Tractable Pareto Optimization of Temporal Preferences

Science.gov (United States)

Morris, Robert; Morris, Paul; Khatib, Lina; Venable, Brent

2003-01-01

This paper focuses on temporal constraint problems where the objective is to optimize a set of local preferences for when events occur. In previous work, a subclass of these problems has been formalized as a generalization of Temporal CSPs, and a tractable strategy for optimization has been proposed, where global optimality is defined as maximizing the minimum of the component preference values. This criterion for optimality, which we call 'Weakest Link Optimization' (WLO), is known to have limited practical usefulness because solutions are compared only on the basis of their worst value; thus, there is no requirement to improve the other values. To address this limitation, we introduce a new algorithm that re-applies WLO iteratively in a way that leads to improvement of all the values. We show the value of this strategy by proving that, with suitable preference functions, the resulting solutions are Pareto Optimal.

Mouse Chromosome Engineering for Modeling Human Disease

OpenAIRE

van der Weyden, Louise; Bradley, Allan

2006-01-01

Chromosomal rearrangements occur frequently in humans and can be disease-associated or phenotypically neutral. Recent technological advances have led to the discovery of copy-number changes previously undetected by cytogenetic techniques. To understand the genetic consequences of such genomic changes, these mutations need to be modeled in experimentally tractable systems. The mouse is an excellent organism for this analysis because of its biological and genetic similarity to humans, and the e...

The filamentous fungus Sordaria macrospora as a genetic model to study fruiting body development.

Science.gov (United States)

Teichert, Ines; Nowrousian, Minou; Pöggeler, Stefanie; Kück, Ulrich

2014-01-01

Filamentous fungi are excellent experimental systems due to their short life cycles as well as easy and safe manipulation in the laboratory. They form three-dimensional structures with numerous different cell types and have a long tradition as genetic model organisms used to unravel basic mechanisms underlying eukaryotic cell differentiation. The filamentous ascomycete Sordaria macrospora is a model system for sexual fruiting body (perithecia) formation. S. macrospora is homothallic, i.e., self-fertile, easily genetically tractable, and well suited for large-scale genomics, transcriptomics, and proteomics studies. Specific features of its life cycle and the availability of a developmental mutant library make it an excellent system for studying cellular differentiation at the molecular level. In this review, we focus on recent developments in identifying gene and protein regulatory networks governing perithecia formation. A number of tools have been developed to genetically analyze developmental mutants and dissect transcriptional profiles at different developmental stages. Protein interaction studies allowed us to identify a highly conserved eukaryotic multisubunit protein complex, the striatin-interacting phosphatase and kinase complex and its role in sexual development. We have further identified a number of proteins involved in chromatin remodeling and transcriptional regulation of fruiting body development. Furthermore, we review the involvement of metabolic processes from both primary and secondary metabolism, and the role of nutrient recycling by autophagy in perithecia formation. Our research has uncovered numerous players regulating multicellular development in S. macrospora. Future research will focus on mechanistically understanding how these players are orchestrated in this fungal model system. Copyright © 2014 Elsevier Inc. All rights reserved.

Analytical Evaluation of Chunk-Based Tractable Multi-cell OFDMA system

Directory of Open Access Journals (Sweden)

P.Kavitha

2018-04-01

Full Text Available This paper evaluates thoroughly the performance of multi-cell OFDMA system. The two types of deployment in multi-cell OFDMA system, such as Strict Fractional Frequency Reuse (FFR and Soft FFR (SFR were evaluated. In order to model the base station locations, homogeneous Poisson point processes were used, i.e. tractable model instead of hexagonal grid was considered. In order to reduce complexity, chunk-based resource allocation scheme was embedded. Each cell divides the users into the users of the central cell area and the users of the cell edge area according to their average received Signal to Interference and Noise Ratio (SINR compared with FFR threshold. The primary stage of the analysis includes the spectral efficiency’s expression deriving from these two deployment scenarios, followed by the analysis with the use of coverage probability. However, the improvement of spectral efficiency is achieved in the case of SFR. On the contrary, coverage probability is far improved by using strict FFR scheme. Through numerical anaysis, We have shown that the optimal FFR threshold to achieve the highest spectral efficiency was 12 dB for both Strict FFR as well as SFR.

Long-Term Adult Feline Liver Organoid Cultures for Disease Modeling of Hepatic Steatosis

NARCIS (Netherlands)

Kruitwagen, Hedwig S.; Oosterhoff, Loes A.; Vernooij, Ingrid G.W.H.; Schrall, Ingrid M.; van Wolferen, Monique E.; Bannink, Farah; Roesch, Camille; van Uden, Lisa; Molenaar, Martijn R.; Helms, J. Bernd; Grinwis, Guy C.M.; Verstegen, Monique M.A.; van der Laan, Luc J.W.; Huch, Meritxell; Geijsen, Niels; Vries, Robert G.; Clevers, Hans; Rothuizen, Jan; Schotanus, Baukje A.; Penning, Louis C.; Spee, Bart

2017-01-01

Hepatic steatosis is a highly prevalent liver disease, yet research is hampered by the lack of tractable cellular and animal models. Steatosis also occurs in cats, where it can cause severe hepatic failure. Previous studies demonstrate the potential of liver organoids for modeling genetic diseases.

A Tractable Disequilbrium Framework for Integrating Computational Thermodynamics and Geodynamics

Science.gov (United States)

Spiegelman, M. W.; Tweed, L. E. L.; Evans, O.; Kelemen, P. B.; Wilson, C. R.

2017-12-01

The consistent integration of computational thermodynamics and geodynamics is essential for exploring and understanding a wide range of processes from high-PT magma dynamics in the convecting mantle to low-PT reactive alteration of the brittle crust. Nevertheless, considerable challenges remain for coupling thermodynamics and fluid-solid mechanics within computationally tractable and insightful models. Here we report on a new effort, part of the ENKI project, that provides a roadmap for developing flexible geodynamic models of varying complexity that are thermodynamically consistent with established thermodynamic models. The basic theory is derived from the disequilibrium thermodynamics of De Groot and Mazur (1984), similar to Rudge et. al (2011, GJI), but extends that theory to include more general rheologies, multiple solid (and liquid) phases and explicit chemical reactions to describe interphase exchange. Specifying stoichiometric reactions clearly defines the compositions of reactants and products and allows the affinity of each reaction (A = -Δ/Gr) to be used as a scalar measure of disequilibrium. This approach only requires thermodynamic models to return chemical potentials of all components and phases (as well as thermodynamic quantities for each phase e.g. densities, heat capacity, entropies), but is not constrained to be in thermodynamic equilibrium. Allowing meta-stable phases mitigates some of the computational issues involved with the introduction and exhaustion of phases. Nevertheless, for closed systems, these problems are guaranteed to evolve to the same equilibria predicted by equilibrium thermodynamics. Here we illustrate the behavior of this theory for a range of simple problems (constructed with our open-source model builder TerraFERMA) that model poro-viscous behavior in the well understood Fo-Fa binary phase loop. Other contributions in this session will explore a range of models with more petrologically interesting phase diagrams as well as

Developing the anemone Aiptasia as a tractable model for cnidarian-dinoflagellate symbiosis: the transcriptome of aposymbiotic A. pallida.

Science.gov (United States)

Lehnert, Erik M; Burriesci, Matthew S; Pringle, John R

2012-06-22

Coral reefs are hotspots of oceanic biodiversity, forming the foundation of ecosystems that are important both ecologically and for their direct practical impacts on humans. Corals are declining globally due to a number of stressors, including rising sea-surface temperatures and pollution; such stresses can lead to a breakdown of the essential symbiotic relationship between the coral host and its endosymbiotic dinoflagellates, a process known as coral bleaching. Although the environmental stresses causing this breakdown are largely known, the cellular mechanisms of symbiosis establishment, maintenance, and breakdown are still largely obscure. Investigating the symbiosis using an experimentally tractable model organism, such as the small sea anemone Aiptasia, should improve our understanding of exactly how the environmental stressors affect coral survival and growth. We assembled the transcriptome of a clonal population of adult, aposymbiotic (dinoflagellate-free) Aiptasia pallida from ~208 million reads, yielding 58,018 contigs. We demonstrated that many of these contigs represent full-length or near-full-length transcripts that encode proteins similar to those from a diverse array of pathways in other organisms, including various metabolic enzymes, cytoskeletal proteins, and neuropeptide precursors. The contigs were annotated by sequence similarity, assigned GO terms, and scanned for conserved protein domains. We analyzed the frequency and types of single-nucleotide variants and estimated the size of the Aiptasia genome to be ~421 Mb. The contigs and annotations are available through NCBI (Transcription Shotgun Assembly database, accession numbers JV077153-JV134524) and at http://pringlelab.stanford.edu/projects.html. The availability of an extensive transcriptome assembly for A. pallida will facilitate analyses of gene-expression changes, identification of proteins of interest, and other studies in this important emerging model system.

Developing the anemone Aiptasia as a tractable model for cnidarian-dinoflagellate symbiosis: the transcriptome of aposymbiotic A. pallida

Directory of Open Access Journals (Sweden)

Lehnert Erik M

2012-06-01

Full Text Available Abstract Background Coral reefs are hotspots of oceanic biodiversity, forming the foundation of ecosystems that are important both ecologically and for their direct practical impacts on humans. Corals are declining globally due to a number of stressors, including rising sea-surface temperatures and pollution; such stresses can lead to a breakdown of the essential symbiotic relationship between the coral host and its endosymbiotic dinoflagellates, a process known as coral bleaching. Although the environmental stresses causing this breakdown are largely known, the cellular mechanisms of symbiosis establishment, maintenance, and breakdown are still largely obscure. Investigating the symbiosis using an experimentally tractable model organism, such as the small sea anemone Aiptasia, should improve our understanding of exactly how the environmental stressors affect coral survival and growth. Results We assembled the transcriptome of a clonal population of adult, aposymbiotic (dinoflagellate-free Aiptasia pallida from ~208 million reads, yielding 58,018 contigs. We demonstrated that many of these contigs represent full-length or near-full-length transcripts that encode proteins similar to those from a diverse array of pathways in other organisms, including various metabolic enzymes, cytoskeletal proteins, and neuropeptide precursors. The contigs were annotated by sequence similarity, assigned GO terms, and scanned for conserved protein domains. We analyzed the frequency and types of single-nucleotide variants and estimated the size of the Aiptasia genome to be ~421 Mb. The contigs and annotations are available through NCBI (Transcription Shotgun Assembly database, accession numbers JV077153-JV134524 and at http://pringlelab.stanford.edu/projects.html. Conclusions The availability of an extensive transcriptome assembly for A. pallida will facilitate analyses of gene-expression changes, identification of proteins of interest, and other studies in this

Phenomics, Genomics and Genetics in Plasmodium vinckei

KAUST Repository

Ramaprasad, Abhinay

2017-11-01

Rodent malaria parasites (RMPs) serve as tractable models for experimental genetics, and as valuable tools to study malaria parasite biology and host-parasitevector interactions. Plasmodium vinckei, one of four RMPs adapted to laboratory mice, is the most geographically widespread species and displays considerable phenotypic and genotypic diversity amongst its subspecies and strains. The phenotypes and genotypes of P. vinckei isolates have been relatively less characterized compared to other RMPs, hampering its use as an experimental model for malaria. Here, we have studied the phenotypes and sequenced the genomes and transcriptomes of ten P. vinckei isolates including representatives of all five subspecies, all of which were collected from wild thicket rats (Thamnomys rutilans) in sub-Saharan Central Africa between the late 1940s and mid 1960s. We have generated a comprehensive resource for P. vinckei comprising of five high-quality reference genomes, growth profiles and genotypes of P. vinckei isolates, and expression profiles of genes across the intra-erythrocytic developmental stages of the parasite. We observe significant phenotypic and genotypic diversity among P. vinckei isolates, making them particularly suitable for classical genetics and genomics-driven studies on malaria parasite biology. As part of a proof of concept study, we have shown that experimental genetic crosses can be performed between P. vinckei parasites to potentially identify genotype-phenotype relationships. We have also shown that they are amenable to genetic manipulation in the laboratory.

Graphical models for genetic analyses

DEFF Research Database (Denmark)

Lauritzen, Steffen Lilholt; Sheehan, Nuala A.

2003-01-01

This paper introduces graphical models as a natural environment in which to formulate and solve problems in genetics and related areas. Particular emphasis is given to the relationships among various local computation algorithms which have been developed within the hitherto mostly separate areas...... of graphical models and genetics. The potential of graphical models is explored and illustrated through a number of example applications where the genetic element is substantial or dominating....

Advances in faba bean genetics and genomics

Directory of Open Access Journals (Sweden)

Donal Martin O'Sullivan

2016-08-01

Full Text Available Vicia faba L, is a globally important grain legume whose main centres of diversity are the Fertile Crescent and Mediterranean basin. Because of its small number (six of exceptionally large and easily observed chromosomes it became a model species for plant cytogenetics the 70s and 80s. It is somewhat ironic therefore, that the emergence of more genomically tractable model plant species such as Arabidopsis and Medicago coincided with a marked decline in genome research on the formerly favoured plant cytogenetic model. Thus, as ever higher density molecular marker coverage and dense genetic and even complete genome sequence maps of key crop and model species emerged through the 1990s and early 2000s, genetic and genome knowledge of Vicia faba lagged far behind other grain legumes such as soybean, common bean and pea.However, cheap sequencing technologies have stimulated the production of deep transcriptome coverage from several tissue types and numerous distinct cultivars in recent years. This has permitted the reconstruction of the faba bean meta-transcriptome and has fuelled development of extensive sets of Simple Sequence Repeat and Single Nucleotide Polymorphism (SNP markers. Genetics of faba bean stretches back to the 1930s, but it was not until 1993 that DNA markers were used to construct genetic maps. A series of Random Amplified Polymorphic DNA-based genetic studies mainly targeted at quantitative loci underlying resistance to a series of biotic and abiotic stresses were conducted during the 1990’s and early 2000s. More recently, SNP-based genetic maps have permitted chromosome intervals of interest to be aligned to collinear segments of sequenced legume genomes such as the model legume Medicago truncatula, which in turn opens up the possibility for hypotheses on gene content, order and function to be translated from model to crop. Some examples of where knowledge of gene content and function have already been productively exploited are

Analysis of tractable distortion metrics for EEG compression applications

International Nuclear Information System (INIS)

Bazán-Prieto, Carlos; Blanco-Velasco, Manuel; Cruz-Roldán, Fernando; Cárdenas-Barrera, Julián

2012-01-01

Coding distortion in lossy electroencephalographic (EEG) signal compression methods is evaluated through tractable objective criteria. The percentage root-mean-square difference, which is a global and relative indicator of the quality held by reconstructed waveforms, is the most widely used criterion. However, this parameter does not ensure compliance with clinical standard guidelines that specify limits to allowable noise in EEG recordings. As a result, expert clinicians may have difficulties interpreting the resulting distortion of the EEG for a given value of this parameter. Conversely, the root-mean-square error is an alternative criterion that quantifies distortion in understandable units. In this paper, we demonstrate that the root-mean-square error is better suited to control and to assess the distortion introduced by compression methods. The experiments conducted in this paper show that the use of the root-mean-square error as target parameter in EEG compression allows both clinicians and scientists to infer whether coding error is clinically acceptable or not at no cost for the compression ratio. (paper)

A tractable algorithm for the wellfounded model

NARCIS (Netherlands)

Jonker, C.M.; Renardel de Lavalette, G.R.

