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.     

Compound Regression Models for Ordered Categorical Data

A general class of sequential models for the analysis of ordered categorical variables is developed and discussed. The models apply if the ordinal response may be subdivided into two or more meaningful sets of response categories. The parametrization explicitly makes use of this subdivision. The models furnish a linear alternative to non-linear models which incorporate a scale parameter. They are shown to be special cases of multivariate generalized linear models. Applications are discussed with the use of several examples.     

多囊肾病动物模型的研究进展

多囊肾病(Polycystic kidney disease,PKD)是以肾脏充满多个液性囊泡,细胞增殖异常,间质炎细胞浸润及细胞外基质重塑等病理特点为主的遗传性疾病。主要分为常染色体显性多囊肾病(Autosomal dominant polycystic kidney disease,ADPKD)及常染色体隐性多囊肾病(Autosomal recessive polycystic kidney disease,ARPKD)。ADPKD更为常见,发病率约为1:500-1000,约50%的患者到60岁会发展为终末期肾脏病。ARPKD较少见,发病率约为1:20000-1:40000,患者多在婴幼儿时期死亡。目前,一旦多囊肾发展为终末期肾脏病,除了肾脏移植和透析外没有更有效的治疗方法,因此,早期的诊治对延缓多囊肾进展及防止其发展为终末期肾脏病是至关重要的。多囊肾动物模型的建立在研究多囊肾疾病具体发病机制及新药研发中具有重要意义。本文介绍了PKD疾病动物模型的研究进展,包括经典PKD自发模型、化学诱导模型及基因修饰模型。     

Measures of Imbalance for Unbalanced Models

In this paper we present a procedure to measure the degree of imbalance of an unbalanced data set. The procedure is based on choosing an appropriate loglinear model for the subclass frequencies of the data. A measure of imbalance is then introduced as some function of the chi-squared statistic used in the goodness-of-fit test for the loglinear model. The proposed procedure can also be used to measure departures from certain types of balance, such as proportionality of subclass frequencies, partial balance, and last-stage uniformity.     

Semi-discrete host-parasitoid models

Arthropod host-parasitoid interactions constitute a very important class of consumer resource dynamics. Discrete-time models are a tradition for such interactions and are characterized by an updating function, which relates the population densities at a fixed date in one year to those at the same date in the previous year. Previous workers have investigated the effects of functional response and density dependence on the stability of the host-parasitoid interaction by heuristically incorporating them in the updating function. Such an approach ignores the effects of population changing continuously within a year due to different processes (for example intraspecific competition, mortality from parasitism) that may act simultaneously. Their cumulative effect on the updating function is not obvious and a more systematic methodology is needed. This paper uses a hybrid approach to formulate the updating function. This is done by modeling the dynamics of various within-year processes in continuous-time, and reproduction as a discrete event. Using this formalism we derive results connecting the stability of the host-parasitoid interaction with different forms of density dependence and the form of the functional response. The latter results contradict previous conclusions from heuristically formulated models, and illustrate the need for such a hybrid approach in discrete-time host-parasitoid theory.     

Multi-patch deterministic and stochastic models for wildlife diseases

Spatial heterogeneity and host demography have a direct impact on the persistence or extinction of a disease. Natural or human-made landscape features such as forests, rivers, roads, and crops are important to the persistence of wildlife diseases. Rabies, hantaviruses, and plague are just a few examples of wildlife diseases where spatial patterns of infection have been observed. We formulate multi-patch deterministic and stochastic epidemic models and use these models to investigate problems related to disease persistence and extinction. We show in some special cases that a unique disease-free equilibrium exists. In these cases, a basic reproduction number ?₀ can be computed and shown to be bounded below and above by the minimum and maximum patch reproduction numbers ? _j , j=1, …, n. The basic reproduction number has a simple form when there is no movement or when all patches are identical or when the movement rate approaches infinity. Numerical examples of the deterministic and stochastic models illustrate the disease dynamics for different movement rates between three patches.     

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.     

