Bioethics in artificial reproduction in the Muslim world

In Islam infertility and its remedy with the unforbidden is allowed and encouraged. It is essential if it involves preservation of procreation and treatment of infertility in one partner of the married couples. This is applicable to artificial reproduction which is one line of treatment of infertility.... The prevention and treatment of infertility are of particular significance in the Muslim World. The social status of the Muslim woman, her dignity and self-esteem are closely related to her procreation potential in the family and in the society as a whole. Childbirth and rearing are regarded as family commitments and not just biological and social functions.     

The autonomy of biological individuals and artificial models

This paper aims to offer an overview of the meaning of autonomy for biological individuals and artificial models rooted in a specific perspective that pays attention to the historical and structural aspects of its origins and evolution. Taking autopoiesis and the recursivity characteristic of its circular logic as a starting point, we depart from some of its consequences to claim that the theory of autonomy should also take into account historical and structural features. Autonomy should not be considered only in internal or constitutive terms, the largely neglected interactive aspects stemming from it should be equally addressed. Artificial models contribute to get a better understanding of the role of autonomy for life and the varieties of its organization and phenomenological diversity.     

The problem of artificial gravity: The current state and prospects

The review deals with the use of artificial gravity in manned space flights. The need for studying this problem is substantiated, with special emphasis on its implications for future interplanetary flights. The deconditioning of astronauts and a loss of their tolerance to gravitational loads despite the use of various preventive procedures are briefly discussed. The efficiency of artificial gravity generated by a short-arm centrifuge (SAC) is evaluated; the possibility of the use of an SAC in space flights (the effect of the main parameters of G-load on humans, and its tolerability, efficiency, etc.) is considered. Both Russian and foreign data are presented on the use of SAC for simulating microgravity effects under ground-based conditions (immersion and ANOH) and in experiments on board biosatellites. It is emphasized that all the data (both original and the data in the literature) testify to the efficiency of SAC as a preventive and therapeutic facility alleviating the negative effects of simulated microgravity. The problems that have not been resolved to date are also presented.     

The problem of measurement indeterminacy in complex neurobiological movement systems

In the study of complex neurobiological movement systems, measurement indeterminacy has typically been overcome by imposing artificial modelling constraints to reduce the number of unknowns (e.g., reducing all muscle, bone and ligament forces crossing a joint to a single vector). However, this approach prevents human movement scientists from investigating more fully the role, functionality and ubiquity of coordinative structures or functional motor synergies. Advancements in measurement methods and analysis techniques are required if the contribution of individual component parts or degrees of freedom of these task-specific structural units is to be established, thereby effectively solving the indeterminacy problem by reducing the number of unknowns. A further benefit of establishing more of the unknowns is that human movement scientists will be able to gain greater insight into ubiquitous processes of physical self-organising that underpin the formation of coordinative structures and the confluence of organismic, environmental and task constraints that determine the exact morphology of these special-purpose devices.     

The Cauchy problem for one-dimensional spiking neuron models

I consider spiking neuron models defined by a one-dimensional differential equation and a reset—i.e., neuron models of the integrate-and-fire type. I address the question of the existence and uniqueness of a solution on for a given initial condition. It turns out that the reset introduces a countable and ordered set of backward solutions for a given initial condition. I discuss the implications of these mathematical results in terms of neural coding and spike timing precision.

Laplace approximation in measurement error models

Likelihood analysis for regression models with measurement errors in explanatory variables typically involves integrals that do not have a closed-form solution. In this case, numerical methods such as Gaussian quadrature are generally employed. However, when the dimension of the integral is large, these methods become computationally demanding or even unfeasible. This paper proposes the use of the Laplace approximation to deal with measurement error problems when the likelihood function involves high-dimensional integrals. The cases considered are generalized linear models with multiple covariates measured with error and generalized linear mixed models with measurement error in the covariates. The asymptotic order of the approximation and the asymptotic properties of the Laplace-based estimator for these models are derived. The method is illustrated using simulations and real-data analysis.     

Flexible parametric measurement error models

Inferences in measurement error models can be sensitive to modeling assumptions. Specifically, if the model is incorrect, the estimates can be inconsistent. To reduce sensitivity to modeling assumptions and yet still retain the efficiency of parametric inference, we propose using flexible parametric models that can accommodate departures from standard parametric models. We use mixtures of normals for this purpose. We study two cases in detail: a linear errors-in-variables model and a change-point Berkson model.     

