A fuzzy-genetic model for estimating forces from electromyographical activity of antagonistic muscles due to planar lower arm movements: the effect of nonlinear muscle properties

The aim of this paper is to create a model for mapping the surface electromyogram (EMG) signals to the force that generated by human arm muscles. Because the parameters of each person's muscle are individual, the model of the muscle must have two characteristics: (1) The model must be adjustable for each subject. (2) The relationship between the input and output of model must be affected by the force-length and the force-velocity behaviors are proven through Hill's experiments. Hill's model is a kinematic mechanistic model with three elements, i.e. one contractile component and two nonlinear spring elements.In this research, fuzzy systems are applied to improve the muscle model. The advantages of using fuzzy system are as follows: they are robust to noise, they prove an adjustable nonlinear mapping, and are able to model the uncertainties of the muscle.Three fuzzy coefficients have been added to the relationships of force-length (active and passive) and force-velocity existing in Hill's model. Then, a genetic algorithm (GA) has been used as a biological search method that can adjust the parameters of the model in order to achieve the optimal possible fit.Finally, the accuracy of the fuzzy genetic implementation Hill-based muscle model (FGIHM) is invested as following: the FGIHM results have 12.4% RMS error (in worse case) in comparison to the experimental data recorded from three healthy male subjects. Moreover, the FGIHM active force-length relationship which is the key characteristics of muscles has been compared to virtual muscle (VM) and Zajac muscle model. The sensitivity of the FGIHM has been evaluated by adding a white noise with zero mean to the input and FGIHM has proved to have lower sensitivity to input noise than the traditional Hill's muscle model.     

mRNA sequence predictions from homologous protein sequences

A model has been developed that permits the prediction of mRNA nucleic acid sequence from the sequences of the translated proteins. The model relies on the information obtained from the comparison of protein sequences in related species to reduce the number of possible codons for those amino acids where mutations are observed. The predictions so obtained have been tested by applying the model to proteins whose mRNA sequences are known. The model's predictions have been found to be 100% accurate if three or more different amino acids are known at a given position and if the protein sequences are restricted to relatively closely related species (within the same class). The use of this model may permit a reduction of the mRNA sequence degeneracy and therefore be helpful in the synthesis of cDNA probes or for the prediction of restriction endonuclease sites. Computer programs have been developed to ease the use of the model.     

Centrosomes and cancer: lessons from a TACC

The recent discovery that many cancer cells have centrosomal abnormalities suggests a link between centrosomes and cancer. Members of the transforming acidic coiled-coil (TACC) family of proteins have been implicated in cancer and are concentrated at centrosomes, where they regulate microtubule stability. I discuss a model of how the TACC proteins might contribute to cancer. This model predicts that defects in TACC function can make important contributions to the development of cancer but are unlikely to be the primary cause of cancer. The model might also apply to several other centrosomal proteins that have been linked to cancer.     

Integration of detailed modules in a core model of body fluid homeostasis and blood pressure regulation

This paper presents a contribution to the definition of the interfaces required to perform heterogeneous model integration in the context of integrative physiology. A formalization of the model integration problem is proposed and a coupling method is presented. The extension of the classic Guyton model, a multi-organ, integrated systems model of blood pressure regulation, is used as an example of the application of the proposed method. To this end, the Guyton model has been restructured, extensive sensitivity analyses have been performed, and appropriate transformations have been applied to replace a subset of its constituting modules by integrating a pulsatile heart and an updated representation of the renin-angiotensin system. Simulation results of the extended integrated model are presented and the impacts of their integration within the original model are evaluated.     

Big questions, small worlds: microbial model systems in ecology

Although many biologists have embraced microbial model systems as tools to address genetic and physiological questions, the explicit use of microbial communities as model systems in ecology has traditionally been more restricted. Here, we highlight recent studies that use laboratory-based microbial model systems to address ecological questions. Such studies have significantly advanced our understanding of processes that have proven difficult to study in field systems, including the genetic and biochemical underpinnings of traits involved in ecological interactions, and the ecological differences driving evolutionary change. It is the simplicity of microbial model systems that makes them such powerful tools for the study of ecology. Such simplicity enables the high degrees of experimental control and replication that are necessary to address many questions that are inaccessible through field observation or experimentation.     

