Resolving the biodiversity paradox

The paradox of biodiversity involves three elements, (i) mathematical models predict that species must differ in specific ways in order to coexist as stable ecological communities, (ii) such differences are difficult to identify, yet (iii) there is widespread evidence of stability in natural communities. Debate has centred on two views. The first explanation involves tradeoffs along a small number of axes, including 'colonization-competition', resource competition (light, water, nitrogen for plants, including the 'successional niche'), and life history (e.g. high-light growth vs. low-light survival and few large vs. many small seeds). The second view is neutrality, which assumes that species differences do not contribute to dynamics. Clark et al. (2004) presented a third explanation, that coexistence is inherently high dimensional, but still depends on species differences. We demonstrate that neither traditional low-dimensional tradeoffs nor neutrality can resolve the biodiversity paradox, in part by showing that they do not properly interpret stochasticity in statistical and in theoretical models. Unless sample sizes are small, traditional data modelling assures that species will appear different in a few dimensions, but those differences will rarely predict coexistence when parameter estimates are plugged into theoretical models. Contrary to standard interpretations, neutral models do not imply functional equivalence, but rather subsume species differences in stochastic terms. New hierarchical modelling techniques for inference reveal high-dimensional differences among species that can be quantified with random individual and temporal effects (RITES), i.e. process-level variation that results from many causes. We show that this variation is large, and that it stands in for species differences along unobserved dimensions that do contribute to diversity. High dimensional coexistence contrasts with the classical notions of tradeoffs along a few axes, which are often not found in data, and with 'neutral models', which mask, rather than eliminate, tradeoffs in stochastic terms. This mechanism can explain coexistence of species that would not occur with simple, low-dimensional tradeoff scenarios.     

A review of the integrate-and-fire neuron model: II. Inhomogeneous synaptic input and network properties

The integrate-and-fire neuron model describes the state of a neuron in terms of its membrane potential, which is determined by the synaptic inputs and the injected current that the neuron receives. When the membrane potential reaches a threshold, an action potential (spike) is generated. This review considers the model in which the synaptic input varies periodically and is described by an inhomogeneous Poisson process, with both current and conductance synapses. The focus is on the mathematical methods that allow the output spike distribution to be analyzed, including first passage time methods and the Fokker–Planck equation. Recent interest in the response of neurons to periodic input has in part arisen from the study of stochastic resonance, which is the noise-induced enhancement of the signal-to-noise ratio. Networks of integrate-and-fire neurons behave in a wide variety of ways and have been used to model a variety of neural, physiological, and psychological phenomena. The properties of the integrate-and-fire neuron model with synaptic input described as a temporally homogeneous Poisson process are reviewed in an accompanying paper (Burkitt in Biol Cybern, 2006).     

Advantages and disadvantages of the animal models v. in vitro studies in iron metabolism: a review

Iron deficiency is the most common nutritional deficiency in the world. Special molecules have evolved for iron acquisition, transport and storage in soluble, nontoxic forms. Studies about the effects of iron on health are focused on iron metabolism or nutrition to prevent or treat iron deficiency and anemia. These studies are focused in two main aspects: (1) basic studies to elucidate iron metabolism and (2) nutritional studies to evaluate the efficacy of iron supplementation to prevent or treat iron deficiency and anemia. This paper reviews the advantages and disadvantages of the experimental models commonly used as well as the methods that are more used in studies related to iron. In vitro studies have used different parts of the gut. In vivo studies are done in humans and animals such as mice, rats, pigs and monkeys. Iron metabolism is a complex process that includes interactions at the systemic level. In vitro studies, despite physiological differences to humans, are useful to increase knowledge related to this essential micronutrient. Isotopic techniques are the most recommended in studies related to iron, but their high cost and required logistic, making them difficult to use. The depletion–-repletion of hemoglobin is a method commonly used in animal studies. Three depletion–-repletion techniques are mostly used: hemoglobin regeneration efficiency, relative biological values (RBV) and metabolic balance, which are official methods of the association of official analytical chemists. These techniques are well-validated to be used as studies related to iron and their results can be extrapolated to humans. Knowledge about the main advantages and disadvantages of the in vitro and animal models, and methods used in these studies, could increase confidence of researchers in the experimental results with less costs.     

多重二元响应Probit模型的渐近有效估计

针对多重二元响应Probit模型提出了两步估计方法,第一步由边际似然得到参数√n相合的估计,第二步通过一步迭代得到渐近有效估计,由于只需一步迭代,因此在利用模拟方法计算信息阵时,可以增加模拟的次数,从而减少模拟所产生的扰动对估计的影响．     

A computational tool for the simulation and optimization of microbial strains accounting integrated metabolic/regulatory information

Background and scope

Recently, a number of methods and tools have been proposed to allow the use of genome-scale metabolic models for the phenotype simulation and optimization of microbial strains, within the field of Metabolic Engineering (ME). One of the limitations of most of these algorithms and tools is the fact that only metabolic information is taken into account, disregarding knowledge on regulatory events.

Implementation and performances

This work proposes a novel software tool that implements methods for the phenotype simulation and optimization of microbial strains using integrated models, encompassing both metabolic and regulatory information. This tool is developed as a plug-in that runs over OptFlux, a computational platform that aims to be a reference tool for the ME community.