In the area of general logic programming (negated atoms allowed in the bodies of rules) and reason maintenance systems, the wellfounded model (first defined by Van Gelder, Ross and Schlipf in 1988) is generally considered to be the declarative semantics of the program. In this paper we present

Using genetic data to estimate diffusion rates in heterogeneous landscapes.

Science.gov (United States)

Roques, L; Walker, E; Franck, P; Soubeyrand, S; Klein, E K

2016-08-01

Having a precise knowledge of the dispersal ability of a population in a heterogeneous environment is of critical importance in agroecology and conservation biology as it can provide management tools to limit the effects of pests or to increase the survival of endangered species. In this paper, we propose a mechanistic-statistical method to estimate space-dependent diffusion parameters of spatially-explicit models based on stochastic differential equations, using genetic data. Dividing the total population into subpopulations corresponding to different habitat patches with known allele frequencies, the expected proportions of individuals from each subpopulation at each position is computed by solving a system of reaction-diffusion equations. Modelling the capture and genotyping of the individuals with a statistical approach, we derive a numerically tractable formula for the likelihood function associated with the diffusion parameters. In a simulated environment made of three types of regions, each associated with a different diffusion coefficient, we successfully estimate the diffusion parameters with a maximum-likelihood approach. Although higher genetic differentiation among subpopulations leads to more accurate estimations, once a certain level of differentiation has been reached, the finite size of the genotyped population becomes the limiting factor for accurate estimation.

Behavior genetic modeling of human fertility

DEFF Research Database (Denmark)

Rodgers, J L; Kohler, H P; Kyvik, K O

2001-01-01

Behavior genetic designs and analysis can be used to address issues of central importance to demography. We use this methodology to document genetic influence on human fertility. Our data come from Danish twin pairs born from 1953 to 1959, measured on age at first attempt to get pregnant (First......Try) and number of children (NumCh). Behavior genetic models were fitted using structural equation modeling and DF analysis. A consistent medium-level additive genetic influence was found for NumCh, equal across genders; a stronger genetic influence was identified for FirstTry, greater for females than for males....... A bivariate analysis indicated significant shared genetic variance between NumCh and FirstTry....

Space-Bounded Church-Turing Thesis and Computational Tractability of Closed Systems.

Science.gov (United States)

Braverman, Mark; Schneider, Jonathan; Rojas, Cristóbal

2015-08-28

We report a new limitation on the ability of physical systems to perform computation-one that is based on generalizing the notion of memory, or storage space, available to the system to perform the computation. Roughly, we define memory as the maximal amount of information that the evolving system can carry from one instant to the next. We show that memory is a limiting factor in computation even in lieu of any time limitations on the evolving system-such as when considering its equilibrium regime. We call this limitation the space-bounded Church-Turing thesis (SBCT). The SBCT is supported by a simulation assertion (SA), which states that predicting the long-term behavior of bounded-memory systems is computationally tractable. In particular, one corollary of SA is an explicit bound on the computational hardness of the long-term behavior of a discrete-time finite-dimensional dynamical system that is affected by noise. We prove such a bound explicitly.

Detection vs. selection: integration of genetic, epigenetic and environmental cues in fluctuating environments.

Science.gov (United States)

McNamara, John M; Dall, Sasha R X; Hammerstein, Peter; Leimar, Olof

2016-10-01

There are many inputs during development that influence an organism's fit to current or upcoming environments. These include genetic effects, transgenerational epigenetic influences, environmental cues and developmental noise, which are rarely investigated in the same formal framework. We study an analytically tractable evolutionary model, in which cues are integrated to determine mature phenotypes in fluctuating environments. Environmental cues received during development and by the mother as an adult act as detection-based (individually observed) cues. The mother's phenotype and a quantitative genetic effect act as selection-based cues (they correlate with environmental states after selection). We specify when such cues are complementary and tend to be used together, and when using the most informative cue will predominate. Thus, we extend recent analyses of the evolutionary implications of subsets of these effects by providing a general diagnosis of the conditions under which detection and selection-based influences on development are likely to evolve and coexist. © 2016 John Wiley & Sons Ltd/CNRS.

Drosophila melanogaster as a model organism to study nanotoxicity.

Science.gov (United States)

Ong, Cynthia; Yung, Lin-Yue Lanry; Cai, Yu; Bay, Boon-Huat; Baeg, Gyeong-Hun

2015-05-01

Drosophila melanogaster has been used as an in vivo model organism for the study of genetics and development since 100 years ago. Recently, the fruit fly Drosophila was also developed as an in vivo model organism for toxicology studies, in particular, the field of nanotoxicity. The incorporation of nanomaterials into consumer and biomedical products is a cause for concern as nanomaterials are often associated with toxicity in many in vitro studies. In vivo animal studies of the toxicity of nanomaterials with rodents and other mammals are, however, limited due to high operational cost and ethical objections. Hence, Drosophila, a genetically tractable organism with distinct developmental stages and short life cycle, serves as an ideal organism to study nanomaterial-mediated toxicity. This review discusses the basic biology of Drosophila, the toxicity of nanomaterials, as well as how the Drosophila model can be used to study the toxicity of various types of nanomaterials.

Non-Genetic Engineering Approaches for Isolating and Generating Novel Yeasts for Industrial Applications

Science.gov (United States)

Chambers, P. J.; Bellon, J. R.; Schmidt, S. A.; Varela, C.; Pretorius, I. S.

Generating novel yeast strains for industrial applications should be quite straightforward; after all, research into the genetics, biochemistry and physiology of Baker's Yeast, Saccharomyces cerevisiae, has paved the way for many advances in the modern biological sciences. We probably know more about this humble eukaryote than any other, and it is the most tractable of organisms for manipulation using modern genetic engineering approaches. In many countries, however, there are restrictions on the use of genetically-modified organisms (GMOs), particularly in foods and beverages, and the level of consumer acceptance of GMOs is, at best, variable. Thus, many researchers working with industrial yeasts use genetic engineering techniques primarily as research tools, and strain development continues to rely on non-GM technologies. This chapter explores the non-GM tools and strategies available to such researchers.

Marinobacter subterrani, a genetically tractable neutrophilic Fe(II-oxidizing strain isolated from the Soudan Iron Mine

Directory of Open Access Journals (Sweden)

Benjamin Michael Bonis

2015-07-01

Full Text Available We report the isolation, characterization, and development of a robust genetic system for a halophilic, Fe(II-oxidizing bacterium isolated from a vertical borehole originating 714 m below the surface located in the Soudan Iron Mine in northern Minnesota, USA. Sequence analysis of the 16S rRNA gene places the isolate in the genus Marinobacter of the Gammaproteobacteria. The genome of the isolate was sequenced using a combination of short- and long-read technologies resulting in 2 contigs representing a 4.4 Mbp genome. Using genomic information, we used a suicide vector for targeted deletion of specific flagellin genes, resulting in a motility-deficient mutant. The motility mutant was successfully complemented by expression of the deleted genes in trans. Random mutagenesis using a transposon was also achieved. Capable of heterotrophic growth, this isolate represents a microaerophilic Fe(II-oxidizing species for which a system for both directed and random mutagenesis has been established. Analysis of 16S rDNA suggests Marinobacter represents a major taxon in the mine, and genetic interrogation of this genus may offer insight into the structure of deep subsurface communities as well as an additional tool for analyzing nutrient and element cycling in the subsurface ecosystem.

Numerical Simulation of a Tumor Growth Dynamics Model Using Particle Swarm Optimization.

Science.gov (United States)

Wang, Zhijun; Wang, Qing

Tumor cell growth models involve high-dimensional parameter spaces that require computationally tractable methods to solve. To address a proposed tumor growth dynamics mathematical model, an instance of the particle swarm optimization method was implemented to speed up the search process in the multi-dimensional parameter space to find optimal parameter values that fit experimental data from mice cancel cells. The fitness function, which measures the difference between calculated results and experimental data, was minimized in the numerical simulation process. The results and search efficiency of the particle swarm optimization method were compared to those from other evolutional methods such as genetic algorithms.

The Drosophila melanogaster host model

Science.gov (United States)

Igboin, Christina O.; Griffen, Ann L.; Leys, Eugene J.

2012-01-01

The deleterious and sometimes fatal outcomes of bacterial infectious diseases are the net result of the interactions between the pathogen and the host, and the genetically tractable fruit fly, Drosophila melanogaster, has emerged as a valuable tool for modeling the pathogen–host interactions of a wide variety of bacteria. These studies have revealed that there is a remarkable conservation of bacterial pathogenesis and host defence mechanisms between higher host organisms and Drosophila. This review presents an in-depth discussion of the Drosophila immune response, the Drosophila killing model, and the use of the model to examine bacterial–host interactions. The recent introduction of the Drosophila model into the oral microbiology field is discussed, specifically the use of the model to examine Porphyromonas gingivalis–host interactions, and finally the potential uses of this powerful model system to further elucidate oral bacterial-host interactions are addressed. PMID:22368770

The Drosophila melanogaster host model

Directory of Open Access Journals (Sweden)

Christina O. Igboin

2012-02-01

Full Text Available The deleterious and sometimes fatal outcomes of bacterial infectious diseases are the net result of the interactions between the pathogen and the host, and the genetically tractable fruit fly, Drosophila melanogaster, has emerged as a valuable tool for modeling the pathogen–host interactions of a wide variety of bacteria. These studies have revealed that there is a remarkable conservation of bacterial pathogenesis and host defence mechanisms between higher host organisms and Drosophila. This review presents an in-depth discussion of the Drosophila immune response, the Drosophila killing model, and the use of the model to examine bacterial–host interactions. The recent introduction of the Drosophila model into the oral microbiology field is discussed, specifically the use of the model to examine Porphyromonas gingivalis–host interactions, and finally the potential uses of this powerful model system to further elucidate oral bacterial-host interactions are addressed.

The Drosophila melanogaster host model.

Science.gov (United States)

Igboin, Christina O; Griffen, Ann L; Leys, Eugene J

2012-01-01

The deleterious and sometimes fatal outcomes of bacterial infectious diseases are the net result of the interactions between the pathogen and the host, and the genetically tractable fruit fly, Drosophila melanogaster, has emerged as a valuable tool for modeling the pathogen-host interactions of a wide variety of bacteria. These studies have revealed that there is a remarkable conservation of bacterial pathogenesis and host defence mechanisms between higher host organisms and Drosophila. This review presents an in-depth discussion of the Drosophila immune response, the Drosophila killing model, and the use of the model to examine bacterial-host interactions. The recent introduction of the Drosophila model into the oral microbiology field is discussed, specifically the use of the model to examine Porphyromonas gingivalis-host interactions, and finally the potential uses of this powerful model system to further elucidate oral bacterial-host interactions are addressed.

Eco-genetic modeling of contemporary life-history evolution.

Science.gov (United States)

Dunlop, Erin S; Heino, Mikko; Dieckmann, Ulf

2009-10-01

We present eco-genetic modeling as a flexible tool for exploring the course and rates of multi-trait life-history evolution in natural populations. We build on existing modeling approaches by combining features that facilitate studying the ecological and evolutionary dynamics of realistically structured populations. In particular, the joint consideration of age and size structure enables the analysis of phenotypically plastic populations with more than a single growth trajectory, and ecological feedback is readily included in the form of density dependence and frequency dependence. Stochasticity and life-history trade-offs can also be implemented. Critically, eco-genetic models permit the incorporation of salient genetic detail such as a population's genetic variances and covariances and the corresponding heritabilities, as well as the probabilistic inheritance and phenotypic expression of quantitative traits. These inclusions are crucial for predicting rates of evolutionary change on both contemporary and longer timescales. An eco-genetic model can be tightly coupled with empirical data and therefore may have considerable practical relevance, in terms of generating testable predictions and evaluating alternative management measures. To illustrate the utility of these models, we present as an example an eco-genetic model used to study harvest-induced evolution of multiple traits in Atlantic cod. The predictions of our model (most notably that harvesting induces a genetic reduction in age and size at maturation, an increase or decrease in growth capacity depending on the minimum-length limit, and an increase in reproductive investment) are corroborated by patterns observed in wild populations. The predicted genetic changes occur together with plastic changes that could phenotypically mask the former. Importantly, our analysis predicts that evolutionary changes show little signs of reversal following a harvest moratorium. This illustrates how predictions offered by

Comparative evaluation of 13 yeast species in the Yarrowia clade on lignocellulosic biomass hydrolysate and genetic engineering of inhibitor tolerant strains for lipid and biofuel production

Science.gov (United States)

Yarrowia lipolytica is an oleaginous yeast that has garnered interest for commercial production of single cell oil and other fatty acid-derived chemicals because of its GRAS status and genetic tractability. Three recent peer-reviewed studies have highlighted the possibility of lipid production by th...