An Empirical Comparison of Parametric and Semiparametric Cure Models

Parametric and semiparametric cure models have been proposed for cure proportion estimation in cancer clinical research. In this paper, several parametric and semiparametric models are compared, and their estimation methods are discussed within the framework of the EM algorithm. We show that the semiparametric PH cure model can achieve efficiency levels similar to those of parametric cure models, provided that the failure time distribution is well specified and uncured patients have an increasing hazard rate. Therefore the semiparametric model is a viable alternative to parametric cure models. When the hazard rate of uncured patients is rapidly decreasing, the estimates from the semiparametric cure model tend to have large variations and biases. However, all other models also tend to have large variations and biases in this case.     

A new semiempirical codon substitution model based on principal component analysis of mammalian sequences

Codon substitution models have traditionally been parametric Markov models, but recently, empirical and semiempirical models also have been proposed. Parametric codon models are typically based on 61×61 rate matrices that are derived from a small number of parameters. These parameters are rooted in experience and theoretical considerations and generally show good performance but are still relatively arbitrary. We have previously used principal component analysis (PCA) on data obtained from mammalian sequence alignments to empirically identify the most relevant parameters for codon substitution models, thereby confirming some commonly used parameters but also suggesting new ones. Here, we present a new semiempirical codon substitution model that is directly based on those PCA results. The substitution rate matrix is constructed from linear combinations of the first few (the most important) principal components with the coefficients being free model parameters. Thus, the model is not only based on empirical rates but also uses the empirically determined most relevant parameters for a codon model to adjust to the particularities of individual data sets. In comparisons against established parametric and semiempirical models, the new model consistently achieves the highest likelihood values when applied to sequences of vertebrates, which include the taxonomic class where the model was trained on.     

Collinearity in ecological niche modeling: Confusions and challenges

Ecological niche models are widely used in ecology and biogeography. Maxent is one of the most frequently used niche modeling tools, and many studies have aimed to optimize its performance. However, scholars have conflicting views on the treatment of predictor collinearity in Maxent modeling. Despite this lack of consensus, quantitative examinations of the effects of collinearity on Maxent modeling, especially in model transfer scenarios, are lacking. To address this knowledge gap, here we quantify the effects of collinearity under different scenarios of Maxent model training and projection. We separately examine the effects of predictor collinearity, collinearity shifts between training and testing data, and environmental novelty on model performance. We demonstrate that excluding highly correlated predictor variables does not significantly influence model performance. However, we find that collinearity shift and environmental novelty have significant negative effects on the performance of model transfer. We thus conclude that (a) Maxent is robust to predictor collinearity in model training; (b) the strategy of excluding highly correlated variables has little impact because Maxent accounts for redundant variables; and (c) collinearity shift and environmental novelty can negatively affect Maxent model transferability. We therefore recommend to quantify and report collinearity shift and environmental novelty to better infer model accuracy when models are spatially and/or temporally transferred.     

Plant 'hairy root' culture

Due to their fast growth rates and biochemical stability, 'hairy root' cultures remain unsurpassed as the choice for model root systems and have promise as a bioprocessing system. Applications are wide-ranging, from the production of natural products and foreign proteins to a model for phytoremediation of organic and metal contaminants. Hairy roots will have a continuing role as an experimental model in plant metabolic engineering.     

Fusion of Sindbis virus with model membranes containing phosphatidylethanolamine: implications for protein-induced membrane fusion

The pH-induced fusion of Sindbis virus with model lipid membranes containing phosphatidylethanolamine has been studied using a quantitative fluorescence technique. The headgroup and acyl chain domains of the lipids have been altered systematically to determine their effect on fusion. Unsaturated phosphatidylethanolamines (PE) have been found to promote fusion, either by themselves, or in combination with phosphatidylcholines (PC). Cholesterol added to a mixture of unsaturated PE and PC was also shown to increase the extent of viral fusion. The results of these studies have been interpreted in terms of a tentative model for the molecular aspects of the target membrane which are necessary for viral fusion. In this model, the target membrane must have a sufficiently-sized domain containing poorly hydrated lipids which are capable of existing in a non-bilayer arrangement.     

Glucose Kinetics in the Collagen-Induced Arthritis Model: An All-in-One Model to Assess Both Efficacy and Metabolic Side Effects of Glucocorticoids