Diffusion of bacteriophages through artificial biofilm models

The simple two-chamber diffusion method was improved to study the diffusion properties of bacteriophage (phage) T4 through a model biofilm agarose gel membrane (AGM) embedded with dead host Escherichia coli K12 cells. The apparent diffusion coefficient (D(app) ) of phage T4 was calculated to be 2.4 × 10(-12) m(2) /s in 0.5% AGM, which was lower than the coefficient of 4.2 × 10(-12) m(2) /s in 0.5% AGM without host cells. The phage adsorption process by dead host cells slowed the apparent phage diffusion. The Langmuir adsorption equation was used to simulate phage adsorption under different multiplicity of infections (MOIs); the maximum adsorbed phage MOI was calculated to be 417 PFU/CFU, and the Langmuir adsorption constant K(L) was 6.9 × 10(-4) CFU/PFU. To evaluate the effects of phage proliferation on diffusion, a simple syringe-based biofilm model was developed. The phage was added into this homogenous biofilm model when the host cells were in an exponential growth phase, and the apparent diffusion coefficient was greatly enhanced. We concluded that D(app) of phages through biofilms could be distinctly affected by phage adsorption and proliferation, and that the idea of D(app) and these methods can be used to study diffusion properties through real biofilms.     

Discrete and continuous state population models in a noisy world

Simple ecological models operate mostly with population densities using continuous variables. However, in reality densities could not change continuously, since the population itself consists of integer numbers of individuals. At first sight this discrepancy appears to be irrelevant, nevertheless, it can cause large deviations between the actual statistical behaviour of biological populations and that predicted by the corresponding models. We investigate the conditions under which simple models, operating with continuous numbers of individuals can be used to approximate the dynamics of populations consisting of integer numbers of individuals. Based on our definition for the (statistical) distance between the two models we show that the continuous approach is acceptable as long as sufficiently high biological noise is present, or, the dynamical behaviour is regular (non-chaotic). The concepts are illustrated with the Ricker model and tested on the Tribolium castaneum data series. Further, we demonstrate with the help of T. castaneum's model that if time series are not much larger than the possible population states (as in this practical case) the noisy discrete and continuous models can behave temporarily differently, almost independently of the noise level. In this case the noisy, discrete model is more accurate [OR has to be applied].     

Biosocial models of adolescent problem behaviors

This paper develops a biosocial model of adolescent age-graded norm violations ("problem behaviors"), combining a traditional social control model with a biological model using steroid hormones. Subjects were 101 white boys drawn from the 8th-, 9th-, and 10th-grade rosters of selected public schools, and ranging in age from 13 to 16. Subjects completed self-administered questionnaires and provided blood samples which were assayed for the behaviorally relevant hormones. Boys' problem behavior shows strong hormone effects. Social and biological variables have both additive and indirect effects. Using a biosocial model leads to conclusions which are different from those which would have been drawn from the sociological model alone.     

Using measurement error models to account for georeferencing error in species distribution models

Georeferencing error is prevalent in datasets used to model species distributions, inducing uncertainty in covariate values associated with species occurrences that result in biased probability of occurrence estimates. Traditionally, this error has been dealt with at the data‐level by using only records with an acceptable level of error (filtering) or by summarizing covariates at sampling units by using measures of central tendency (averaging). Here we compare those previous approaches to a novel implementation of a Bayesian logistic regression with measurement error (ME), a seldom used method in species distribution modeling. We show that the ME model outperforms data‐level approaches on 1) specialist species and 2) when either sample sizes are small, the georeferencing error is large or when all georeferenced occurrences have a fixed level of error. Thus, for certain types of species and datasets the ME model is an effective method to reduce biases in probability of occurrence estimates and account for the uncertainty generated by georeferencing error. Our approach may be expanded for its use with presence‐only data as well as to include other sources of uncertainty in species distribution models.     

The parameter identification problem for SIR epidemic models: identifying unreported cases

Journal of Mathematical Biology - A SIR epidemic model is analyzed with respect to identification of its parameters, based upon reported case data from public health sources. The objective of the...     

Zero-inflated Poisson models with measurement error in the response

Zero-inflated count data arise frequently from genomics studies. Analysis of such data is often based on a mixture model which facilitates excess zeros in combination with a Poisson distribution, and various inference methods have been proposed under such a model. Those analysis procedures, however, are challenged by the presence of measurement error in count responses. In this article, we propose a new measurement error model to describe error-contaminated count data. We show that ignoring the measurement error effects in the analysis may generally lead to invalid inference results, and meanwhile, we identify situations where ignoring measurement error can still yield consistent estimators. Furthermore, we propose a Bayesian method to address the effects of measurement error under the zero-inflated Poisson model and discuss the identifiability issues. We develop a data-augmentation algorithm that is easy to implement. Simulation studies are conducted to evaluate the performance of the proposed method. We apply our method to analyze the data arising from a prostate adenocarcinoma genomic study.