Genetic coding and gene expression - new Quadruplet genetic coding model

Science.gov (United States)

Shankar Singh, Rama

2012-07-01

Successful demonstration of human genome project has opened the door not only for developing personalized medicine and cure for genetic diseases, but it may also answer the complex and difficult question of the origin of life. It may lead to making 21st century, a century of Biological Sciences as well. Based on the central dogma of Biology, genetic codons in conjunction with tRNA play a key role in translating the RNA bases forming sequence of amino acids leading to a synthesized protein. This is the most critical step in synthesizing the right protein needed for personalized medicine and curing genetic diseases. So far, only triplet codons involving three bases of RNA, transcribed from DNA bases, have been used. Since this approach has several inconsistencies and limitations, even the promise of personalized medicine has not been realized. The new Quadruplet genetic coding model proposed and developed here involves all four RNA bases which in conjunction with tRNA will synthesize the right protein. The transcription and translation process used will be the same, but the Quadruplet codons will help overcome most of the inconsistencies and limitations of the triplet codes. Details of this new Quadruplet genetic coding model and its subsequent potential applications including relevance to the origin of life will be presented.

Context trees for privacy-preserving modeling of genetic data

NARCIS (Netherlands)

Kusters, C.J.; Ignatenko, T.

2016-01-01

In this work, we use context trees for privacypreserving modeling of genetic sequences. The resulting estimated models are applied for functional comparison of genetic sequences in a privacy preserving way. Here we define privacy as uncertainty about the genetic source sequence given its model and

Latent spatial models and sampling design for landscape genetics

Science.gov (United States)

Hanks, Ephraim M.; Hooten, Mevin B.; Knick, Steven T.; Oyler-McCance, Sara J.; Fike, Jennifer A.; Cross, Todd B.; Schwartz, Michael K.

2016-01-01

We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (Centrocercus urophasianus) population genetics in the western United States.

Noise in Genetic Toggle Switch Models

Directory of Open Access Journals (Sweden)

Andrecut M.

2006-06-01

Full Text Available In this paper we study the intrinsic noise effect on the switching behavior of a simple genetic circuit corresponding to the genetic toggle switch model. The numerical results obtained from a noisy mean-field model are compared to those obtained from the stochastic Gillespie simulation of the corresponding system of chemical reactions. Our results show that by using a two step reaction approach for modeling the transcription and translation processes one can make the system to lock in one of the steady states for exponentially long times.

Behavior genetics: Bees as model

International Nuclear Information System (INIS)

Nates Parra, Guiomar

2011-01-01

The honeybee Apis mellifera (Apidae) is a model widely used in behavior because of its elaborate social life requiring coordinate actions among the members of the society. Within a colony, division of labor, the performance of tasks by different individuals, follows genetically determined physiological changes that go along with aging. Modern advances in tools of molecular biology and genomics, as well as the sequentiation of A. mellifera genome, have enabled a better understanding of honeybee behavior, in particular social behavior. Numerous studies show that aspects of worker behavior are genetically determined, including defensive, hygienic, reproductive and foraging behavior. For example, genetic diversity is associated with specialization to collect water, nectar and pollen. Also, control of worker reproduction is associated with genetic differences. In this paper, I review the methods and the main results from the study of the genetic and genomic basis of some behaviors in bees.

Organoid Models of Human and Mouse Ductal Pancreatic Cancer

Science.gov (United States)

Boj, Sylvia F.; Hwang, Chang-Il; Baker, Lindsey A.; Chio, Iok In Christine; Engle, Dannielle D.; Corbo, Vincenzo; Jager, Myrthe; Ponz-Sarvise, Mariano; Tiriac, Hervé; Spector, Mona S.; Gracanin, Ana; Oni, Tobiloba; Yu, Kenneth H.; van Boxtel, Ruben; Huch, Meritxell; Rivera, Keith D.; Wilson, John P.; Feigin, Michael E.; Öhlund, Daniel; Handly-Santana, Abram; Ardito-Abraham, Christine M.; Ludwig, Michael; Elyada, Ela; Alagesan, Brinda; Biffi, Giulia; Yordanov, Georgi N.; Delcuze, Bethany; Creighton, Brianna; Wright, Kevin; Park, Youngkyu; Morsink, Folkert H.M.; Molenaar, I. Quintus; Borel Rinkes, Inne H.; Cuppen, Edwin; Hao, Yuan; Jin, Ying; Nijman, Isaac J.; Iacobuzio-Donahue, Christine; Leach, Steven D.; Pappin, Darryl J.; Hammell, Molly; Klimstra, David S.; Basturk, Olca; Hruban, Ralph H.; Offerhaus, George Johan; Vries, Robert G.J.; Clevers, Hans; Tuveson, David A.

2015-01-01

SUMMARY Pancreatic cancer is one of the most lethal malignancies due to its late diagnosis and limited response to treatment. Tractable methods to identify and interrogate pathways involved in pancreatic tumorigenesis are urgently needed. We established organoid models from normal and neoplastic murine and human pancreas tissues. Pancreatic organoids can be rapidly generated from resected tumors and biopsies, survive cryopreservation and exhibit ductal- and disease stage-specific characteristics. Orthotopically transplanted neoplastic organoids recapitulate the full spectrum of tumor development by forming early-grade neoplasms that progress to locally invasive and metastatic carcinomas. Due to their ability to be genetically manipulated, organoids are a platform to probe genetic cooperation. Comprehensive transcriptional and proteomic analyses of murine pancreatic organoids revealed genes and pathways altered during disease progression. The confirmation of many of these protein changes in human tissues demonstrates that organoids are a facile model system to discover characteristics of this deadly malignancy. PMID:25557080

Testing the Structure of Hydrological Models using Genetic Programming

Science.gov (United States)

Selle, B.; Muttil, N.

2009-04-01

Genetic Programming is able to systematically explore many alternative model structures of different complexity from available input and response data. We hypothesised that genetic programming can be used to test the structure hydrological models and to identify dominant processes in hydrological systems. To test this, genetic programming was used to analyse a data set from a lysimeter experiment in southeastern Australia. The lysimeter experiment was conducted to quantify the deep percolation response under surface irrigated pasture to different soil types, water table depths and water ponding times during surface irrigation. Using genetic programming, a simple model of deep percolation was consistently evolved in multiple model runs. This simple and interpretable model confirmed the dominant process contributing to deep percolation represented in a conceptual model that was published earlier. Thus, this study shows that genetic programming can be used to evaluate the structure of hydrological models and to gain insight about the dominant processes in hydrological systems.

Novel polyglutamine model uncouples proteotoxicity from aging.

Science.gov (United States)

Christie, Nakeirah T M; Lee, Amy L; Fay, Hannah G; Gray, Amelia A; Kikis, Elise A

2014-01-01

Polyglutamine expansions in certain proteins are the genetic determinants for nine distinct progressive neurodegenerative disorders and resultant age-related dementia. In these cases, neurodegeneration is due to the aggregation propensity and resultant toxic properties of the polyglutamine-containing proteins. We are interested in elucidating the underlying mechanisms of toxicity of the protein ataxin-3, in which a polyglutamine expansion is the genetic determinant for Machado-Joseph Disease (MJD), also referred to as spinocerebellar ataxia 3 (SCA3). To this end, we have developed a novel model for ataxin-3 protein aggregation, by expressing a disease-related polyglutamine-containing fragment of ataxin-3 in the genetically tractable body wall muscle cells of the model system C. elegans. Here, we demonstrate that this ataxin-3 fragment aggregates in a polyQ length-dependent manner in C. elegans muscle cells and that this aggregation is associated with cellular dysfunction. However, surprisingly, this aggregation and resultant toxicity was not influenced by aging. This is in contrast to polyglutamine peptides alone whose aggregation/toxicity is highly dependent on age. Thus, the data presented here not only describe a new polyglutamine model, but also suggest that protein context likely influences the cellular interactions of the polyglutamine-containing protein and thereby modulates its toxic properties.

Novel polyglutamine model uncouples proteotoxicity from aging.

Directory of Open Access Journals (Sweden)

Nakeirah T M Christie

Full Text Available Polyglutamine expansions in certain proteins are the genetic determinants for nine distinct progressive neurodegenerative disorders and resultant age-related dementia. In these cases, neurodegeneration is due to the aggregation propensity and resultant toxic properties of the polyglutamine-containing proteins. We are interested in elucidating the underlying mechanisms of toxicity of the protein ataxin-3, in which a polyglutamine expansion is the genetic determinant for Machado-Joseph Disease (MJD, also referred to as spinocerebellar ataxia 3 (SCA3. To this end, we have developed a novel model for ataxin-3 protein aggregation, by expressing a disease-related polyglutamine-containing fragment of ataxin-3 in the genetically tractable body wall muscle cells of the model system C. elegans. Here, we demonstrate that this ataxin-3 fragment aggregates in a polyQ length-dependent manner in C. elegans muscle cells and that this aggregation is associated with cellular dysfunction. However, surprisingly, this aggregation and resultant toxicity was not influenced by aging. This is in contrast to polyglutamine peptides alone whose aggregation/toxicity is highly dependent on age. Thus, the data presented here not only describe a new polyglutamine model, but also suggest that protein context likely influences the cellular interactions of the polyglutamine-containing protein and thereby modulates its toxic properties.

Introduction to genetic algorithms as a modeling tool

International Nuclear Information System (INIS)

Wildberger, A.M.; Hickok, K.A.

1990-01-01

Genetic algorithms are search and classification techniques modeled on natural adaptive systems. This is an introduction to their use as a modeling tool with emphasis on prospects for their application in the power industry. It is intended to provide enough background information for its audience to begin to follow technical developments in genetic algorithms and to recognize those which might impact on electric power engineering. Beginning with a discussion of genetic algorithms and their origin as a model of biological adaptation, their advantages and disadvantages are described in comparison with other modeling tools such as simulation and neural networks in order to provide guidance in selecting appropriate applications. In particular, their use is described for improving expert systems from actual data and they are suggested as an aid in building mathematical models. Using the Thermal Performance Advisor as an example, it is suggested how genetic algorithms might be used to make a conventional expert system and mathematical model of a power plant adapt automatically to changes in the plant's characteristics

Comparing estimates of genetic variance across different relationship models.

Science.gov (United States)

Legarra, Andres

2016-02-01

Use of relationships between individuals to estimate genetic variances and heritabilities via mixed models is standard practice in human, plant and livestock genetics. Different models or information for relationships may give different estimates of genetic variances. However, comparing these estimates across different relationship models is not straightforward as the implied base populations differ between relationship models. In this work, I present a method to compare estimates of variance components across different relationship models. I suggest referring genetic variances obtained using different relationship models to the same reference population, usually a set of individuals in the population. Expected genetic variance of this population is the estimated variance component from the mixed model times a statistic, Dk, which is the average self-relationship minus the average (self- and across-) relationship. For most typical models of relationships, Dk is close to 1. However, this is not true for very deep pedigrees, for identity-by-state relationships, or for non-parametric kernels, which tend to overestimate the genetic variance and the heritability. Using mice data, I show that heritabilities from identity-by-state and kernel-based relationships are overestimated. Weighting these estimates by Dk scales them to a base comparable to genomic or pedigree relationships, avoiding wrong comparisons, for instance, "missing heritabilities". Copyright © 2015 Elsevier Inc. All rights reserved.

Testing the structure of a hydrological model using Genetic Programming

Science.gov (United States)

Selle, Benny; Muttil, Nitin

2011-01-01

SummaryGenetic Programming is able to systematically explore many alternative model structures of different complexity from available input and response data. We hypothesised that Genetic Programming can be used to test the structure of hydrological models and to identify dominant processes in hydrological systems. To test this, Genetic Programming was used to analyse a data set from a lysimeter experiment in southeastern Australia. The lysimeter experiment was conducted to quantify the deep percolation response under surface irrigated pasture to different soil types, watertable depths and water ponding times during surface irrigation. Using Genetic Programming, a simple model of deep percolation was recurrently evolved in multiple Genetic Programming runs. This simple and interpretable model supported the dominant process contributing to deep percolation represented in a conceptual model that was published earlier. Thus, this study shows that Genetic Programming can be used to evaluate the structure of hydrological models and to gain insight about the dominant processes in hydrological systems.

Analysis of genetic effects of nuclear-cytoplasmic interaction on quantitative traits: genetic model for diploid plants.

Science.gov (United States)

Han, Lide; Yang, Jian; Zhu, Jun

2007-06-01

A genetic model was proposed for simultaneously analyzing genetic effects of nuclear, cytoplasm, and nuclear-cytoplasmic interaction (NCI) as well as their genotype by environment (GE) interaction for quantitative traits of diploid plants. In the model, the NCI effects were further partitioned into additive and dominance nuclear-cytoplasmic interaction components. Mixed linear model approaches were used for statistical analysis. On the basis of diallel cross designs, Monte Carlo simulations showed that the genetic model was robust for estimating variance components under several situations without specific effects. Random genetic effects were predicted by an adjusted unbiased prediction (AUP) method. Data on four quantitative traits (boll number, lint percentage, fiber length, and micronaire) in Upland cotton (Gossypium hirsutum L.) were analyzed as a worked example to show the effectiveness of the model.

Short communication: Genetic lag represents commercial herd genetic merit more accurately than the 4-path selection model.

Science.gov (United States)

Dechow, C D; Rogers, G W

2018-05-01

Expectation of genetic merit in commercial dairy herds is routinely estimated using a 4-path genetic selection model that was derived for a closed population, but commercial herds using artificial insemination sires are not closed. The 4-path model also predicts a higher rate of genetic progress in elite herds that provide artificial insemination sires than in commercial herds that use such sires, which counters other theoretical assumptions and observations of realized genetic responses. The aim of this work is to clarify whether genetic merit in commercial herds is more accurately reflected under the assumptions of the 4-path genetic response formula or by a genetic lag formula. We demonstrate by tracing the transmission of genetic merit from parents to offspring that the rate of genetic progress in commercial dairy farms is expected to be the same as that in the genetic nucleus. The lag in genetic merit between the nucleus and commercial farms is a function of sire and dam generation interval, the rate of genetic progress in elite artificial insemination herds, and genetic merit of sires and dams. To predict how strategies such as the use of young versus daughter-proven sires, culling heifers following genomic testing, or selective use of sexed semen will alter genetic merit in commercial herds, genetic merit expectations for commercial herds should be modeled using genetic lag expectations. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Genetic and non-genetic animal models for autism spectrum disorders (ASD).