Prednisolone and other glucocorticoids (GCs) are potent anti-inflammatory drugs, but chronic use is hampered by metabolic side effects. Therefore, there is an urgent medical need for improved GCs that are as effective as classical GCs but have a better safety profile. A well-established model to assess anti-inflammatory efficacy is the chronic collagen-induced arthritis (CIA) model in mice, a model with features resembling rheumatoid arthritis. Models to quantify undesired effects of glucocorticoids on glucose kinetics are less well-established. Recently, we have described a model to quantify basal blood glucose kinetics using stably-labeled glucose. In the present study, we have integrated this blood glucose kinetic model in the CIA model to enable quantification of both efficacy and adverse effects in one animal model. Arthritis scores were decreased after treatment with prednisolone, confirming the anti-inflammatory properties of GCs. Both inflammation and prednisolone induced insulin resistance as insulin secretion was strongly increased whereas blood glucose concentrations and hepatic glucose production were only slightly decreased. This insulin resistance did not directly resulted in hyperglycemia, indicating a highly adaptive compensatory mechanism in these mice. In conclusion, this ‘all-in-one’ model allows for studying effects of (novel) GC compounds on the development of arthritis and glucose kinetics in a single animal. This integrative model provides a valuable tool for investigating (drug-induced) metabolic dysregulation in an inflammatory setting.     

Mechanochemical couplings of kinesin motors

Kinesins are molecular motors capable of moving processively along microtubule in a stepwise manner by hydrolyzing ATP. Numerous experimental results on various aspects of their dynamical behaviours are available in literature. Although a number of models of tightly coordinated mechanism have been proposed to explain some experimental results, up to now no good explanation has been given to all these experimental results by using a single model. We have recently proposed such a model of partially coordinated hand-over-hand moving mechanism. In this paper, we use this model to study in detail various aspects of the dynamical properties of single kinesin molecules. We show that kinesin dimers walk hand-over-hand along microtubules in a partially coordinated rather than a tightly coordinated manner. The degree of coordination depends on the ratio of the two heads' ATPase rates that are in turn determined by both internal elastic force and external load. We have tested this model using various available experimental results on different samples and obtained a good agreement between the theory and the experiments.     

Random-walk models of cell dispersal included in mechanobiological simulations of tissue differentiation

Computational models have shown that biophysical stimuli can be correlated with observed patterns of tissue differentiation, and simulations have been performed that predict the time course of tissue differentiation in, for example, long bone fracture healing. Some simulations have used a diffusion model to simulate the migration and proliferation of cells with the differentiating tissue. However, despite the convenience of the diffusion model, diffusion is not the mechanism of cell dispersal: cells disperse by crawling or proliferation, or are transported in a moving fluid. In this paper, a random-walk model (i.e., a stochastic model), with and without a preferred direction, is studied as an approach to simulate cell proliferation/migration in differentiating tissues and it is compared with the diffusion model. A simulation of tissue differentiation of gap tissue in a two-dimensional model of a bone/implant interface was performed to demonstrate the differences between diffusion vs. random walk with a preferred direction. Results of diffusion and random-walk models are similar with respect to the change in the stiffness of the gap tissue but rather different results are obtained regarding tissue patterning in the differentiating tissues; the diffusion approach predicted continuous patterns of tissue differentiation whereas the random-walk model showed a more discontinuous pattern-histological results are not available that can unequivocally establish which is most similar to experimental observation. Comparing isotropic to anisotropic random walk (preferred direction of proliferation and cell migration), a more rapid reduction of the relative displacement between implant and bone is predicted. In conclusion, we have shown how random-walk models of cell dispersal and proliferation can be implemented, and shown where differences between them exist. Further study of the random-walk model is warranted, given the importance of cell seeding and cell dispersal/proliferation in many mechanobiological problems.     

Consensus structure of Pf1 filamentous bacteriophage from X-ray fibre diffraction and solid-state NMR

Filamentous bacteriophages (filamentous bacterial viruses or Inovirus) are simple and well-characterised macromolecular assemblies that are widely used in molecular biology and biophysics, both as paradigms for studying basic biological questions and as practical tools in areas as diverse as immunology and solid-state physics. The strains fd, M13 and f1 are virtually identical filamentous phages that infect bacteria expressing F-pili, and are sometimes grouped as the Ff phages. For historical reasons fd has often been used for structural studies, but M13 and f1 are more often used for biological experiments. Many other strains have been identified that are genetically quite distinct from Ff and yet have a similar molecular structure and life cycle. One of these, Pf1, gives the highest resolution X-ray fibre diffraction patterns known for filamentous bacteriophage. These diffraction patterns have been used in the past to derive a molecular model for the structure of the phage. Solid-state NMR experiments have been used in separate studies to derive a significantly different model of Pf1. Here we combine previously published X-ray fibre diffraction data and solid-state NMR data to give a consensus structure model for Pf1 filamentous bacteriophage, and we discuss the implications of this model for assembly of the phage at the bacterial membrane.     