Moment Equations and Dynamics of a Household SIS Epidemiological Model

An SIS epidemiological model of individuals partitioned into households is studied, where infections take place either within or between households, the latter generally happening much less frequently. The model is explored using stochastic spatial simulations, as well as mathematical models which consist of an infinite system of ordinary differential equations for the moments of the distribution describing the proportions of individuals who are infectious among households. Various moment-closure approximations are used to truncate the system of ODEs to finite systems of equations. These approximations can sometimes lead to a system of ill-behaved ODEs which predict moments which become negative or unbounded. A reparametrization of the ODEs is then developed, which forces all moments to satisfy necessary constraints.Changing the proportion of contacts within and between households does not change the endemic equilibrium, but does affect the amount of time it takes to approach the fixed point; increasing the proportion of contacts within households slows the spread of the infection toward endemic equilibrium. The system of moment equations does describe this phenomenon, although less accurately in the limit as the proportion of between-household contacts approaches zero. The results indicate that although controlling the movement of individuals does not affect the long-term frequency of an infection with SIS dynamics, it can have a large effect on the time-scale of the dynamics, which may provide an opportunity for other controls such as immunizations to be applied.     

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).     

Estimating amino acid substitution models for metazoan evolutionary studies

Amino acid substitution models represent the substitution rates among amino acids during the evolution of protein sequences. The models are a prerequisite for maximum likelihood or Bayesian methods to analyse the phylogenetic relationships among species based on their protein sequences. Estimating amino acid substitution models requires large protein datasets and intensive computation. In this paper, we presented the estimation of both time-reversible model (Q.met) and time non-reversible model (NQ.met) for multicellular animals (Metazoa). Analyses showed that the Q.met and NQ.met models were significantly better than existing models in analysing metazoan protein sequences. Moreover, the time non-reversible model NQ.met enables us to reconstruct the rooted phylogenetic tree for Metazoa. We recommend researchers to employ the Q.met and NQ.met models in analysing metazoan protein sequences.     

An assessment of non-randomized medical treatment of long-term schizophrenia relapse using bivariate binary-response transition models

The analyses of observational longitudinal studies involving concurrent changes in treatment and medical conditions present difficulties because of the multitude of directions of potential relationships: past medication influences current symptoms; past symptoms influence current medication; and current medication is associated with current symptoms. In the context of a long-term study of non-randomized pharmacological treatment of schizophrenic relapse, we present an analysis of bivariate discrete-time transitional data with binary responses in an attempt to understand the transitional and concurrent relationships between schizophrenia relapse and medication use. A naive analysis does not show any association between previous medication and current relapse. However, we provide evidence suggesting that current treatment may impact current relapse for those who have previously taken medication, but not for those who haven't taken medication in the past. When univariate models are specified to assess these associations, the bivariate nature of the problem requires a choice of which response, relapse or medication, should be the dependent variable. In this case, the choice of relapse or medication as a dependent variable does matter. Hence, our results derive from models where both relapse and medication are treated as dependent variables. Specifically, we specify a bivariate log odds ratio for current relapse and current medication use and a separate univariate logit component for each of these outcomes. Each of these components contains transitional associations with previous relapse and medication. Such models represent extensions of univariate transitional association models (e.g. Diggle et al. (1994)) and correspond to bivariate transitional models (e.g. Zeger and Liang (1991)). We incorporate changes in transitional associations into the full-data parametric model for final inference, and investigate if these temporal changes are due to learning effects or the impact of drop-out. We also perform residual analyses and sensitivity analyses in the context of missing data patterns.     

Mixed effects models with bivariate and univariate association parameters for longitudinal bivariate binary response data

When two binary responses are measured for each study subject across time, it may be of interest to model how the bivariate associations and marginal univariate risks involving the two responses change across time. To achieve such a goal, marginal models with bivariate log odds ratio and univariate logit components are extended to include random effects for all components. Specifically, separate normal random effects are specified on the log odds ratio scale for bivariate responses and on the logit scale for univariate responses. Assuming conditional independence given the random effects facilitates the modeling of bivariate associations across time with missing at random incomplete data. We fit the model to a dataset for which such structures are feasible: a longitudinal randomized trial of a cardiovascular educational program where the responses of interest are change in hypertension and hypercholestemia status. The proposed model is compared to a naive bivariate model that assumes independence between time points and univariate mixed effects logit models.     

Three dimensional printing as an effective method of producing anatomically accurate models for studies in thermal ecology

Hollow copper models painted to match the reflectance of the animal subject are standard in thermal ecology research. While the copper electroplating process results in accurate models, it is relatively time consuming, uses caustic chemicals, and the models are often anatomically imprecise. Although the decreasing cost of 3D printing can potentially allow the reproduction of highly accurate models, the thermal performance of 3D printed models has not been evaluated. We compared the cost, accuracy, and performance of both copper and 3D printed lizard models and found that the performance of the models were statistically identical in both open and closed habitats. We also find that 3D models are more standard, lighter, durable, and inexpensive, than the copper electroformed models.