Science.gov (United States)

Ergaz, Zivanit; Weinstein-Fudim, Liza; Ornoy, Asher

2016-09-01

Autism spectrum disorder (ASD) is associated, in addition to complex genetic factors, with a variety of prenatal, perinatal and postnatal etiologies. We discuss the known animal models, mostly in mice and rats, of ASD that helps us to understand the etiology, pathogenesis and treatment of human ASD. We describe only models where behavioral testing has shown autistic like behaviors. Some genetic models mimic known human syndromes like fragile X where ASD is part of the clinical picture, and others are without defined human syndromes. Among the environmentally induced ASD models in rodents, the most common model is the one induced by valproic acid (VPA) either prenatally or early postnatally. VPA induces autism-like behaviors following single exposure during different phases of brain development, implying that the mechanism of action is via a general biological mechanism like epigenetic changes. Maternal infection and inflammation are also associated with ASD in man and animal models. Copyright © 2016 Elsevier Inc. All rights reserved.

Cellular Clocks : Coupled Circadian Dispatch and Cell Division Cycles

NARCIS (Netherlands)

Merrow, Martha; Roenneberg, Till

2004-01-01

Gating of cell division by the circadian clock is well known, yet its mechanism is little understood. Genetically tractable model systems have led to new hypotheses and questions concerning the coupling of these two cellular cycles.

Developing robotic behavior using a genetic programming model

International Nuclear Information System (INIS)

Pryor, R.J.

1998-01-01

This report describes the methodology for using a genetic programming model to develop tracking behaviors for autonomous, microscale robotic vehicles. The use of such vehicles for surveillance and detection operations has become increasingly important in defense and humanitarian applications. Through an evolutionary process similar to that found in nature, the genetic programming model generates a computer program that when downloaded onto a robotic vehicle's on-board computer will guide the robot to successfully accomplish its task. Simulations of multiple robots engaged in problem-solving tasks have demonstrated cooperative behaviors. This report also discusses the behavior model produced by genetic programming and presents some results achieved during the study

Can genetics help psychometrics? Improving dimensionality assessment through genetic factor modeling.

Science.gov (United States)

Franić, Sanja; Dolan, Conor V; Borsboom, Denny; Hudziak, James J; van Beijsterveldt, Catherina E M; Boomsma, Dorret I

2013-09-01

In the present article, we discuss the role that quantitative genetic methodology may play in assessing and understanding the dimensionality of psychological (psychometric) instruments. Specifically, we study the relationship between the observed covariance structures, on the one hand, and the underlying genetic and environmental influences giving rise to such structures, on the other. We note that this relationship may be such that it hampers obtaining a clear estimate of dimensionality using standard tools for dimensionality assessment alone. One situation in which dimensionality assessment may be impeded is that in which genetic and environmental influences, of which the observed covariance structure is a function, differ from each other in structure and dimensionality. We demonstrate that in such situations settling dimensionality issues may be problematic, and propose using quantitative genetic modeling to uncover the (possibly different) dimensionalities of the underlying genetic and environmental structures. We illustrate using simulations and an empirical example on childhood internalizing problems.

CMCpy: Genetic Code-Message Coevolution Models in Python

Science.gov (United States)

Becich, Peter J.; Stark, Brian P.; Bhat, Harish S.; Ardell, David H.

2013-01-01

Code-message coevolution (CMC) models represent coevolution of a genetic code and a population of protein-coding genes (“messages”). Formally, CMC models are sets of quasispecies coupled together for fitness through a shared genetic code. Although CMC models display plausible explanations for the origin of multiple genetic code traits by natural selection, useful modern implementations of CMC models are not currently available. To meet this need we present CMCpy, an object-oriented Python API and command-line executable front-end that can reproduce all published results of CMC models. CMCpy implements multiple solvers for leading eigenpairs of quasispecies models. We also present novel analytical results that extend and generalize applications of perturbation theory to quasispecies models and pioneer the application of a homotopy method for quasispecies with non-unique maximally fit genotypes. Our results therefore facilitate the computational and analytical study of a variety of evolutionary systems. CMCpy is free open-source software available from http://pypi.python.org/pypi/CMCpy/. PMID:23532367

Evolutionary genetics: the Drosophila model

Indian Academy of Sciences (India)

Unknown

Evolutionary genetics straddles the two fundamental processes of life, ... of the genus Drosophila have been used extensively as model systems in experimental ... issue will prove interesting, informative and thought-provoking for both estab-.

Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

DEFF Research Database (Denmark)

Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo

2014-01-01

A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented co...... applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed......A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented...... concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant...

Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

DEFF Research Database (Denmark)

Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo

2013-01-01

A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented co...... applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed......A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented...... concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant...

[The emphases and basic procedures of genetic counseling in psychotherapeutic model].

Science.gov (United States)

Zhang, Yuan-Zhi; Zhong, Nanbert

2006-11-01

The emphases and basic procedures of genetic counseling are all different with those in old models. In the psychotherapeutic model, genetic counseling will not only focus on counselees' genetic disorders and birth defects, but also their psychological problems. "Client-centered therapy" termed by Carl Rogers plays an important role in genetic counseling process. The basic procedures of psychotherapeutic model of genetic counseling include 7 steps: initial contact, introduction, agendas, inquiry of family history, presenting information, closing the session and follow-up.

Unified tractable model for downlink MIMO cellular networks using stochastic geometry

KAUST Repository

Afify, Laila H.

2016-07-26

Several research efforts are invested to develop stochastic geometry models for cellular networks with multiple antenna transmission and reception (MIMO). On one hand, there are models that target abstract outage probability and ergodic rate for simplicity. On the other hand, there are models that sacrifice simplicity to target more tangible performance metrics such as the error probability. Both types of models are completely disjoint in terms of the analytic steps to obtain the performance measures, which makes it challenging to conduct studies that account for different performance metrics. This paper unifies both techniques and proposes a unified stochastic-geometry based mathematical paradigm to account for error probability, outage probability, and ergodic rates in MIMO cellular networks. The proposed model is also unified in terms of the antenna configurations and leads to simpler error probability analysis compared to existing state-of-the-art models. The core part of the analysis is based on abstracting unnecessary information conveyed within the interfering signals by assuming Gaussian signaling. To this end, the accuracy of the proposed framework is verified against state-of-the-art models as well as system level simulations. We provide via this unified study insights on network design by reflecting system parameters effect on different performance metrics. © 2016 IEEE.

An approximate multitrait model for genetic evaluation in dairy cattle with a robust estimation of genetic trends (Open Access publication

Directory of Open Access Journals (Sweden)

Madsen Per

2007-07-01

Full Text Available Abstract In a stochastic simulation study of a dairy cattle population three multitrait models for estimation of genetic parameters and prediction of breeding values were compared. The first model was an approximate multitrait model using a two-step procedure. The first step was a single trait model for all traits. The solutions for fixed effects from these analyses were subtracted from the phenotypes. A multitrait model only containing an overall mean, an additive genetic and a residual term was applied on these preadjusted data. The second model was similar to the first model, but the multitrait model also contained a year effect. The third model was a full multitrait model. Genetic trends for total merit and for the individual traits in the breeding goal were compared for the three scenarios to rank the models. The full multitrait model gave the highest genetic response, but was not significantly better than the approximate multitrait model including a year effect. The inclusion of a year effect into the second step of the approximate multitrait model significantly improved the genetic trend for total merit. In this study, estimation of genetic parameters for breeding value estimation using models corresponding to the ones used for prediction of breeding values increased the accuracy on the breeding values and thereby the genetic progress.

Population genetics of Setaria viridis, a new model system.

Science.gov (United States)

Huang, Pu; Feldman, Maximilian; Schroder, Stephan; Bahri, Bochra A; Diao, Xianmin; Zhi, Hui; Estep, Matt; Baxter, Ivan; Devos, Katrien M; Kellogg, Elizabeth A

2014-10-01

An extensive survey of the standing genetic variation in natural populations is among the priority steps in developing a species into a model system. In recent years, green foxtail (Setaria viridis), along with its domesticated form foxtail millet (S. italica), has rapidly become a promising new model system for C4 grasses and bioenergy crops, due to its rapid life cycle, large amount of seed production and small diploid genome, among other characters. However, remarkably little is known about the genetic diversity in natural populations of this species. In this study, we survey the genetic diversity of a worldwide sample of more than 200 S. viridis accessions, using the genotyping-by-sequencing technique. Two distinct genetic groups in S. viridis and a third group resembling S. italica were identified, with considerable admixture among the three groups. We find the genetic variation of North American S. viridis correlates with both geography and climate and is representative of the total genetic diversity in this species. This pattern may reflect several introduction/dispersal events of S. viridis into North America. We also modelled demographic history and show signal of recent population decline in one subgroup. Finally, we show linkage disequilibrium decay is rapid (<45 kb) in our total sample and slow in genetic subgroups. These results together provide an in-depth understanding of the pattern of genetic diversity of this new model species on a broad geographic scale. They also provide key guidelines for on-going and future work including germplasm preservation, local adaptation, crossing designs and genomewide association studies. © 2014 John Wiley & Sons Ltd.

Shaping asteroid models using genetic evolution (SAGE)

Science.gov (United States)

Bartczak, P.; Dudziński, G.

2018-02-01

In this work, we present SAGE (shaping asteroid models using genetic evolution), an asteroid modelling algorithm based solely on photometric lightcurve data. It produces non-convex shapes, orientations of the rotation axes and rotational periods of asteroids. The main concept behind a genetic evolution algorithm is to produce random populations of shapes and spin-axis orientations by mutating a seed shape and iterating the process until it converges to a stable global minimum. We tested SAGE on five artificial shapes. We also modelled asteroids 433 Eros and 9 Metis, since ground truth observations for them exist, allowing us to validate the models. We compared the derived shape of Eros with the NEAR Shoemaker model and that of Metis with adaptive optics and stellar occultation observations since other models from various inversion methods were available for Metis.

Numeral eddy current sensor modelling based on genetic neural network

International Nuclear Information System (INIS)

Yu Along

2008-01-01

This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method

Efficient simulation and likelihood methods for non-neutral multi-allele models.

Science.gov (United States)

Joyce, Paul; Genz, Alan; Buzbas, Erkan Ozge

2012-06-01

Throughout the 1980s, Simon Tavaré made numerous significant contributions to population genetics theory. As genetic data, in particular DNA sequence, became more readily available, a need to connect population-genetic models to data became the central issue. The seminal work of Griffiths and Tavaré (1994a , 1994b , 1994c) was among the first to develop a likelihood method to estimate the population-genetic parameters using full DNA sequences. Now, we are in the genomics era where methods need to scale-up to handle massive data sets, and Tavaré has led the way to new approaches. However, performing statistical inference under non-neutral models has proved elusive. In tribute to Simon Tavaré, we present an article in spirit of his work that provides a computationally tractable method for simulating and analyzing data under a class of non-neutral population-genetic models. Computational methods for approximating likelihood functions and generating samples under a class of allele-frequency based non-neutral parent-independent mutation models were proposed by Donnelly, Nordborg, and Joyce (DNJ) (Donnelly et al., 2001). DNJ (2001) simulated samples of allele frequencies from non-neutral models using neutral models as auxiliary distribution in a rejection algorithm. However, patterns of allele frequencies produced by neutral models are dissimilar to patterns of allele frequencies produced by non-neutral models, making the rejection method inefficient. For example, in some cases the methods in DNJ (2001) require 10(9) rejections before a sample from the non-neutral model is accepted. Our method simulates samples directly from the distribution of non-neutral models, making simulation methods a practical tool to study the behavior of the likelihood and to perform inference on the strength of selection.

Genetic demographic networks: Mathematical model and applications.

Science.gov (United States)

Kimmel, Marek; Wojdyła, Tomasz

2016-10-01

Recent improvement in the quality of genetic data obtained from extinct human populations and their ancestors encourages searching for answers to basic questions regarding human population history. The most common and successful are model-based approaches, in which genetic data are compared to the data obtained from the assumed demography model. Using such approach, it is possible to either validate or adjust assumed demography. Model fit to data can be obtained based on reverse-time coalescent simulations or forward-time simulations. In this paper we introduce a computational method based on mathematical equation that allows obtaining joint distributions of pairs of individuals under a specified demography model, each of them characterized by a genetic variant at a chosen locus. The two individuals are randomly sampled from either the same or two different populations. The model assumes three types of demographic events (split, merge and migration). Populations evolve according to the time-continuous Moran model with drift and Markov-process mutation. This latter process is described by the Lyapunov-type equation introduced by O'Brien and generalized in our previous works. Application of this equation constitutes an original contribution. In the result section of the paper we present sample applications of our model to both simulated and literature-based demographies. Among other we include a study of the Slavs-Balts-Finns genetic relationship, in which we model split and migrations between the Balts and Slavs. We also include another example that involves the migration rates between farmers and hunters-gatherers, based on modern and ancient DNA samples. This latter process was previously studied using coalescent simulations. Our results are in general agreement with the previous method, which provides validation of our approach. Although our model is not an alternative to simulation methods in the practical sense, it provides an algorithm to compute pairwise

Hierarchical linear modeling of longitudinal pedigree data for genetic association analysis

DEFF Research Database (Denmark)

Tan, Qihua; B Hjelmborg, Jacob V; Thomassen, Mads

2014-01-01

-effect models to explicitly model the genetic relationship. These have proved to be an efficient way of dealing with sample clustering in pedigree data. Although current algorithms implemented in popular statistical packages are useful for adjusting relatedness in the mixed modeling of genetic effects...... associated with blood pressure with estimated inflation factors of 0.99, suggesting that our modeling of random effects efficiently handles the genetic relatedness in pedigrees. Application to simulated data captures important variants specified in the simulation. Our results show that the method is useful......Genetic association analysis on complex phenotypes under a longitudinal design involving pedigrees encounters the problem of correlation within pedigrees, which could affect statistical assessment of the genetic effects. Approaches have been proposed to integrate kinship correlation into the mixed...

Unified tractable model for downlink MIMO cellular networks using stochastic geometry

KAUST Repository

Afify, Laila H.; Elsawy, Hesham; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

2016-01-01

for simplicity. On the other hand, there are models that sacrifice simplicity to target more tangible performance metrics such as the error probability. Both types of models are completely disjoint in terms of the analytic steps to obtain the performance measures

Genetic screens in Caenorhabditis elegans models for neurodegenerative diseases

NARCIS (Netherlands)

Alvarenga Fernandes Sin, Olga; Michels, Helen; Nollen, Ellen A. A.

