Genetic programming metamodel for rotating beams

This paper investigates the use of Genetic Programming (GP) to create an approximate model for the non-linear relationship between flexural stiffness, length, mass per unit length and rotation speed associated with rotating beams and their natural frequencies. GP, a relatively new form of artificial intelligence, is derived from the Darwinian concept of evolution and genetics and it creates computer programs to solve problems by manipulating their tree structures. GP predicts the size and structural complexity of the empirical model by minimizing the mean square error at the specified points of input-output relationship dataset. This dataset is generated using a finite element model. The validity of the GP-generated model is tested by comparing the natural frequencies at training and at additional input data points. It is found that by using a non-dimensional stiffness, it is possible to get simple and accurate function approximation for the natural frequency. This function approximation model is then used to study the relationships between natural frequency and various influencing parameters for uniform and tapered beams. The relations obtained with GP model agree well with FEM results and can be used for preliminary design and structural optimization studies.

A Haplotype at the UCP1 Gene Locus Contributes to Genetic Risk for Type 2 Diabetes in Asian Indians (CURES-72)

Background: The gene encoding for uncoupling protein-1 (UCP1) is considered to be a candidate gene for type 2 diabetes because of its role in thermogenesis and energy expenditure. The objective of the study was to examine whether genetic variations in the UCP1 gene are associated with type 2 diabetes and its related traits in Asian Indians. Methods: The study subjects, 810 type 2 diabetic subjects and 990 normal glucose tolerant (NGT) subjects, were chosen from the Chennai Urban Rural Epidemiological Study (CURES), an ongoing population-based study in southern India. The polymorphisms were genotyped using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Linkage disequilibrium (LD) was estimated from the estimates of haplotypic frequencies. Results: The three polymorphisms, namely -3826A -> G, an A -> C transition in the 5'-untranslated region (UTR) and Met229Leu, were not associated with type 2 diabetes. However, the frequency of the A-C-Met (-3826A -> G-5'UTR A -> C-Met229Leu) haplotype was significantly higher among the type 2 diabetic subjects (2.67%) compared with the NGT subjects (1.45%, P < 0.01). The odds ratio for type 2 diabetes for the individuals carrying the haplotype A-C-Met was 1.82 (95% confidence interval, 1.29-2.78, P = 0.009). Conclusions: The haplotype, A-C-Met, in the UCP1 gene is significantly associated with the increased genetic risk for developing type 2 diabetes in Asian Indians.

Phosphorylation status of the phosphoprotein P of rinderpest virus modulates transcription and replication of the genome

The phosphoprotein P of paramyxoviruses is known to play more than one role in genome transcription and replication. Phosphorylation of P at the NH2 terminus by cellular casein kinase II has been shown to be necessary for transcription of the genome in some of the viruses, while it is dispensable for replication. The phosphorylation null mutant of rinderpest virus P protein, in which three serine residues have been mutated, has been shown earlier to be non-functional in an in vivo minigenome replication/transcription system. In this work, we have shown that the phosphorylation of P protein is essential for transcription, whereas the null mutant is active in replication of the genome in vivo. The null mutant P acts as a transdominant repressor of transcriptional activity of wild-type P and as an activator of replication carried out by wild-type P protein. These results suggest the phosphorylation status of P may act as a replication switch during virus replication. We also show that the phosphorylation null mutant P is capable of interacting with L and N proteins and is able to form a tripartite complex of L-(N-P) when expressed in insect cells, similar to wild-type P protein.

Real-coded genetic algorithm for machining condition optimization

In this paper, we consider the machining condition optimization models presented in earlier studies. Finding the optimal combination of machining conditions within the constraints is a difficult task. Hence, in earlier studies standard optimization methods are used. The non-linear nature of the objective function, and the constraints that need to be satisfied makes it difficult to use the standard optimization methods for the solution. In this paper, we present a real coded genetic algorithm (RCGA), to find the optimal combination of machining conditions. We present various issues related to real coded genetic algorithm such as solution representation, crossover operators, and repair algorithm in detail. We also present the results obtained for these models using real coded genetic algorithm and discuss the advantages of using real coded genetic algorithm for these problems. From the results obtained, we conclude that real coded genetic algorithm is reliable and accurate for solving the machining condition optimization models.