Availability

The plug-in is made available in the OptFlux web site (www.optflux.org) together with examples and documentation.     

A review of the use of primates in studying human schistosomiasis

Rodents, useful models for studies of human schistosomiasis not ethically possible in man, are not satisfactory in every respect. Primates, in many ways better models than rodents, though too expensive for general use, remain invaluable for testing the relevance of rodent findings to man. Higher primates such as chimpanzees are ideal models but, as endangered species, should only be used for critical studies for which lower, nonhuman primates are unsuitable.     

Hierarchical multivariate mixture generalized linear models for the analysis of spatial data: An application to disease mapping

Disease mapping of a single disease has been widely studied in the public health setup. Simultaneous modeling of related diseases can also be a valuable tool both from the epidemiological and from the statistical point of view. In particular, when we have several measurements recorded at each spatial location, we need to consider multivariate models in order to handle the dependence among the multivariate components as well as the spatial dependence between locations. It is then customary to use multivariate spatial models assuming the same distribution through the entire population density. However, in many circumstances, it is a very strong assumption to have the same distribution for all the areas of population density. To overcome this issue, we propose a hierarchical multivariate mixture generalized linear model to simultaneously analyze spatial Normal and non‐Normal outcomes. As an application of our proposed approach, esophageal and lung cancer deaths in Minnesota are used to show the outperformance of assuming different distributions for different counties of Minnesota rather than assuming a single distribution for the population density. Performance of the proposed approach is also evaluated through a simulation study.     

A parameterization of leaf phenology for the terrestrial ecosystem component of climate models

Leaf phenology remains one of the most difficult processes to parameterize in terrestrial ecosystem models because our understanding of the physical processes that initiate leaf onset and senescence is incomplete. While progress has been made at the molecular level, for example by identifying genes that are associated with senescence and flowering for selected plant species, a picture of the processes controlling leaf phenology is only beginning to emerge. A variety of empirical formulations have been used with varying degrees of success in terrestrial ecosystem models for both extratropical and tropical biomes. For instance, the use of growing degree‐days (GDDs) to initiate leaf onset has received considerable recognition and this approach is used in a number of models. There are, however, limitations when using GDDs and other empirically based formulations in global transient climate change simulations. The phenology scheme developed for the Canadian Terrestrial Ecosystem Model (CTEM), designed for inclusion in the Canadian Centre for Climate Modelling and Analysis coupled general circulation model, is described. The representation of leaf phenology is general enough to be applied over the globe and sufficiently robust for use in transient climate change simulations. Leaf phenology is functionally related to the (possibly changing) climate state and to atmospheric composition rather than to geographical boundaries or controls implicitly based on current climate. In this approach, phenology is controlled by environmental conditions as they affect the carbon balance. A carbon‐gain‐based scheme initiates leaf onset when it is beneficial for the plant, in carbon terms, to produce new leaves. Leaf offset is initiated by unfavourable environmental conditions that incur carbon losses and these include shorter day length, cooler temperatures, and dry soil moisture conditions. The comparison of simulated leaf onset and offset times with observation‐based estimates for temperate and boreal deciduous, tropical evergreen, and tropical deciduous plant functional types at selected locations indicates that the phenology scheme performs satisfactorily. Model simulated leaf area index and stem and root biomass are also compared with observational estimates to illustrate the performance of CTEM.     

First principle-based models in plant suspension cell cultures: a review

In this work, the development and application of published models for describing the behavior of plant cell cultures is reviewed. The structure of each type of model is analyzed and the new tendencies for the modeling of biotechnological processes that can be applied in plant cell cultures are presented. This review is a tool for clarifying the main features that characterize each type of model in the field of plant cell cultures and can be used as a support on the selection of the more suitable model type, taking into account the purpose of specific research.     

On stochastic compartmental models

The statistical averaging of compartmental models with parameters being random processes is derived for the case of vanishing input and uniformly bounded input. The difference of resulting models is discussed.     

A bivariate cure-mixture approach for modeling familial association in diseases

For modeling correlation in familial diseases with variable ages at onset, we propose a bivariate model that incorporates two types of pairwise association, one between the lifetime risk or the overall susceptibility of two individuals and one between the ages at onset between two susceptible individuals. For estimation, we consider a two-stage estimation procedure similar to that of Shih (1998, Biometrics 54, 1115-1128). We evaluate the properties of the estimators through simulations and compare the performance with that from a bivariate survival model that allows correlation between ages at onset only. We apply the methodology to breast cancer using the kinship data from the Washington Ashkenazi Study. We also discuss potential applications of the proposed method in the area of cure modeling.     

The behavior of the atmosphere in long-range transport

Long-range transport of gases and aerosols depends on vertical and horizontal air shifts: studies on atmospheric transport have to be based on the meteorological conditions of the environment. The aim of this paper is to review the physical principles on which meteorological models are based, and to make an exhaustive inventory of the main models used for the prognosis and diagnosis of air masses and a list of the long-range transport models associated to them. URL addresses are provided for all of these models to allow the reader to check their usefulness. A practical application is included with reference to trajectories followed by air masses that reach the Iberian Peninsula at various altitudes, depending on the synoptic meteorological situation. Further examples describe air masses entering Europe with desert dust from Africa. This dust may eventually develop into freezing nuclei and form hailstones in summer storms.