2014-01-01

Caenorhabditis elegans comprises unique features that make it an attractive model organism in diverse fields of biology. Genetic screens are powerful to identify genes and C. elegans can be customized to forward or reverse genetic screens and to establish gene function. These genetic screens can be

Macrophage–Microbe Interactions: Lessons from the Zebrafish Model

Directory of Open Access Journals (Sweden)

Nagisa Yoshida

2017-12-01

Full Text Available Macrophages provide front line defense against infections. The study of macrophage–microbe interplay is thus crucial for understanding pathogenesis and infection control. Zebrafish (Danio rerio larvae provide a unique platform to study macrophage–microbe interactions in vivo, from the level of the single cell to the whole organism. Studies using zebrafish allow non-invasive, real-time visualization of macrophage recruitment and phagocytosis. Furthermore, the chemical and genetic tractability of zebrafish has been central to decipher the complex role of macrophages during infection. Here, we discuss the latest developments using zebrafish models of bacterial and fungal infection. We also review novel aspects of macrophage biology revealed by zebrafish, which can potentiate development of new therapeutic strategies for humans.

Animal models for human genetic diseases | Sharif | African Journal ...

African Journals Online (AJOL)

The study of human genetic diseases can be greatly aided by animal models because of their similarity to humans in terms of genetics. In addition to understand diverse aspects of basic biology, model organisms are extensively used in applied research in agriculture, industry, and also in medicine, where they are used to ...

Modelling the co-evolution of indirect genetic effects and inherited variability.

Science.gov (United States)

Marjanovic, Jovana; Mulder, Han A; Rönnegård, Lars; Bijma, Piter

2018-03-28

When individuals interact, their phenotypes may be affected not only by their own genes but also by genes in their social partners. This phenomenon is known as Indirect Genetic Effects (IGEs). In aquaculture species and some plants, however, competition not only affects trait levels of individuals, but also inflates variability of trait values among individuals. In the field of quantitative genetics, the variability of trait values has been studied as a quantitative trait in itself, and is often referred to as inherited variability. Such studies, however, consider only the genetic effect of the focal individual on trait variability and do not make a connection to competition. Although the observed phenotypic relationship between competition and variability suggests an underlying genetic relationship, the current quantitative genetic models of IGE and inherited variability do not allow for such a relationship. The lack of quantitative genetic models that connect IGEs to inherited variability limits our understanding of the potential of variability to respond to selection, both in nature and agriculture. Models of trait levels, for example, show that IGEs may considerably change heritable variation in trait values. Currently, we lack the tools to investigate whether this result extends to variability of trait values. Here we present a model that integrates IGEs and inherited variability. In this model, the target phenotype, say growth rate, is a function of the genetic and environmental effects of the focal individual and of the difference in trait value between the social partner and the focal individual, multiplied by a regression coefficient. The regression coefficient is a genetic trait, which is a measure of cooperation; a negative value indicates competition, a positive value cooperation, and an increasing value due to selection indicates the evolution of cooperation. In contrast to the existing quantitative genetic models, our model allows for co-evolution of

ENU mutagenesis to generate genetically modified rat models.

Science.gov (United States)

van Boxtel, Ruben; Gould, Michael N; Cuppen, Edwin; Smits, Bart M G

2010-01-01

The rat is one of the most preferred model organisms in biomedical research and has been extremely useful for linking physiology and pathology to the genome. However, approaches to genetically modify specific genes in the rat germ line remain relatively scarce. To date, the most efficient approach for generating genetically modified rats has been the target-selected N-ethyl-N-nitrosourea (ENU) mutagenesis-based technology. Here, we describe the detailed protocols for ENU mutagenesis and mutant retrieval in the rat model organism.

Chemical and genetic tools to explore S1P biology.

Science.gov (United States)

Cahalan, Stuart M

2014-01-01

The zwitterionic lysophospholipid Sphingosine 1-Phosphate (S1P) is a pleiotropic mediator of physiology and pathology. The synthesis, transport, and degradation of S1P are tightly regulated to ensure that S1P is present in the proper concentrations in the proper location. The binding of S1P to five G protein-coupled S1P receptors regulates many physiological systems, particularly the immune and vascular systems. Our understanding of the functions of S1P has been aided by the tractability of the system to both chemical and genetic manipulation. Chemical modulators have been generated to affect most of the known components of S1P biology, including agonists of S1P receptors and inhibitors of enzymes regulating S1P production and degradation. Genetic knockouts and manipulations have been similarly engineered to disrupt the functions of individual S1P receptors or enzymes involved in S1P metabolism. This chapter will focus on the development and utilization of these chemical and genetic tools to explore the complex biology surrounding S1P and its receptors, with particular attention paid to the in vivo findings that these tools have allowed for.

Teaching Genetic Counseling Skills: Incorporating a Genetic Counseling Adaptation Continuum Model to Address Psychosocial Complexity.

Science.gov (United States)

Shugar, Andrea

2017-04-01

Genetic counselors are trained health care professionals who effectively integrate both psychosocial counseling and information-giving into their practice. Preparing genetic counseling students for clinical practice is a challenging task, particularly when helping them develop effective and active counseling skills. Resistance to incorporating these skills may stem from decreased confidence, fear of causing harm or a lack of clarity of psycho-social goals. The author reflects on the personal challenges experienced in teaching genetic counselling students to work with psychological and social complexity, and proposes a Genetic Counseling Adaptation Continuum model and methodology to guide students in the use of advanced counseling skills.

THE ALLOMETRIC-AUTOREGRESSIVE MODEL IN GENETIC ...

African Journals Online (AJOL)

The application of an allometric-autoregressive model for the quantification of growth and efficiency of feed utilization for purposes of selection for ... be of value in genetic studies. ... mass) gives a fair indication of the cumulative preweaning.

Fitting the CDO correlation skew: a tractable structural jump-diffusion model

DEFF Research Database (Denmark)

Willemann, Søren

2007-01-01

We extend a well-known structural jump-diffusion model for credit risk to handle both correlations through diffusion of asset values and common jumps in asset value. Through a simplifying assumption on the default timing and efficient numerical techniques, we develop a semi-analytic framework...... allowing for instantaneous calibration to heterogeneous CDS curves and fast computation of CDO tranche spreads. We calibrate the model to CDX and iTraxx data from February 2007 and achieve a satisfactory fit. To price the senior tranches for both indices, we require a risk-neutral probability of a market...

Animal models for human genetic diseases

African Journals Online (AJOL)

Sharif Sons

The study of human genetic diseases can be greatly aided by animal models because of their similarity .... and gene targeting in embryonic stem cells) has been a powerful tool in .... endonucleases that are designed to make a doublestrand.

Different Parameters Support Generalization and Discrimination Learning in "Drosophila" at the Flight Simulator

Science.gov (United States)

Brembs, Bjorn; de Ibarra, Natalie Hempel

2006-01-01

We have used a genetically tractable model system, the fruit fly "Drosophila melanogaster" to study the interdependence between sensory processing and associative processing on learning performance. We investigated the influence of variations in the physical and predictive properties of color stimuli in several different operant-conditioning…

Viral persistence, liver disease and host response in Hepatitis C-like virus rat model

DEFF Research Database (Denmark)

Trivedi, Sheetal; Murthy, Satyapramod; Sharma, Himanshu

2018-01-01

The lack of a relevant, tractable, and immunocompetent animal model for hepatitis C virus (HCV) has severely impeded investigations of viral persistence, immunity and pathogenesis. In the absence of immunocompetent models with robust HCV infection, homolog hepaciviruses in their natural host could...... potentially provide useful surrogate models. We isolated a rodent hepacivirus (RHV) from wild rats (Rattus norvegicus), RHV-rn1, acquired the complete viral genome sequence and developed an infectious reverse genetics system. RHV-rn1 resembles HCV in genomic features including the pattern of polyprotein...... cleavage sites and secondary structures in the viral 5' and 3' UTRs. We used site-directed and random mutagenesis to determine that only the first of the two miR-122 seed sites in viral 5'UTR is required for viral replication and persistence in rats. Next, we used the clone derived virus progeny to infect...

Alternate service delivery models in cancer genetic counseling: a mini-review

Directory of Open Access Journals (Sweden)

Adam Hudson Buchanan

2016-05-01

Full Text Available Demand for cancer genetic counseling has grown rapidly in recent years as germline genomic information has become increasingly incorporated into cancer care and the field has entered the public consciousness through high-profile celebrity publications. Increased demand and existing variability in the availability of trained cancer genetics clinicians place a priority on developing and evaluating alternate service delivery models for genetic counseling. This mini-review summarizes the state of science regarding service delivery models such as telephone counseling, telegenetics and group counseling. Research on comparative effectiveness of these models in traditional individual, in-person genetic counseling has been promising for improving access to care in a manner acceptable to patients. Yet, it has not fully evaluated the short- and long-term patient- and system-level outcomes that will help answer the question of whether these models achieve the same beneficial psychosocial and behavioral outcomes as traditional cancer genetic counseling. We propose a research agenda focused on comparative effectiveness of available service delivery models and how to match models to patients and practice settings. Only through this rigorous research can clinicians and systems find the optimal balance of clinical quality, ready and secure access to care, and financial sustainability. Such research will be integral to achieving the promise of genomic medicine in oncology.

Tractable Stochastic Geometry Model for IoT Access in LTE Networks

KAUST Repository

Gharbieh, Mohammad; Elsawy, Hesham; Bader, Ahmed; Alouini, Mohamed-Slim

2017-01-01

The Internet of Things (IoT) is large-scale by nature. This is not only manifested by the large number of connected devices, but also by the high volumes of traffic that must be accommodated. Cellular networks are indeed a natural candidate for the data tsunami the IoT is expected to generate in conjunction with legacy human-type traffic. However, the random access process for scheduling request represents a major bottleneck to support IoT via LTE cellular networks. Accordingly, this paper develops a mathematical framework to model and study the random access channel (RACH) scalability to accommodate IoT traffic. The developed model is based on stochastic geometry and discrete time Markov chains (DTMC) to account for different access strategies and possible sources of inter-cell and intra-cell interferences. To this end, the developed model is utilized to assess and compare three different access strategies, which incorporate a combination of transmission persistency, back-off, and power ramping. The analysis and the results showcased herewith clearly illustrate the vulnerability of the random access procedure as the IoT intensity grows. Finally, the paper offers insights into effective scenarios for each transmission strategy in terms of IoT intensity and RACH detection thresholds.

Tractable Stochastic Geometry Model for IoT Access in LTE Networks

KAUST Repository

Gharbieh, Mohammad

2017-02-07

The Internet of Things (IoT) is large-scale by nature. This is not only manifested by the large number of connected devices, but also by the high volumes of traffic that must be accommodated. Cellular networks are indeed a natural candidate for the data tsunami the IoT is expected to generate in conjunction with legacy human-type traffic. However, the random access process for scheduling request represents a major bottleneck to support IoT via LTE cellular networks. Accordingly, this paper develops a mathematical framework to model and study the random access channel (RACH) scalability to accommodate IoT traffic. The developed model is based on stochastic geometry and discrete time Markov chains (DTMC) to account for different access strategies and possible sources of inter-cell and intra-cell interferences. To this end, the developed model is utilized to assess and compare three different access strategies, which incorporate a combination of transmission persistency, back-off, and power ramping. The analysis and the results showcased herewith clearly illustrate the vulnerability of the random access procedure as the IoT intensity grows. Finally, the paper offers insights into effective scenarios for each transmission strategy in terms of IoT intensity and RACH detection thresholds.

Estimation and interpretation of genetic effects with epistasis using the NOIA model.

Science.gov (United States)

Alvarez-Castro, José M; Carlborg, Orjan; Rönnegård, Lars

2012-01-01

We introduce this communication with a brief outline of the historical landmarks in genetic modeling, especially concerning epistasis. Then, we present methods for the use of genetic modeling in QTL analyses. In particular, we summarize the essential expressions of the natural and orthogonal interactions (NOIA) model of genetic effects. Our motivation for reviewing that theory here is twofold. First, this review presents a digest of the expressions for the application of the NOIA model, which are often mixed with intermediate and additional formulae in the original articles. Second, we make the required theory handy for the reader to relate the genetic concepts to the particular mathematical expressions underlying them. We illustrate those relations by providing graphical interpretations and a diagram summarizing the key features for applying genetic modeling with epistasis in comprehensive QTL analyses. Finally, we briefly review some examples of the application of NOIA to real data and the way it improves the interpretability of the results.

Genetic engineering in nonhuman primates for human disease modeling.

Science.gov (United States)

Sato, Kenya; Sasaki, Erika

2018-02-01

Nonhuman primate (NHP) experimental models have contributed greatly to human health research by assessing the safety and efficacy of newly developed drugs, due to their physiological and anatomical similarities to humans. To generate NHP disease models, drug-inducible methods, and surgical treatment methods have been employed. Recent developments in genetic and developmental engineering in NHPs offer new options for producing genetically modified disease models. Moreover, in recent years, genome-editing technology has emerged to further promote this trend and the generation of disease model NHPs has entered a new era. In this review, we summarize the generation of conventional disease model NHPs and discuss new solutions to the problem of mosaicism in genome-editing technology.

Modeling genetic imprinting effects of DNA sequences with multilocus polymorphism data

Directory of Open Access Journals (Sweden)

Staud Roland

2009-08-01

Full Text Available Abstract Single nucleotide polymorphisms (SNPs represent the most widespread type of DNA sequence variation in the human genome and they have recently emerged as valuable genetic markers for revealing the genetic architecture of complex traits in terms of nucleotide combination and sequence. Here, we extend an algorithmic model for the haplotype analysis of SNPs to estimate the effects of genetic imprinting expressed at the DNA sequence level. The model provides a general procedure for identifying the number and types of optimal DNA sequence variants that are expressed differently due to their parental origin. The model is used to analyze a genetic data set collected from a pain genetics project. We find that DNA haplotype GAC from three SNPs, OPRKG36T (with two alleles G and T, OPRKA843G (with alleles A and G, and OPRKC846T (with alleles C and T, at the kappa-opioid receptor, triggers a significant effect on pain sensitivity, but with expression significantly depending on the parent from which it is inherited (p = 0.008. With a tremendous advance in SNP identification and automated screening, the model founded on haplotype discovery and statistical inference may provide a useful tool for genetic analysis of any quantitative trait with complex inheritance.

