified common SNPs associated with complex diseases. However, most of the variants found this way account for only a small portion of the trait variance. This fact leads researchers to focus on rare-variant mapping with large scale sequencing, which can be facilitated by using linkage information. The question arises why linkage analysis often fails to identify genes when analyzing complex diseases. Using simulations we have investigated the power of parametric and nonparametric linkage statistics (KC-LOD, NPL, LOD and MOD scores), to detect the effect of genes responsible for complex diseases using different pedigree structures. RESULTS: As expected, a small number of pedigrees with less than three affected individuals has low power to map disease genes with modest effect. Interestingly, the power decreases when unaffected individuals are included in the analysis, irrespective of the true mode of inheritance. Furthermore, we found that the best performing statistic depends not only on the type of pedigrees but also on the true mode of inheritance. CONCLUSIONS: When applied in a sensible way linkage is an appropriate and robust technique to map genes for complex disease. Unlike association analysis, linkage analysis is not hampered by allelic heterogeneity. So, why does linkage analysis often fail with complex diseases? Evidently, when using an insufficient number of small pedigrees, one might miss a true genetic linkage when actually a real effect exists. Furthermore, we show that the test statistic has an important effect on the power to detect linkage as well. Therefore, a linkage analysis might fail if an inadequate test statistic is employed. We provide recommendations regarding the most favorable test statistics, in terms of power, for a given mode of inheritance and type of pedigrees under study, in order to reduce the probability to miss a true linkage.     

温控历期法在荔枝蒂蛀虫测报上的应用研究

温控试验结果结合当地气象资料,应用田间调查和室内饲养观察,进行荔枝蒂蛀虫的预测预报的改进和完善,并把测报技术用于实际生产中,建立荔枝、龙眼果园,荔枝蒂蛀虫种群发生期的预测,其结果与实际发生期基本相吻合。     

Reproductive output and duration of the pelagic larval stage determine seascape-wide connectivity of marine populations

Connectivity among marine populations is critical for persistence of metapopulations, coping with climate change, and determining the geographic distribution of species. The influence of pelagic larval duration (PLD) on connectivity has been studied extensively, but relatively little is known about the influence of other biological parameters, such as the survival and behavior of larvae, and the fecundity of adults, on population connectivity. Furthermore, the interaction between the seascape (habitat structure and currents) and these biological parameters is unclear. We explore these interactions using a biophysical model of larval dispersal across the Indo-Pacific. We describe an approach that quantifies geographic patterns of connectivity from demographically relevant to evolutionarily significant levels across a range of species. We predict that at least 95% of larval settlement occurs within 155?km of the source population and within 13 days irrespective of the species' life history, yet long-distant connections remain likely. Self-recruitment is primarily driven by the local oceanography, larval mortality, and the larval precompetency period, whereas broad-scale connectivity is strongly influenced by reproductive output (abundance and fecundity of adults) and the length of PLD. The networks we have created are geographically explicit models of marine connectivity that define dispersal corridors, barriers, and the emergent structure of marine populations. These models provide hypotheses for empirical testing.     

Heterogeneity in multistage carcinogenesis and mixture modeling

Carcinogenesis is commonly described as a multistage process, in which stem cells are transformed into cancer cells via a series of mutations. In this article, we consider extensions of the multistage carcinogenesis model by mixture modeling. This approach allows us to describe population heterogeneity in a biologically meaningful way. We focus on finite mixture models, for which we prove identifiability. These models are applied to human lung cancer data from several birth cohorts. Maximum likelihood estimation does not perform well in this application due to the heavy censoring in our data. We thus use analytic graduation instead. Very good fits are achieved for models that combine a small high risk group with a large group that is quasi immune.     