On the implementation of a wrinkling,hyperelastic membrane model for skin and other materials

A number of researchers have studied the mechanical properties of skin and developed constitutive models to describe its behaviour. Typically, many of these studies have concentrated on the uniaxial tensile behaviour of the skin, on the grounds that it will wrinkle under in-plane compression and have minimal stiffness. However, although there is a substantial body of literature on wrinkling models, the practical implementation of such a model of skin in a finite element setting has not been widely addressed. This paper presents computational details of a wrinkling, hyperelastic membrane model and aspects of its implementation and areas requiring further research are discussed. The model is based on an Ogden constitutive model, which provides accurate results at moderate strains, but it would be straightforward to implement other constitutive models such as the Fung or Arruda–Boyce models using a similar approach. Example results are presented which demonstrate that the model can provide a good approximation to experimental data. The model has many other possible applications, both for biological materials and for other thin hyperelastic membranes.     

The evolution of male dominance

Conclusions We have proposed that an alternative explanation of sex-role evolution is available which is, unlike the prevailing conservative explanation, based on empirically supported observations. The model proposed here focuses upon the differential access of men and women to scarce resources and goods. Generally, but not always, this involves control of communications systems, intergroup relations, surplus products, and special ritual and recreative ends. We have argued that men have had greater access to these sources of power than have women because of demographic factors, and because of the power reproducing character of social evolution.Our model explicitly rejects explanations based on conservative premises. We have rejected the conservative assumptions that systems are based on survival needs, that male and female role division accords with inherent male and female capacity to meet survival demands, that status and power are a consequence of an individual's contribution to the subsistence system or to defense systems, that females are incapable of coping with the rigors of traditional male roles such as hunting, that males need to protect females, that males carry out most subsistence activity, and that the activities males engage in are those which are most necessary to system survival. In its place we have formulated a constraint model which focuses on the demographic limitations placed on females and the greater access these limitations give males to social resources through their greater ability to occupy cultural interfaces.We have also shown that the introduction of conservative premises into work aimed at providing an alternative model of sex-role evolution has prevented the formulation of research based on an alternative model. From Marx and Engels through the cultural evolutionists and up to Sanday's work, the introduction of conservative premises dealing with system needs of subsistence or of defense have prevented social scientists from dealing with the main problem: that of the strategic basis, and potential, of power wielded by men merely because they are new.T.A. Caine teaches in the Department of Sociology, University of Minnesota, Minneapolis.C.A.H. Caine teaches in the Department of Anthropology, Hamline University, St. Paul, Minnesota.     

On Ohta's hypothesis: Most amino acid substitutions are deleterious

Ohta's hypothesis that most amino acid substitutions are deleterious grew out of a class of population-genetics models called shift models. Recently, shift models have been shown to be biologically unreasonable and have been replaced by a more plausible house-of-cards model. In this paper, the simplest form of the house-of-cards models is shown to be incompatible with most of the major features of protein evolution. Moreover, this model is shown to not be a model of exclusively deleterious-allele evolution, but rather to be a model with an equal mix of deleterious and advantageous substitutions.     

Model parameter uncertainties in a dual-species biofilm competition model affect ecological output parameters much stronger than morphological ones

Bacterial biofilms are complex microbial depositions on immersed interfaces that form wherever the environmental conditions sustain microbial growth. Despite their name, biofilms can develop in highly irregular structures. Recently several mathematical concepts have been introduced to model these spatially structured microbial populations. Regardless of the type of model, they all have, even for microbially relatively simple systems, many parameters which generally are known at most approximately. We investigate the effect of uncertainties in model parameters on four morphological and four ecological output parameters using a nonlinear diffusion model for a biofilm in which two species compete for a shared nutrient. To this end we conduct an extensive computer simulation experiment for two different levels of data uncertainty, three different hydrodynamic conditions, and two different scenarios of bulk substrate availability. Our results indicate that input model parameter uncertainties have a much larger effect on ecological than on morphological output parameters.     

An integrative model of coupled water and solute exchange in the heart

Physiologists have devised many models for interpreting water and solute exchange data in whole organs, but the models have typically neglected key aspects of the underlying physiology to present the simplest possible model for a given experimental situation. We have developed a physiologically realistic model of microcirculatory water and solute exchange and applied it to diverse observations of water and solute exchange in the heart. Model simulations are consistent with the results of osmotic weight transient, tracer indicator dilution, and steady-state lymph sampling experiments. The key model features that permit this unification are the use of an axially distributed blood-tissue exchange region, inclusion of a lymphatic drain in the interstitium, and the independent computation of transcapillary solute and solvent fluxes through three different pathways.