A central role for TOR signalling in a yeast model for juvenile CLN3 disease

Directory of Open Access Journals (Sweden)

Michael E. Bond

2015-11-01

Full Text Available Yeasts provide an excellent genetically tractable eukaryotic system for investigating the function of genes in their biological context, and are especially relevant for those conserved genes that cause disease. We study the role of btn1, the orthologue of a human gene that underlies an early onset neurodegenerative disease (juvenile CLN3 disease, neuronal ceroid lipofuscinosis (NCLs or Batten disease in the fission yeast Schizosaccharomyces pombe. A global screen for genetic interactions with btn1 highlighted a conserved key signalling hub in which multiple components functionally relate to this conserved disease gene. This signalling hub includes two major mitogen-activated protein kinase (MAPK cascades, and centers on the Tor kinase complexes TORC1 and TORC2. We confirmed that yeast cells modelling CLN3 disease exhibit features consistent with dysfunction in the TORC pathways, and showed that modulating TORC function leads to a comprehensive rescue of defects in this yeast disease model. The same pathways may be novel targets in the development of therapies for the NCLs and related diseases.

A simple algorithm to estimate genetic variance in an animal threshold model using Bayesian inference Genetics Selection Evolution 2010, 42:29

DEFF Research Database (Denmark)

Ødegård, Jørgen; Meuwissen, Theo HE; Heringstad, Bjørg

2010-01-01

Background In the genetic analysis of binary traits with one observation per animal, animal threshold models frequently give biased heritability estimates. In some cases, this problem can be circumvented by fitting sire- or sire-dam models. However, these models are not appropriate in cases where...... records exist for the parents). Furthermore, the new algorithm showed much faster Markov chain mixing properties for genetic parameters (similar to the sire-dam model). Conclusions The new algorithm to estimate genetic parameters via Gibbs sampling solves the bias problems typically occurring in animal...... individual records exist on parents. Therefore, the aim of our study was to develop a new Gibbs sampling algorithm for a proper estimation of genetic (co)variance components within an animal threshold model framework. Methods In the proposed algorithm, individuals are classified as either "informative...

Potential uses of genetic geological modelling to identify new uranium provinces

International Nuclear Information System (INIS)

Finch, W.I.

1982-01-01

Genetic-geological modelling is the placing of the various processes of the development of a uranium province into distinct stages that are ordered chronologically and made part of a matrix with corresponding geologic evidence. The models can be applied to a given region by using one of several methods to determine a numerical favorability rating. Two of the possible methods, geologic decision analysis and an oil-and-gas type of play analysis, are briefly described. Simplified genetic models are given for environments of the quartz-pebble conglomerate, unconformity-related vein, and sandstone types of deposits. Comparison of the genetic models of these three sedimentary-related environments reveals several common attributes that may define a general uranium province environment

Portfolio optimization by using linear programing models based on genetic algorithm

Science.gov (United States)

Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.

2018-01-01

In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.

Two-level mixed modeling of longitudinal pedigree data for genetic association analysis

DEFF Research Database (Denmark)

Tan, Q.

2013-01-01

of follow-up. Approaches have been proposed to integrate kinship correlation into the mixed effect models to explicitly model the genetic relationship which have been proven as an efficient way for dealing with sample clustering in pedigree data. Although useful for adjusting relatedness in the mixed...... assess the genetic associations with the mean level and the rate of change in a phenotype both with kinship correlation integrated in the mixed effect models. We apply our method to longitudinal pedigree data to estimate the genetic effects on systolic blood pressure measured over time in large pedigrees......Genetic association analysis on complex phenotypes under a longitudinal design involving pedigrees encounters the problem of correlation within pedigrees which could affect statistical assessment of the genetic effects on both the mean level of the phenotype and its rate of change over the time...

Model comparisons and genetic and environmental parameter ...

African Journals Online (AJOL)

arc

Model comparisons and genetic and environmental parameter estimates of growth and the ... breeding strategies and for accurate breeding value estimation. The objectives ...... Sci. 23, 72-76. Van Wyk, J.B., Fair, M.D. & Cloete, S.W.P., 2003.

Long-Term Adult Feline Liver Organoid Cultures for Disease Modeling of Hepatic Steatosis.

Science.gov (United States)

Kruitwagen, Hedwig S; Oosterhoff, Loes A; Vernooij, Ingrid G W H; Schrall, Ingrid M; van Wolferen, Monique E; Bannink, Farah; Roesch, Camille; van Uden, Lisa; Molenaar, Martijn R; Helms, J Bernd; Grinwis, Guy C M; Verstegen, Monique M A; van der Laan, Luc J W; Huch, Meritxell; Geijsen, Niels; Vries, Robert G; Clevers, Hans; Rothuizen, Jan; Schotanus, Baukje A; Penning, Louis C; Spee, Bart

2017-04-11

Hepatic steatosis is a highly prevalent liver disease, yet research is hampered by the lack of tractable cellular and animal models. Steatosis also occurs in cats, where it can cause severe hepatic failure. Previous studies demonstrate the potential of liver organoids for modeling genetic diseases. To examine the possibility of using organoids to model steatosis, we established a long-term feline liver organoid culture with adult liver stem cell characteristics and differentiation potential toward hepatocyte-like cells. Next, organoids from mouse, human, dog, and cat liver were provided with fatty acids. Lipid accumulation was observed in all organoids and interestingly, feline liver organoids accumulated more lipid droplets than human organoids. Finally, we demonstrate effects of interference with β-oxidation on lipid accumulation in feline liver organoids. In conclusion, feline liver organoids can be successfully cultured and display a predisposition for lipid accumulation, making them an interesting model in hepatic steatosis research. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Simulating pattern-process relationships to validate landscape genetic models

Science.gov (United States)

A. J. Shirk; S. A. Cushman; E. L. Landguth

2012-01-01

Landscapes may resist gene flow and thereby give rise to a pattern of genetic isolation within a population. The mechanism by which a landscape resists gene flow can be inferred by evaluating the relationship between landscape models and an observed pattern of genetic isolation. This approach risks false inferences because researchers can never feasibly test all...

Construction of a Recyclable Genetic Marker and Serial Gene Deletions in the Human Pathogenic Mucorales Mucor circinelloides.

Science.gov (United States)

Garcia, Alexis; Adedoyin, Gloria; Heitman, Joseph; Lee, Soo Chan

2017-07-05

Mucor circinelloides is a human pathogen, biofuel producer, and model system that belongs to a basal fungal lineage; however, the genetics of this fungus are limited. In contrast to ascomycetes and basidiomycetes, basal fungal lineages have been understudied. This may be caused by a lack of attention given to these fungi, as well as limited tools for genetic analysis. Nonetheless, the importance of these fungi as pathogens and model systems has increased. M. circinelloides is one of a few genetically tractable organisms in the basal fungi, but it is far from a robust genetic system when compared to model fungi in the subkingdom Dikarya. One problem is the organism is resistant to drugs utilized to select for dominant markers in other fungal transformation systems. Thus, we developed a blaster recyclable marker system by using the pyrG gene (encoding an orotidine-5'-phosphate decarboxylase, ortholog of URA3 in Saccharomyces cerevisiae ). A 237-bp fragment downstream of the pyrG gene was tandemly incorporated into the upstream region of the gene, resulting in construction of a pyrG-dpl237 marker. To test the functionality of the pyrG-dpl237 marker, we disrupted the carRP gene that is involved in carotenoid synthesis in pyrG - mutant background. The resulting carRP :: pyrG-dpl237 mutants exhibit a white colony phenotype due to lack of carotene, whereas wild type displays yellowish colonies. The pyrG marker was then successfully excised, generating carRP-dpl237 on 5-FOA medium. The mutants became auxotrophic and required uridine for growth. We then disrupted the calcineurin B regulatory subunit cnbR gene in the carRP :: dpl237 strain, generating mutants with the alleles carRP :: dpl237 and cnbR :: pyrG These results demonstrate that the recyclable marker system is fully functional, and therefore the pyrG-dpl237 marker can be used for sequential gene deletions in M. circinelloides . Copyright © 2017 Garcia et al.

Construction of a Recyclable Genetic Marker and Serial Gene Deletions in the Human Pathogenic Mucorales Mucor circinelloides

Directory of Open Access Journals (Sweden)

Alexis Garcia

2017-07-01

Full Text Available Mucor circinelloides is a human pathogen, biofuel producer, and model system that belongs to a basal fungal lineage; however, the genetics of this fungus are limited. In contrast to ascomycetes and basidiomycetes, basal fungal lineages have been understudied. This may be caused by a lack of attention given to these fungi, as well as limited tools for genetic analysis. Nonetheless, the importance of these fungi as pathogens and model systems has increased. M. circinelloides is one of a few genetically tractable organisms in the basal fungi, but it is far from a robust genetic system when compared to model fungi in the subkingdom Dikarya. One problem is the organism is resistant to drugs utilized to select for dominant markers in other fungal transformation systems. Thus, we developed a blaster recyclable marker system by using the pyrG gene (encoding an orotidine-5′-phosphate decarboxylase, ortholog of URA3 in Saccharomyces cerevisiae. A 237-bp fragment downstream of the pyrG gene was tandemly incorporated into the upstream region of the gene, resulting in construction of a pyrG-dpl237 marker. To test the functionality of the pyrG-dpl237 marker, we disrupted the carRP gene that is involved in carotenoid synthesis in pyrG− mutant background. The resulting carRP::pyrG-dpl237 mutants exhibit a white colony phenotype due to lack of carotene, whereas wild type displays yellowish colonies. The pyrG marker was then successfully excised, generating carRP-dpl237 on 5-FOA medium. The mutants became auxotrophic and required uridine for growth. We then disrupted the calcineurin B regulatory subunit cnbR gene in the carRP::dpl237 strain, generating mutants with the alleles carRP::dpl237 and cnbR::pyrG. These results demonstrate that the recyclable marker system is fully functional, and therefore the pyrG-dpl237 marker can be used for sequential gene deletions in M. circinelloides.

Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops.

Science.gov (United States)

Yabe, Shiori; Yamasaki, Masanori; Ebana, Kaworu; Hayashi, Takeshi; Iwata, Hiroyoshi

2016-01-01

Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS), which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an "island model" inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the potential of genomic

Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops.

Directory of Open Access Journals (Sweden)

Shiori Yabe

Full Text Available Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS, which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an "island model" inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the

Model predictive control classical, robust and stochastic

CERN Document Server

Kouvaritakis, Basil

2016-01-01

For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...

PM Synchronous Motor Dynamic Modeling with Genetic Algorithm ...

African Journals Online (AJOL)

Adel

This paper proposes dynamic modeling simulation for ac Surface Permanent Magnet Synchronous ... Simulations are implemented using MATLAB with its genetic algorithm toolbox. .... selection, the process that drives biological evolution.

The genetic analysis of repeated measures I: Simplex models

NARCIS (Netherlands)

Molenaar, P.C.M.; Boomsma, D.I.

1987-01-01

Extends the simplex model to a model that may be used for the genetic and environmental analysis of covariance (ANCOVA) structures. This "double" simplex structure can be specified as a linear structural relationships model. It is shown that data that give rise to a simplex correlation structure,

Gene × Environment Interactions in Schizophrenia: Evidence from Genetic Mouse Models.

Science.gov (United States)

Moran, Paula; Stokes, Jennifer; Marr, Julia; Bock, Gavin; Desbonnet, Lieve; Waddington, John; O'Tuathaigh, Colm

2016-01-01

The study of gene × environment, as well as epistatic interactions in schizophrenia, has provided important insight into the complex etiopathologic basis of schizophrenia. It has also increased our understanding of the role of susceptibility genes in the disorder and is an important consideration as we seek to translate genetic advances into novel antipsychotic treatment targets. This review summarises data arising from research involving the modelling of gene × environment interactions in schizophrenia using preclinical genetic models. Evidence for synergistic effects on the expression of schizophrenia-relevant endophenotypes will be discussed. It is proposed that valid and multifactorial preclinical models are important tools for identifying critical areas, as well as underlying mechanisms, of convergence of genetic and environmental risk factors, and their interaction in schizophrenia.

Gene × Environment Interactions in Schizophrenia: Evidence from Genetic Mouse Models

Science.gov (United States)

Marr, Julia; Bock, Gavin; Desbonnet, Lieve; Waddington, John

2016-01-01

The study of gene × environment, as well as epistatic interactions in schizophrenia, has provided important insight into the complex etiopathologic basis of schizophrenia. It has also increased our understanding of the role of susceptibility genes in the disorder and is an important consideration as we seek to translate genetic advances into novel antipsychotic treatment targets. This review summarises data arising from research involving the modelling of gene × environment interactions in schizophrenia using preclinical genetic models. Evidence for synergistic effects on the expression of schizophrenia-relevant endophenotypes will be discussed. It is proposed that valid and multifactorial preclinical models are important tools for identifying critical areas, as well as underlying mechanisms, of convergence of genetic and environmental risk factors, and their interaction in schizophrenia. PMID:27725886

Genetic demixing and evolution in linear stepping stone models

Science.gov (United States)

Korolev, K. S.; Avlund, Mikkel; Hallatschek, Oskar; Nelson, David R.

2010-04-01

Results for mutation, selection, genetic drift, and migration in a one-dimensional continuous population are reviewed and extended. The population is described by a continuous limit of the stepping stone model, which leads to the stochastic Fisher-Kolmogorov-Petrovsky-Piscounov equation with additional terms describing mutations. Although the stepping stone model was first proposed for population genetics, it is closely related to “voter models” of interest in nonequilibrium statistical mechanics. The stepping stone model can also be regarded as an approximation to the dynamics of a thin layer of actively growing pioneers at the frontier of a colony of micro-organisms undergoing a range expansion on a Petri dish. The population tends to segregate into monoallelic domains. This segregation slows down genetic drift and selection because these two evolutionary forces can only act at the boundaries between the domains; the effects of mutation, however, are not significantly affected by the segregation. Although fixation in the neutral well-mixed (or “zero-dimensional”) model occurs exponentially in time, it occurs only algebraically fast in the one-dimensional model. An unusual sublinear increase is also found in the variance of the spatially averaged allele frequency with time. If selection is weak, selective sweeps occur exponentially fast in both well-mixed and one-dimensional populations, but the time constants are different. The relatively unexplored problem of evolutionary dynamics at the edge of an expanding circular colony is studied as well. Also reviewed are how the observed patterns of genetic diversity can be used for statistical inference and the differences are highlighted between the well-mixed and one-dimensional models. Although the focus is on two alleles or variants, q -allele Potts-like models of gene segregation are considered as well. Most of the analytical results are checked with simulations and could be tested against recent spatial

Neurophysiology of Drosophila models of Parkinson's disease.

