Modelling aspects of oviduct fluid formation in vitro

© 2017 Society for Reproduction and Fertility. Oviduct fluid is the microenvironment that supports early reproductive processes including fertilisation, embryo cleavage and genome activation. However, the composition and regulation of this critical environment remain rather poorly defined. This study uses an in vitro preparation of the bovine oviduct epithelium to investigate the formation and composition of in vitro-derived oviduct fluid (ivDOF) within a controlled environment. We confirm the presence of oviduct-specific glycoprotein 1 in ivDOF and show that the amino acid and carbohydrate content resembles that of previously reported in vivo data. In parallel, using a different culture system, a panel of oviduct epithelial solute carrier genes and the corresponding flux of amino acids within ivDOF in response to steroid hormones were investigated. We next incorporated fibroblasts directly beneath the epithelium. This dual culture arrangement represents more faithfully the in vivo environment and impacts on ivDOF composition. Lastly, physiological and pathophysiological endocrine states were modelled and their impact on the in vitro oviduct preparation was evaluated. These experiments help clarify the dynamic function of the oviduct in vitro and suggest a number of future research avenues, such as investigating epithelial-fibroblast interactions, probing the molecular aetiologies of subfertility and optimising embryo culture media

Optimum Tracking with Evolution Strategies

Evolutionary algorithms are frequently applied to dynamic optimization problems in which the objective varies with time. It is desirable to gain an improved understanding of the influence of different genetic operators and of the parameters of a strategy on its tracking performance. An approach that has proven useful in the past is to mathematically analyze the strategy's behavior in simple, idealized environments. The present paper investigates the performance of a multiparent evolution strategy that employs cumulative step length adaptation for an optimization task in which the target moves linearly with uniform speed. Scaling laws that quite accurately describe the behavior of the strategy and that greatly contribute to its understanding are derived. It is shown that in contrast to previously obtained results for a randomly moving target, cumulative step length adaptation fails to achieve optimal step lengths if the target moves in a linear fashion. Implications for the choice of population size parameters are discussed

Local Performance of the (1 + 1)-ES in a Noisy Environment

While noise is a phenomenon present in many real-world optimization problems, the understanding of its potential effects on the performance of evolutionary algorithms is still incomplete. This paper investigates the effects of noise for the infinite-dimensional quadratic sphere and a (1 +1)-ES with isotropic normal mutations. It is shown that overvaluation as a result of failure to reevaluate parental fitness leads to both reduced success probabilities and improved performance. Implications for mutation strength adaptation rules are discussed and optimal resampling rates are computed

On the Effects of Outliers on Evolutionary Optimization

Most studies concerned with the effects of noise on evolutionary computation have assumed a Gaussian noise model. However, practical optimization strategies frequently face situations where the noise is not Gaussian, and sometimes it does not even have a nite variance.In particular, outliers may be present. In this paper, Cauchy distributed noise is used for modeling such situations. A performance law that describes how the progress of an evolution strategy using intermediate recombination scales in the presence of such noise is derived. Implications of that law are studied numerically, and comparisons with the case of Gaussian noise are drawn

Qualms Regarding the Optimality of Cumulative Path Length Control in CSA/CMA-Evolution Strategies

The cumulative step-size adaptation (CSA) based on path length control is regarded as a robust alternative to the standard mutative self-adaptation technique in evolution strategies (ES), guaranteeing an almost optimal control of the mutation operator. In this short paper it is shown that the underlying basic assumption in CSA - the perpendicularity of expected consecutive steps - does not necessarily guarantee optimal progress performance for (...)intermediate recombinative ES

On the Benefits of Distributed Populations for Noisy Optimization

While in the absence of noise, no improvement in local performance can be gained from retaining but the best candidate solution found so far, it has been shown experimentally that in the presence of noise, operating with a non-singular population of candidate solutions can have a marked and positive effect on the local performance of evolution strategies. So as to determine the reasons for the improved performance, we study the evolutionary dynamics of the -ES in the presence of noise. Considering a simple, idealized environment, a moment-based approach that utilizes recent results involving concomitants of selected order statistics is developed. This approach yields an intuitive explanation for the performance advantage of multi-parent strategies in the presence of noise. It is then shown that the idealized dynamic process considered does bear relevance to optimization problems in high-dimensional search spaces

Evolutionary Optimization with Cumulative Step Length Adaptation

Iterative algorithms for numerical optimization in continuous spaces typically need to adapt their step lengths in the course of the search. While some strategies employ fixed schedules for reducing the step lengths over time, others attempt to adapt interactively in response to either the outcome of trial steps or to the history of the search process. Evolutionary algorithms are of the latter kind. One of the control strategies that is commonly used in evolution strategies is the cumulative step length adaptation approach. This paper presents a first theoretical analysis of that adaptation strategy by considering the algorithm as a dynamical system. The analysis includes the practically relevant case of noise interfering in the optimization process. Recommendations are made with respect to the problem of choosing appropriate population sizes

An investigation on effectiveness of temperature treatment for fluorine-based reactive plasma jet machining of N-BK7®

In this study, a fluorine-based reactive plasma jet is investigated as a promising tool for ultraprecise surface machining of N-BK7®. Plasma-generated particles react with an N-BK7 surface to create volatile and nonvolatile compounds. The desorption of volatile compounds results in an etched surface, whereas nonvolatile compounds form a residual layer in the etched area, causing unpredictable effects on the etching rate. Surface temperature treatment is proposed to improve the machining procedure with respect to deterministic material removal, leading to predictable results. It is shown that, at an elevated surface temperature, the residual layer properties are modified in favor of improved etching performance. The etching behavior of N-BK7 is compared with fused silica to verify the optimality of the obtained results

A novel Deal–Grove-inspired model for fluorine-based plasma jet etching of borosilicate crown optical glass

The Deal–Grove model is a state-of-the-art approach proposed for describing the thermal oxidation of silicon and the oxide thickness over time. In this study, the Deal–Grove concept provided the inspiration for a mathematical model for simulating plasma jet-based dry etching process of borosilicate crown glass (N-BK7®). The whole process is contained in two so-called Deal–Grove parameters, which are extracted from experimental data including local etching depth and surface temperature distribution. The proposed model is extended for the evolution of dynamic etch profiles, and the obtained results are validated experimentally. By establishing such a model, it is possible to predict the effect of the residual layer and surface temperature on the evolution of local etching depths over dwell time