The role of receptors in multistage carcinogenesis

The principal characteristic of neoplasia is its inherited alteration of genetic expression. The regulation of gene expression may be altered both by mutational events and by environmental mediators. During carcinogenesis the permanent alterations in genetic expression resulting from mutations occur primarily during the final stage of progression when biological malignancy becomes evident. During the preceding reversible stage of promotion, alteration and genetic expression are the result of the chronic stimulation of an altered (initiated) cell responding to the environmental mediator or promoting agent. A major mechanism of this effect occurs by receptors exhibiting specificity for the mediator and for their interaction with the genome. Withdrawal of the promoting agent prior to the genetic alterations characteristic of the stage of progression leads to a reversal of the effects of the promoting agent and the death by apoptosis of most cells in the stage of promotion. Carcinogenesis mediated by the chronic ligand (promoting agent)-receptor interaction increases the probability of the development of the stage of progression; thus alteration or prevention of the stage of promotion by removal of the promoting agent or inhibition of its action remains the best opportunity for cancer prevention. Application of the reversible promoting agent-receptor interaction to specific environmental circumstances where such plays a major role can lead to a more rational risk estimation of promoting agents for the human population.     

Apoptosis and multistage carcinogenesis in rat liver

Apoptosis is a type of active cell death. It is involved in the homeostasis of cell number in tissues and is controlled by the growth regulatory network in the organism. It is also involved in the active removal of damaged cells. We have studied the role of apoptosis in cancer pre-stages and overt cancer in vivo, using rat liver as our main model system. Quantitative determination of apoptosis in histological specimens revealed that the rate of apoptosis tends to increase from normal to (pre)neoplastic to malignant cells. Thereby active cell death largely counterbalances the increasing replicative activity in developing malignancy. Tumor promoters shift the balance in favor of cell replication, whereas promoter withdrawal, fasting or TGF-β1 favor apoptosis (anti-promotion). Preneoplastic cells are more susceptible than normal liver cells to stimulation of both cell replication or cell death. Consequentially (pre)neoplastic tissue may preferentially grow or die during the appropriate treatment. Regimens that favor apoptosis and lower cell replication are shown to result in the elimination of preneoplastic cell clones from the liver (anti-initiation) and to reduce the cancer risk of the animal.     

Antioxidants and multistage carcinogenesis in mouse skin

The two-step initiation-promotion protocol for the induction of skin tumors in mice is a convenient model to elucidate what molecular events are involved in the multistage process of carcinogenesis and how they can be modulated. The current theories concerning the mechanisms of skin tumor initiation, stages 1 and 2 of tumor promotion, and tumor progression are reviewed. Because chemical carcinogens and tumor promoters may, directly or indirectly, generate reactive oxygen species (ROS) and because various antioxidants inhibit effectively some of the biochemical and biological events linked to tumor initiation, promotion and/or progression, it is conceivable that different sequences and levels of free radical-induced macromolecule damage may contribute to the evolution of the epidermal target cells from the preneoplastic stage to the malignant stage.     

Cellular targets and host genes in multistage carcinogenesis

Recent studies indicate that although cellular DNA is the critical target in the action of initiating carcinogens, specific membrane-associated receptors mediate the actions of certain tumor promoters. A stereochemical model is presented to explain how three different types of tumor promoters (phorbol esters, indole alkaloids, and polyacetates) can interact with the same class of cellular receptors. Multistage chemical carcinogenesis might involve progressive alterations in the expression of cellular DNA sequences homologous to oncogenes and regulatory sequences in certain retroviruses. We found that the oncogene c-mos is not rearranged or expressed in a series of carcinogen-transformed murine C3H 10T112 cells. These cells do express, however, a unique set of poly(A)+ RNAs that contain sequences homologous to the Moloney leukemia virus long terminal repeat sequence. Studies are in progress to determine the significance of this finding with respect to the carcinogenic process.     

Some aspects of hydrodynamics in multistage bubble columns

Hydrodynamic aspects covering the flow regimes and dispersed phase holdup have been investigated in multistage bubble columns using different geometries for the horizontal perforated plates. Air-water, air-kerosene and air-CMC solutions are investigated to cover a wide range in physical properties of liquids. The data show that the plate perforation diameter, plate spacing and the gas flow rate are the principal variables influencing the flow regimes and the dispersed phase holdup.