Science.gov (United States)

West, Ryan J H; Furmston, Rebecca; Williams, Charles A C; Elliott, Christopher J H

2015-01-01

We provide an insight into the role Drosophila has played in elucidating neurophysiological perturbations associated with Parkinson's disease- (PD-) related genes. Synaptic signalling deficits are observed in motor, central, and sensory systems. Given the neurological impact of disease causing mutations within these same genes in humans the phenotypes observed in fly are of significant interest. As such we observe four unique opportunities provided by fly nervous system models of Parkinson's disease. Firstly, Drosophila models are instrumental in exploring the mechanisms of neurodegeneration, with several PD-related mutations eliciting related phenotypes including sensitivity to energy supply and vesicular deformities. These are leading to the identification of plausible cellular mechanisms, which may be specific to (dopaminergic) neurons and synapses rather than general cellular phenotypes. Secondly, models show noncell autonomous signalling within the nervous system, offering the opportunity to develop our understanding of the way pathogenic signalling propagates, resembling Braak's scheme of spreading pathology in PD. Thirdly, the models link physiological deficits to changes in synaptic structure. While the structure-function relationship is complex, the genetic tractability of Drosophila offers the chance to separate fundamental changes from downstream consequences. Finally, the strong neuronal phenotypes permit relevant first in vivo drug testing.

Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming

Directory of Open Access Journals (Sweden)

Wafa Abdelmalek

2009-01-01

Full Text Available The volatility is a crucial variable in option pricing and hedging strategies. The aim of this paper is to provide some initial evidence of the empirical relevance of genetic programming to volatility's forecasting. By using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes-implied volatility is compared between time series samples and moneyness-time to maturity classes. Total and out-of-sample mean squared errors are used as forecasting's performance measures. Comparisons reveal that the time series model seems to be more accurate in forecasting-implied volatility than moneyness time to maturity models. Overall, results are strongly encouraging and suggest that the genetic programming approach works well in solving financial problems.

The complete genome sequence of Haloferax volcanii DS2, a model archaeon.

Directory of Open Access Journals (Sweden)

Amber L Hartman

2010-03-01

Full Text Available Haloferax volcanii is an easily culturable moderate halophile that grows on simple defined media, is readily transformable, and has a relatively stable genome. This, in combination with its biochemical and genetic tractability, has made Hfx. volcanii a key model organism, not only for the study of halophilicity, but also for archaeal biology in general.We report here the sequencing and analysis of the genome of Hfx. volcanii DS2, the type strain of this species. The genome contains a main 2.848 Mb chromosome, three smaller chromosomes pHV1, 3, 4 (85, 438, 636 kb, respectively and the pHV2 plasmid (6.4 kb.The completed genome sequence, presented here, provides an invaluable tool for further in vivo and in vitro studies of Hfx. volcanii.

Genetic Resources in the “Calabaza Pipiana” Squash (Cucurbita argyrosperma) in Mexico: Genetic Diversity, Genetic Differentiation and Distribution Models

Science.gov (United States)

Sánchez-de la Vega, Guillermo; Castellanos-Morales, Gabriela; Gámez, Niza; Hernández-Rosales, Helena S.; Vázquez-Lobo, Alejandra; Aguirre-Planter, Erika; Jaramillo-Correa, Juan P.; Montes-Hernández, Salvador; Lira-Saade, Rafael; Eguiarte, Luis E.

2018-01-01

Analyses of genetic variation allow understanding the origin, diversification and genetic resources of cultivated plants. Domesticated taxa and their wild relatives are ideal systems for studying genetic processes of plant domestication and their joint is important to evaluate the distribution of their genetic resources. Such is the case of the domesticated subspecies C. argyrosperma ssp. argyrosperma, known in Mexico as calabaza pipiana, and its wild relative C. argyrosperma ssp. sororia. The main aim of this study was to use molecular data (microsatellites) to assess the levels of genetic variation and genetic differentiation within and among populations of domesticated argyrosperma across its distribution in Mexico in comparison to its wild relative, sororia, and to identify environmental suitability in previously proposed centers of domestication. We analyzed nine unlinked nuclear microsatellite loci to assess levels of diversity and distribution of genetic variation within and among populations in 440 individuals from 19 populations of cultivated landraces of argyrosperma and from six wild populations of sororia, in order to conduct a first systematic analysis of their genetic resources. We also used species distribution models (SDMs) for sororia to identify changes in this wild subspecies’ distribution from the Holocene (∼6,000 years ago) to the present, and to assess the presence of suitable environmental conditions in previously proposed domestication sites. Genetic variation was similar among subspecies (HE = 0.428 in sororia, and HE = 0.410 in argyrosperma). Nine argyrosperma populations showed significant levels of inbreeding. Both subspecies are well differentiated, and genetic differentiation (FST) among populations within each subspecies ranged from 0.152 to 0.652. Within argyrosperma we found three genetic groups (Northern Mexico, Yucatan Peninsula, including Michoacan and Veracruz, and Pacific coast plus Durango). We detected low levels of gene

Genetic Resources in the “Calabaza Pipiana” Squash (Cucurbita argyrosperma in Mexico: Genetic Diversity, Genetic Differentiation and Distribution Models

Directory of Open Access Journals (Sweden)

Guillermo Sánchez-de la Vega

2018-03-01

Full Text Available Analyses of genetic variation allow understanding the origin, diversification and genetic resources of cultivated plants. Domesticated taxa and their wild relatives are ideal systems for studying genetic processes of plant domestication and their joint is important to evaluate the distribution of their genetic resources. Such is the case of the domesticated subspecies C. argyrosperma ssp. argyrosperma, known in Mexico as calabaza pipiana, and its wild relative C. argyrosperma ssp. sororia. The main aim of this study was to use molecular data (microsatellites to assess the levels of genetic variation and genetic differentiation within and among populations of domesticated argyrosperma across its distribution in Mexico in comparison to its wild relative, sororia, and to identify environmental suitability in previously proposed centers of domestication. We analyzed nine unlinked nuclear microsatellite loci to assess levels of diversity and distribution of genetic variation within and among populations in 440 individuals from 19 populations of cultivated landraces of argyrosperma and from six wild populations of sororia, in order to conduct a first systematic analysis of their genetic resources. We also used species distribution models (SDMs for sororia to identify changes in this wild subspecies’ distribution from the Holocene (∼6,000 years ago to the present, and to assess the presence of suitable environmental conditions in previously proposed domestication sites. Genetic variation was similar among subspecies (HE = 0.428 in sororia, and HE = 0.410 in argyrosperma. Nine argyrosperma populations showed significant levels of inbreeding. Both subspecies are well differentiated, and genetic differentiation (FST among populations within each subspecies ranged from 0.152 to 0.652. Within argyrosperma we found three genetic groups (Northern Mexico, Yucatan Peninsula, including Michoacan and Veracruz, and Pacific coast plus Durango. We detected low

An animal model of differential genetic risk for methamphetamine intake

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Tamara ePhillips

2015-09-01

Full Text Available The question of whether genetic factors contribute to risk for methamphetamine (MA use and dependence has not been intensively investigated. Compared to human populations, genetic animal models offer the advantages of control over genetic family history and drug exposure. Using selective breeding, we created lines of mice that differ in genetic risk for voluntary MA intake and identified the chromosomal addresses of contributory genes. A quantitative trait locus was identified on chromosome 10 that accounts for more than 50% of the genetic variance in MA intake in the selected mouse lines. In addition, behavioral and physiological screening identified differences corresponding with risk for MA intake that have generated hypotheses that are testable in humans. Heightened sensitivity to aversive and certain physiological effects of MA, such as MA-induced reduction in body temperature, are hallmarks of mice bred for low MA intake. Furthermore, unlike MA-avoiding mice, MA-preferring mice are sensitive to rewarding and reinforcing MA effects, and to MA-induced increases in brain extracellular dopamine levels. Gene expression analyses implicate the importance of a network enriched in transcription factor genes, some of which regulate the mu opioid receptor gene, Oprm1, in risk for MA use. Neuroimmune factors appear to play a role in differential response to MA between the mice bred for high and low intake. In addition, chromosome 10 candidate gene studies provide strong support for a trace amine associated receptor 1 gene, Taar1, polymorphism in risk for MA intake. MA is a trace amine-associated receptor 1 (TAAR1 agonist, and a non-functional Taar1 allele segregates with high MA consumption. Thus, reduced TAAR1 function has the potential to increase risk for MA use. Overall, existing findings support the MA drinking lines as a powerful model for identifying genetic factors involved in determining risk for harmful MA use. Future directions include the

Gene × Environment Interactions in Schizophrenia: Evidence from Genetic Mouse Models

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Paula Moran

2016-01-01

Full Text Available The study of gene × environment, as well as epistatic interactions in schizophrenia, has provided important insight into the complex etiopathologic basis of schizophrenia. It has also increased our understanding of the role of susceptibility genes in the disorder and is an important consideration as we seek to translate genetic advances into novel antipsychotic treatment targets. This review summarises data arising from research involving the modelling of gene × environment interactions in schizophrenia using preclinical genetic models. Evidence for synergistic effects on the expression of schizophrenia-relevant endophenotypes will be discussed. It is proposed that valid and multifactorial preclinical models are important tools for identifying critical areas, as well as underlying mechanisms, of convergence of genetic and environmental risk factors, and their interaction in schizophrenia.

On relativistic models of strange stars

Indian Academy of Sciences (India)

tractable models of superdense stars in equilibrium. Several aspects of physical relevance of compact star models, based on Vaidya–Tikekar ansatz, have been in- vestigated [7–10] by a number of workers. Mukherjee et al [11–13] indicated the possibility of using this set-up to describe models of the compact star like Her.

A Rational Model In Theoretical Genetics

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Karl Javorszky

2008-07-01

Full Text Available This model connects information processing in biological organisms with methods and concepts used in classical, technical information processing. The central concept shows copying and regulatory interaction between a logical sequence consisting of triplets and the amount of constituents of a set. The basic mathematical model of information processing within a biological cell has been worked out. The cell in the model copies its present state into a sequence and reads it off the sequence. The sequence comes in triplets and is not one sequence but appears in two almost identical varieties. We treat consecutive and contemporary assemblies of information carrying media as equally suited to contain information. Methods used so far utilised the consecutive property of media, while in biology one observes the concurrent existence of specific realisations of possibilities. Genetics connects the two approaches by using an interplay between consecutively (sequentially ordered logical markers (the DNA and the state of the set engulfing the DNA. Several mathematical tools have been evolved to assemble an interface between sequentially ordered carriers and the same number of carriers if they arrive contemporaneously. Using linguistic theory and formal logic one concludes that measurement(s on a cell are a (set of logical sentence(s relating to an assembly of n objects with group structures among each other. We linearise and count all possible group relations on a set of n objects and introduce the concept of multidimensional partitions hitherto left undefined. We introduce the concept of a maximally structured set by establishing an upper limit to the information carrying capacity of n objects used commutatively and sequentially at the same time (like genetics does. The copying and re-copying mechanism which is the core matter with genetics appears in the model as differing transmission efficiency coefficients of media if the media are used once sequentially

Dependence of Computational Models on Input Dimension: Tractability of Approximation and Optimization Tasks

Czech Academy of Sciences Publication Activity Database

Kainen, P.C.; Kůrková, Věra; Sanguineti, M.

2012-01-01

Roč. 58, č. 2 (2012), s. 1203-1214 ISSN 0018-9448 R&D Projects: GA MŠk(CZ) ME10023; GA ČR GA201/08/1744; GA ČR GAP202/11/1368 Grant - others:CNR-AV ČR(CZ-IT) Project 2010–2012 Complexity of Neural -Network and Kernel Computational Models Institutional research plan: CEZ:AV0Z10300504 Keywords : dictionary-based computational models * high-dimensional approximation and optimization * model complexity * polynomial upper bounds Subject RIV: IN - Informatics, Computer Science Impact factor: 2.621, year: 2012

Tractable approximations for probabilistic models: The adaptive Thouless-Anderson-Palmer mean field approach

DEFF Research Database (Denmark)

Opper, Manfred; Winther, Ole

2001-01-01

We develop an advanced mean held method for approximating averages in probabilistic data models that is based on the Thouless-Anderson-Palmer (TAP) approach of disorder physics. In contrast to conventional TAP. where the knowledge of the distribution of couplings between the random variables...... is required. our method adapts to the concrete couplings. We demonstrate the validity of our approach, which is so far restricted to models with nonglassy behavior? by replica calculations for a wide class of models as well as by simulations for a real data set....

Genetics of traffic assignment models for strategic transport planning

NARCIS (Netherlands)

Bliemer, M.C.J.; Raadsen, M.P.H.; Brederode, L.J.N.; Bell, M.G.H.; Wismans, Luc Johannes Josephus; Smith, M.J.

2016-01-01

This paper presents a review and classification of traffic assignment models for strategic transport planning purposes by using concepts analogous to genetics in biology. Traffic assignment models share the same theoretical framework (DNA), but differ in capability (genes). We argue that all traffic

Cross-validation analysis for genetic evaluation models for ranking in endurance horses.