Transcription of Human Resistin Gene Involves an Interaction of Sp1 with Peroxisome Proliferator-Activating Receptor Gamma (PPAR gamma)

Background: Resistin is a cysteine rich protein, mainly expressed and secreted by circulating human mononuclear cells. While several factors responsible for transcription of mouse resistin gene have been identified, not much is known about the factors responsible for the differential expression of human resistin.Methodology/Principal Finding: We show that the minimal promoter of human resistin lies within similar to 80 bp sequence upstream of the transcriptional start site (-240) whereas binding sites for cRel, CCAAT enhancer binding protein alpha (C/EBP-alpha), activating transcription factor 2 (ATF-2) and activator protein 1 (AP-1) transcription factors, important for induced expression, are present within sequences up to -619. Specificity Protein 1(Sp1) binding site (-276 to -295) is also present and an interaction of Sp1 with peroxisome proliferator activating receptor gamma (PPAR gamma) is necessary for constitutive expression in U937 cells. Indeed co-immunoprecipitation assay demonstrated a direct physical interaction of Sp1 with PPAR gamma in whole cell extracts of U937 cells. Phorbol myristate acetate (PMA) upregulated the expression of resistin mRNA in U937 cells by increasing the recruitment of Sp1, ATF-2 and PPAR gamma on the resistin gene promoter. Furthermore, PMA stimulation of U937 cells resulted in the disruption of Sp1 and PPAR gamma interaction. Chromatin immunoprecipitation (ChIP) assay confirmed the recruitment of transcription factors phospho ATF-2, Sp1, Sp3, PPAR gamma, chromatin modifier histone deacetylase 1 (HDAC1) and the acetylated form of histone H3 but not cRel, C/EBP-alpha and phospho c-Jun during resistingene transcription.Conclusion: Our findings suggest a complex interplay of Sp1 and PPAR gamma along with other transcription factors that drives the expression of resistin in human monocytic U937 cells.

Host factor Ebp1 inhibits rinderpest virus transcription in vivo

ErbB3 binding protein Ebp1 has been shown to downregulate ErbB3 receptor-mediated signaling to inhibit cell proliferation. Rinderpest virus belongs to the family Paramyxoviridae and is characterized by the presence of a non-segmented negative-sense RNA genome. In this work, we show that rinderpest virus infection of Vero cells leads to the down-regulation of the host factor Ebp1, at both the mRNA and protein levels. Ebp1 protein has been shown to co-localize with viral inclusion bodies in infected cells, and it is packaged into virions, presumably through its interaction with the N protein or the N-RNA itself. Overexpression of Ebp1 inhibits viral transcription and multiplication in infected cells, suggesting that a mutual antagonism operates between host factor Ebp1 and the virus.

A genetic algorithm with characteristic amplification through multiple geographically isolated populations and varied fitness landscapes

This paper proposes a new approach, wherein multiple populations are evolved on different landscapes. The problem statement is broken down, to describe discrete characteristics. Each landscape, described by its fitness landscape is used to optimize or amplify a certain characteristic or set of characteristics. Individuals from each of these populations are kept geographically isolated from each other Each population is evolved individually. After a predetermined number of evolutions, the system of populations is analysed against a normalized fitness function. Depending on this score and a predefined merging scheme, the populations are merged, one at a time, while continuing evolution. Merging continues until only one final population remains. This population is then evolved, following which the resulting population will contain the optimal solution. The final resulting population will contain individuals which have been optimized against all characteristics as desired by the problem statement. Each individual population is optimized for a local maxima. Thus when populations are merged, the effect is to produce a new population which is closer to the global maxima.

A genetic algorithm with characteristic amplification through multiple geographically isolated populations and varied fitness landscapes

This paper proposes a new approach, wherein multiple populations are evolved on different landscapes. The problem statement is broken down, to describe discrete characteristics. Each landscape, described by its fitness landscape is used to optimize or amplify a certain characteristic or set of characteristics. Individuals from each of these populations are kept geographically isolated from each other Each population is evolved individually. After a predetermined number of evolutions, the system of populations is analysed against a normalized fitness function. Depending on this score and a predefined merging scheme, the populations are merged, one at a time, while continuing evolution. Merging continues until only one final population remains. This population is then evolved, following which the resulting population will contain the optimal solution. The final resulting population will contain individuals which have been optimized against all characteristics as desired by the problem statement. Each individual population is optimized for a local maxima. Thus when populations are merged, the effect is to produce a new population which is closer to the global maxima.

A Dynamic Migration Model for Self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

A real coded genetic algorithm for data partitioning and scheduling in networks with arbitrary processor release time

The problem of scheduling divisible loads in distributed computing systems, in presence of processor release time is considered. The objective is to find the optimal sequence of load distribution and the optimal load fractions assigned to each processor in the system such that the processing time of the entire processing load is a minimum. This is a difficult combinatorial optimization problem and hence genetic algorithms approach is presented for its solution.

Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using nondominated sorting genetic algorithm

Many optimal control problems are characterized by their multiple performance measures that are often noncommensurable and competing with each other. The presence of multiple objectives in a problem usually give rise to a set of optimal solutions, largely known as Pareto-optimal solutions. Evolutionary algorithms have been recognized to be well suited for multi-objective optimization because of their capability to evolve a set of nondominated solutions distributed along the Pareto front. This has led to the development of many evolutionary multi-objective optimization algorithms among which Nondominated Sorting Genetic Algorithm (NSGA and its enhanced version NSGA-II) has been found effective in solving a wide variety of problems. Recently, we reported a genetic algorithm based technique for solving dynamic single-objective optimization problems, with single as well as multiple control variables, that appear in fed-batch bioreactor applications. The purpose of this study is to extend this methodology for solution of multi-objective optimal control problems under the framework of NSGA-II. The applicability of the technique is illustrated by solving two optimal control problems, taken from literature, which have usually been solved by several methods as single-objective dynamic optimization problems. (C) 2004 Elsevier Ltd. All rights reserved.

