Distribution of CFTR mutations in Eastern Hungarians: Relevance to genetic testing and to the introduction of newborn screening for cystic fibrosis

AbstractBackgroundThe aim of this study was characterization of an updated distribution of CFTR mutations in a representative cohort of 40 CF patients with the classical form of the disease drawn from Eastern Hungary. Due to the homogeneity of the Hungarian population our data are generally applicable to other regions of the country, including the sizeable diaspora.MethodsWe utilized the recommended “cascade” CFTR mutation screening approach, initially using a commercial assay, followed by examination of the common “Slavic” deletion CFTRdele2,3(21kb). Subsequently, the entire CFTR coding region of the CFTR gene was sequenced in patients with yet unidentified mutations.ResultsThe Elucigene CF29Tm v2 assay detected 81.25% of all CF causing mutations. An addition of the CFTRdele2,3(21kb) increased the mutation detection rate to 86.25%. DNA sequencing enabled us to identify mutations on 79/80 CF alleles. Mutations [CFTRdele2,3(21kb), p.Gln685ThrfsX4 (2184insA) were found at an unusually high frequency, each comprising 5.00% of all CF alleles.ConclusionWe have identified common CF causing mutations in the Hungarian population with the most common mutations (p.Phe508del, p.Asn1303Lys, CFTRdele2,3(21kb), 2184insA, p.Gly542X, and p.Leu101X), comprising over 93.75% of all CF alleles. Obtained data are applicable to the improvement of DNA diagnostics in Hungary and beyond, and are the necessary prerequisite for the introduction of a nationwide “two tier” CF newborn screening program

Genetic Programming for the Identification of Nonlinear Input-Output Models

Linear-in-parameters models are quite widespread in process engineering, e.g. NAARX, polynomial ARMA models, etc. This paper proposes a new method for structure selection of these models

Population Based Algorithms for Batch Process Development

Abstract: The aim of this paper is to present how population based stochastic optimization algorithms can be used for batch process development. The theory of evolutionary and swarm optimization are overviewed and compared. A detailed application example is given to demonstrate how these tools can be applied to solve process optimization problems. The performances of Evolutionary Strategy, Particle Swarm Optimization, and the classical Sequential Quadratic Programming based algorithms are compared. 1. Evolutionary Algorithms The Evolutionary Algorithm (EA) is an optimization method which uses the computational model of natural selection. EAs work with a population of potential solutions to a problem, where each individual within the population represents a particular solution, generally represented in some form of genetic code. A single process engineering problem can contain a mixture of decision variable formats (numbers, symbols, and other structural parameters). Since the EA operates on a “genetic ” encoding of the optimized variables, diverse types of variable can be simultaneously optimized. The fitness value of the individual expresses how good the solution is at solving the problem. Better solutions are assigned higher values of fitness than worse performing solutions. The key of EA is that the fitness also determines how successful the individual will be at propagating its genes (its code) to subsequent generations. Table 1. A typical evolutionary algorithm procedure EA;

Interactive Evolutionary Computation in Process Engineering

In practical system identification, process optimization and controller design, it is often desirable to simultaneously handle several objectives and constraints. In some cases, these objectives and constraints are non-commensurable and they are not explicitly/mathematically available. This paper proposes a new subjective optimization method based on Interactive Evolutionary Computation (IEC) to handle these problems. IEC is an evolutionary algorithm whose fitness function is provided by human users. The whole approach has been implemented in MATLAB (EAsy-IEC Toolbox) and applied to two case-studies: tuning a Model Predictive Controller and temperature profile design of a batch beer fermenter. The results show that IEC is an e#cient and comfortable method to incorporate the prior knowledge of the user into optimization problems. The developed EASyIEC Toolbox (for Matlab) can be downloaded from the website of the authors: http://www.fmt.vein.hu/softcomp/EAsy

Application of Neumann’s Machine of Self- Reproduction- Evolution and Artificial life in Process Engineering

Abstract: The aim of this paper is to present the historical background of artificial life based stochastic optimization algorithms from the viewpoint of Neumann’s Self-reproduction scheme. The theory of evolutionary and swarm optimization are overviewed and compared from this viewpoint. A detailed application example is given to demonstrate how these tools can be applied to solve process optimization problems. The performances of Evolutionary Strategy, Particle Swar

Genetic Programming for System Identification

Linear in parameter models are quite widespread in process engineering, e.g. NAARX, polynomial ARMA models, etc. This paper proposes a new method for nonlinear structure selection for linear in parameter models. The method uses Genetic Programming (GP) to generate nonlinear input-output models represented in tree structure. The main idea of the paper is to apply Orthogonal Least Squares algorithm (OLS) to estimate the contribution of the branches of tree to the accuracy of the model. The proposed method speeds up the convergence of the GP and results in more robust and interpretable models. The simulation results show that the proposed tool provides an efficient and fast method to determine the order and the structure of nonlinear input-output models

Feedback Linearizing Control Using Hybrid Neural Networks Identified . . .

Globally Linearizing Control (GLC) is a control algorithm capable of using nonlinear process model directly. In GLC, mostly, first-principles models derived from dynamic mass, energy and momentum balances are used. When the process is not perfectly known, the unknown parts of the first principles model should be represented by black-box models, e.g. by neural networks. This paper is devoted to the identification and application of such hybrid models for GLC. It is shown that the first principles part of the model determines the dominant structure of the controller, while the black-box elements of the hybrid model are used as state and/or disturbance estimators. For the identification of the neural network elements of the hybrid model a sensitivity approach based algorithm has been developed. The underlying framework is illustrated by the temperature control of a continuous stirred tank reactor (CSTR) where a neural network is used to model the heat released by an exothermic chemical reaction

Interactive Evolutionary Computation in Identification of Dynamical Systems

this paper, Interactive Evolutionary Computation (IEC) is used to e#ectively handle these identification problems. IEC is an optimization method that adopts evolutionary computation (EC) among system optimization based on subjective human evaluation. The proposed approach has been implemented in MATLAB (EAsy-IEC Toolbox) and applied to the identification of a pilot batch reactor. The results show that IEC is an e#cient and comfortable method to incorporate a priori knowledge of the user into a user-guided optimization and identification problems. The developed EASy-IEC Toolbox can be downloaded from the website of the authors: http://www.fmt.vein.hu/softcomp/EAs

Interactive Evolutionary Computation for Model based Optimization of Batch Fermentation

At the optimization of temperature and feeding profiles of batch processes it is often desirable to consider several objectives and constraints into the optimization problem. During the beer fermentation a temperature profile is applied to drive the process so as to obey to certain constraints. The design of this temperature profile is an optimization problem where the objective is to minimize the operation time and optimize the quality of the beer. Similarly to other practical problems, these objectives and constraints are often non-commensurable and the objective functions are explicitly/mathematically not available. In this paper, Interactive Evolutionary Computation (IEC) is used to effectively handle such optimization problems. IEC is an evolutionary algorithm whose fitness function is provided by human users. The proposed approach has been implemented in MATLAB and applied to design temperature profile for beer fermentation process. The results show that IEC is an efficient and comfortable method to incorporate the priori knowledge of the user into the model based optimization of batch processes. A detailed description of the proposed approach helps the construction of the algorithms; still easier, the developed EAsy-IEC Toolbox and the beer fermentation model written KEY WORDS Model Based Optimization, Interactive Evolutionary Computing, Beer Fermentation