Characterization of the human SLC22A18 gene promoter and its regulation by the transcription factor Sp1

SLC22A18, a poly-specific organic cation transporter, is paternally imprinted in humans and mice. It shows loss-of-heterozygosity in childhood and adult tumors, and gain-of-imprinting in hepatocarcinomas and breast cancers. Despite the importance of this gene, its transcriptional regulation has not been studied, and the promoter has not yet been characterized. We therefore set out to identify the potential cis-regulatory elements including the promoter of this gene. The luciferase reporter assay in human cells indicated that a region from -120 by to +78 by is required for the core promoter activity. No consensus TATA or CHAT boxes were found in this region, but two Sp1 binding sites were conserved in human, chimpanzee, mouse and rat. Mutational analysis of the two Sp1 sites suggested their requirement for the promoter activity. Chromatin-immunoprecipitation showed binding of Sp1 to the promoter region in vivo. Overexpression of Sp1 in Drosophila Sp1-null SL2 cells suggested that Sp1 is the transactivator of the promoter. The human core promoter was functional in mouse 3T3 and monkey COS7 cells. We found a CpG island which spanned the core promoter and exon 1. COBRA technique did not reveal promoter methylation in 10 normal oral tissues, 14 oral tumors, and two human cell lines HuH7 and A549. This study provides the first insight into the mechanism that controls expression of this imprinted tumor suppressor gene. A COBRA-based assay has been developed to look for promoter methylation in different cancers. The present data will help to understand the regulation of this gene and its role in tumorigenesis. (C) 2008 Elsevier B.V. All rights reserved.

Characterization of the human SLC22A18 gene promoter and its regulation by the transcription factor Sp1

SLC22A18, a poly-specific organic cation transporter, is paternally imprinted in humans and mice. It shows loss-of-heterozygosity in childhood and adult tumors, and gain-of-imprinting in hepatocarcinomas and breast cancers. Despite the importance of this gene, its transcriptional regulation has not been studied, and the promoter has not yet been characterized. We therefore set out to identify the potential cis-regulatory elements including the promoter of this gene. The luciferase reporter assay in human cells indicated that a region from -120 by to +78 by is required for the core promoter activity. No consensus TATA or CHAT boxes were found in this region, but two Sp1 binding sites were conserved in human, chimpanzee, mouse and rat. Mutational analysis of the two Sp1 sites suggested their requirement for the promoter activity. Chromatin-immunoprecipitation showed binding of Sp1 to the promoter region in vivo. Overexpression of Sp1 in Drosophila Sp1-null SL2 cells suggested that Sp1 is the transactivator of the promoter. The human core promoter was functional in mouse 3T3 and monkey COS7 cells. We found a CpG island which spanned the core promoter and exon 1. COBRA technique did not reveal promoter methylation in 10 normal oral tissues, 14 oral tumors, and two human cell lines HuH7 and A549. This study provides the first insight into the mechanism that controls expression of this imprinted tumor suppressor gene. A COBRA-based assay has been developed to look for promoter methylation in different cancers. The present data will help to understand the regulation of this gene and its role in tumorigenesis. (C) 2008 Elsevier B.V. All rights reserved.

A Dynamic Migration Model for self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

Novel insertion and deletion mutants of RpoB that render Mycobacterium smegmatis RNA polymerase resistant to rifampicin-mediated inhibition of transcription

The startling increase in the occurrence of rifampicin (Rif) resistance in the clinical isolates of Mycobacterium tuberculosis worldwide is posing a serious concern to tuberculosis management. The majority of Rif resistance in bacteria arises from mutations in the RpoB subunit of the RNA polymerase. We isolated M. smegmatis strains harbouring either an insertion (6 aa) or a deletion (10 aa) in their RpoB proteins. Although these strains showed a compromised fitness for growth in 7H9 Middlebrook medium, their resistance to Rif was remarkably high. The attenuated growth of the strains correlated with decreased specific activities of the RNA polymerases from the mutants. While the RNA polymerases from the parent or a mutant strain (harbouring a frequently occurring mutation, H442Y, in RpoB) were susceptible to Rif-mediated inhibition of transcription from calf thymus DNA, those from the insertion and deletion mutants were essentially refractory to such inhibition. Three-dimensional structure modelling revealed that the RpoB amino acids that interact with Rif are either deleted or unable to interact with Rif due to their unsuitable spatial positioning in these mutants. We discuss possible uses of the RpoB mutants in studying transcriptional regulation in mycobacteria and as potential targets for drug design.