Science.gov (United States)

García-Ballesteros, S; Varona, L; Valera, M; Gutiérrez, J P; Cervantes, I

2018-01-01

Ranking trait was used as a selection criterion for competition horses to estimate racing performance. In the literature the most common approaches to estimate breeding values are the linear or threshold statistical models. However, recent studies have shown that a Thurstonian approach was able to fix the race effect (competitive level of the horses that participate in the same race), thus suggesting a better prediction accuracy of breeding values for ranking trait. The aim of this study was to compare the predictability of linear, threshold and Thurstonian approaches for genetic evaluation of ranking in endurance horses. For this purpose, eight genetic models were used for each approach with different combinations of random effects: rider, rider-horse interaction and environmental permanent effect. All genetic models included gender, age and race as systematic effects. The database that was used contained 4065 ranking records from 966 horses and that for the pedigree contained 8733 animals (47% Arabian horses), with an estimated heritability around 0.10 for the ranking trait. The prediction ability of the models for racing performance was evaluated using a cross-validation approach. The average correlation between real and predicted performances across genetic models was around 0.25 for threshold, 0.58 for linear and 0.60 for Thurstonian approaches. Although no significant differences were found between models within approaches, the best genetic model included: the rider and rider-horse random effects for threshold, only rider and environmental permanent effects for linear approach and all random effects for Thurstonian approach. The absolute correlations of predicted breeding values among models were higher between threshold and Thurstonian: 0.90, 0.91 and 0.88 for all animals, top 20% and top 5% best animals. For rank correlations these figures were 0.85, 0.84 and 0.86. The lower values were those between linear and threshold approaches (0.65, 0.62 and 0.51). In

Applicability of genetic algorithms to parameter estimation of economic models

Directory of Open Access Journals (Sweden)

Marcel Ševela

2004-01-01

Full Text Available The paper concentrates on capability of genetic algorithms for parameter estimation of non-linear economic models. In the paper we test the ability of genetic algorithms to estimate of parameters of demand function for durable goods and simultaneously search for parameters of genetic algorithm that lead to maximum effectiveness of the computation algorithm. The genetic algorithms connect deterministic iterative computation methods with stochastic methods. In the genteic aůgorithm approach each possible solution is represented by one individual, those life and lifes of all generations of individuals run under a few parameter of genetic algorithm. Our simulations resulted in optimal mutation rate of 15% of all bits in chromosomes, optimal elitism rate 20%. We can not set the optimal extend of generation, because it proves positive correlation with effectiveness of genetic algorithm in all range under research, but its impact is degreasing. The used genetic algorithm was sensitive to mutation rate at most, than to extend of generation. The sensitivity to elitism rate is not so strong.

Heterogeneous expectations in monetary DSGE models

NARCIS (Netherlands)

Massaro, D.

2013-01-01

This paper derives a general New Keynesian framework with heterogeneous expectations by explicitly solving the micro-foundations underpinning the model. The resulting reduced form is analytically tractable and encompasses the representative rational agent benchmark as a special case. We specify a

Genetic algorithms and experimental discrimination of SUSY models

International Nuclear Information System (INIS)

Allanach, B.C.; Quevedo, F.; Grellscheid, D.

2004-01-01

We introduce genetic algorithms as a means to estimate the accuracy required to discriminate among different models using experimental observables. We exemplify the technique in the context of the minimal supersymmetric standard model. If supersymmetric particles are discovered, models of supersymmetry breaking will be fit to the observed spectrum and it is beneficial to ask beforehand: what accuracy is required to always allow the discrimination of two particular models and which are the most important masses to observe? Each model predicts a bounded patch in the space of observables once unknown parameters are scanned over. The questions can be answered by minimising a 'distance' measure between the two hypersurfaces. We construct a distance measure that scales like a constant fraction of an observable, since that is how the experimental errors are expected to scale. Genetic algorithms, including concepts such as natural selection, fitness and mutations, provide a solution to the minimisation problem. We illustrate the efficiency of the method by comparing three different classes of string models for which the above questions could not be answered with previous techniques. The required accuracy is in the range accessible to the Large Hadron Collider (LHC) when combined with a future linear collider (LC) facility. The technique presented here can be applied to more general classes of models or observables. (author)

Initial assessment of a model relating intratumoral genetic heterogeneity to radiological morphology

Science.gov (United States)

Noterdaeme, O; Kelly, M; Friend, P; Soonowalla, Z; Steers, G; Brady, M

2010-01-01

Tumour heterogeneity has major implications for tumour development and response to therapy. Tumour heterogeneity results from mutations in the genes responsible for mismatch repair or maintenance of chromosomal stability. Cells with different genetic properties may grow at different rates and exhibit different resistance to therapeutic interventions. To date, there exists no approach to non-invasively assess tumour heterogeneity. Here we present a biologically inspired model of tumour growth, which relates intratumoral genetic heterogeneity to gross morphology visible on radiological images. The model represents the development of a tumour as a set of expanding spheres, each sphere representing a distinct clonal centre, with the sprouting of new spheres corresponding to new clonal centres. Each clonal centre may possess different characteristics relating to genetic composition, growth rate and response to treatment. We present a clinical example for which the model accurately tracks tumour growth and shows the correspondence to genetic variation (as determined by array comparative genomic hybridisation). One clinical implication of our work is that the assessment of heterogeneous tumours using Response Evaluation Criteria In Solid Tumours (RECIST) or volume measurements may not accurately reflect tumour growth, stability or the response to treatment. We believe that this is the first model linking the macro-scale appearance of tumours to their genetic composition. We anticipate that our model will provide a more informative way to assess the response of heterogeneous tumours to treatment, which is of increasing importance with the development of novel targeted anti-cancer treatments. PMID:19690073

Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity

Science.gov (United States)

Louis, S.J.; Raines, G.L.

2003-01-01

We use a genetic algorithm to calibrate a spatially and temporally resolved cellular automata to model mining activity on public land in Idaho and western Montana. The genetic algorithm searches through a space of transition rule parameters of a two dimensional cellular automata model to find rule parameters that fit observed mining activity data. Previous work by one of the authors in calibrating the cellular automaton took weeks - the genetic algorithm takes a day and produces rules leading to about the same (or better) fit to observed data. These preliminary results indicate that genetic algorithms are a viable tool in calibrating cellular automata for this application. Experience gained during the calibration of this cellular automata suggests that mineral resource information is a critical factor in the quality of the results. With automated calibration, further refinements of how the mineral-resource information is provided to the cellular automaton will probably improve our model.

Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures

Science.gov (United States)

Liang, Shoudan; Fuhrman, Stefanie; Somogyi, Roland

1998-01-01

Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

Different concepts and models of information for family-relevant genetic findings: comparison and ethical analysis.

Science.gov (United States)

Lenk, Christian; Frommeld, Debora

2015-08-01

Genetic predispositions often concern not only individual persons, but also other family members. Advances in the development of genetic tests lead to a growing number of genetic diagnoses in medical practice and to an increasing importance of genetic counseling. In the present article, a number of ethical foundations and preconditions for this issue are discussed. Four different models for the handling of genetic information are presented and analyzed including a discussion of practical implications. The different models' ranges of content reach from a strictly autonomous position over self-governed arrangements in the practice of genetic counseling up to the involvement of official bodies and committees. The different models show a number of elements which seem to be very useful for the handling of genetic data in families from an ethical perspective. In contrast, the limitations of the standard medical attempt regarding confidentiality and personal autonomy in the context of genetic information in the family are described. Finally, recommendations for further ethical research and the development of genetic counseling in families are given.

Parallel Genetic Algorithms for calibrating Cellular Automata models: Application to lava flows

International Nuclear Information System (INIS)

D'Ambrosio, D.; Spataro, W.; Di Gregorio, S.; Calabria Univ., Cosenza; Crisci, G.M.; Rongo, R.; Calabria Univ., Cosenza

2005-01-01

Cellular Automata are highly nonlinear dynamical systems which are suitable far simulating natural phenomena whose behaviour may be specified in terms of local interactions. The Cellular Automata model SCIARA, developed far the simulation of lava flows, demonstrated to be able to reproduce the behaviour of Etnean events. However, in order to apply the model far the prediction of future scenarios, a thorough calibrating phase is required. This work presents the application of Genetic Algorithms, general-purpose search algorithms inspired to natural selection and genetics, far the parameters optimisation of the model SCIARA. Difficulties due to the elevated computational time suggested the adoption a Master-Slave Parallel Genetic Algorithm far the calibration of the model with respect to the 2001 Mt. Etna eruption. Results demonstrated the usefulness of the approach, both in terms of computing time and quality of performed simulations

Genetic Algorithm Based Microscale Vehicle Emissions Modelling

Directory of Open Access Journals (Sweden)

Sicong Zhu

2015-01-01

Full Text Available There is a need to match emission estimations accuracy with the outputs of transport models. The overall error rate in long-term traffic forecasts resulting from strategic transport models is likely to be significant. Microsimulation models, whilst high-resolution in nature, may have similar measurement errors if they use the outputs of strategic models to obtain traffic demand predictions. At the microlevel, this paper discusses the limitations of existing emissions estimation approaches. Emission models for predicting emission pollutants other than CO2 are proposed. A genetic algorithm approach is adopted to select the predicting variables for the black box model. The approach is capable of solving combinatorial optimization problems. Overall, the emission prediction results reveal that the proposed new models outperform conventional equations in terms of accuracy and robustness.

Genetic algorithms and genetic programming for multiscale modeling: Applications in materials science and chemistry and advances in scalability

Science.gov (United States)

Sastry, Kumara Narasimha

2007-03-01

Effective and efficient rnultiscale modeling is essential to advance both the science and synthesis in a, wide array of fields such as physics, chemistry, materials science; biology, biotechnology and pharmacology. This study investigates the efficacy and potential of rising genetic algorithms for rnultiscale materials modeling and addresses some of the challenges involved in designing competent algorithms that solve hard problems quickly, reliably and accurately. In particular, this thesis demonstrates the use of genetic algorithms (GAs) and genetic programming (GP) in multiscale modeling with the help of two non-trivial case studies in materials science and chemistry. The first case study explores the utility of genetic programming (GP) in multi-timescaling alloy kinetics simulations. In essence, GP is used to bridge molecular dynamics and kinetic Monte Carlo methods to span orders-of-magnitude in simulation time. Specifically, GP is used to regress symbolically an inline barrier function from a limited set of molecular dynamics simulations to enable kinetic Monte Carlo that simulate seconds of real time. Results on a non-trivial example of vacancy-assisted migration on a surface of a face-centered cubic (fcc) Copper-Cobalt (CuxCo 1-x) alloy show that GP predicts all barriers with 0.1% error from calculations for less than 3% of active configurations, independent of type of potentials used to obtain the learning set of barriers via molecular dynamics. The resulting method enables 2--9 orders-of-magnitude increase in real-time dynamics simulations taking 4--7 orders-of-magnitude less CPU time. The second case study presents the application of multiobjective genetic algorithms (MOGAs) in multiscaling quantum chemistry simulations. Specifically, MOGAs are used to bridge high-level quantum chemistry and semiempirical methods to provide accurate representation of complex molecular excited-state and ground-state behavior. Results on ethylene and benzene---two common

An empirically tractable model of optimal oil spills prevention in Russian sea harbours

Energy Technology Data Exchange (ETDEWEB)

Deissenberg, C. [CEFI-CNRS, Les Milles (France); Gurman, V.; Tsirlin, A. [RAS, Program Systems Inst., Pereslavl-Zalessky (Russian Federation); Ryumina, E. [Russian Academy of Sciences, Moscow (Russian Federation). Inst. of Economic Market Problems

2001-07-01

Based on previous theoretical work by Gottinger (1997, 1998), we propose a simple model of optimal monitoring of oil-related activities in harbour areas that is suitable for empirical estimation within the Russian-Ukrainian context, in spite of the poor availability of data in these countries. Specifically, the model indicates how to best allocate at the steady state a given monitoring budget between different monitoring activities. An approximate analytical solution to the optimization problem is derived, and a simple procedure for estimating the model on the basis of the actually available data is suggested. An application using data obtained for several harbours of the Black and Baltic Seas is given. It suggests that the current Russian monitoring practice could be much improved by better allocating the available monitoring resources. (Author)

Model parameters estimation and sensitivity by genetic algorithms

International Nuclear Information System (INIS)

Marseguerra, Marzio; Zio, Enrico; Podofillini, Luca

2003-01-01

In this paper we illustrate the possibility of extracting qualitative information on the importance of the parameters of a model in the course of a Genetic Algorithms (GAs) optimization procedure for the estimation of such parameters. The Genetic Algorithms' search of the optimal solution is performed according to procedures that resemble those of natural selection and genetics: an initial population of alternative solutions evolves within the search space through the four fundamental operations of parent selection, crossover, replacement, and mutation. During the search, the algorithm examines a large amount of solution points which possibly carries relevant information on the underlying model characteristics. A possible utilization of this information amounts to create and update an archive with the set of best solutions found at each generation and then to analyze the evolution of the statistics of the archive along the successive generations. From this analysis one can retrieve information regarding the speed of convergence and stabilization of the different control (decision) variables of the optimization problem. In this work we analyze the evolution strategy followed by a GA in its search for the optimal solution with the aim of extracting information on the importance of the control (decision) variables of the optimization with respect to the sensitivity of the objective function. The study refers to a GA search for optimal estimates of the effective parameters in a lumped nuclear reactor model of literature. The supporting observation is that, as most optimization procedures do, the GA search evolves towards convergence in such a way to stabilize first the most important parameters of the model and later those which influence little the model outputs. In this sense, besides estimating efficiently the parameters values, the optimization approach also allows us to provide a qualitative ranking of their importance in contributing to the model output. The

[The discussion of the infiltrative model of mathematical knowledge to genetics teaching].

Science.gov (United States)

Liu, Jun; Luo, Pei-Gao

2011-11-01

Genetics, the core course of biological field, is an importance major-basic course in curriculum of many majors related with biology. Due to strong theoretical and practical as well as abstract of genetics, it is too difficult to study on genetics for many students. At the same time, mathematics is one of the basic courses in curriculum of the major related natural science, which has close relationship with the establishment, development and modification of genetics. In this paper, to establish the intrinsic logistic relationship and construct the integral knowledge network and to help students improving the analytic, comprehensive and logistic abilities, we applied some mathematical infiltrative model genetic knowledge in genetics teaching, which could help students more deeply learn and understand genetic knowledge.

Research on interactive genetic-geological models to evaluate favourability for undiscovered uranium resources

International Nuclear Information System (INIS)

Finch, W.I.; Granger, H.C.; Lupe, R.; McCammon, R.B.

1980-01-01

Current methods of evaluating favourability for undiscovered uranium resources are unduly subjective, quite possibly inconsistent and, as a consequence, of questionable reliability. This research is aimed at reducing the subjectivity and increasing the reliability by designing an improved method that depends largely on geological data and their statistical frequency of occurrence. This progress report outlines a genetic approach to modelling the geological factors that controlled uranium mineralization in order to evaluate the favourability for the occurrence of undiscovered uranium deposits of the type modelled. A genetic model is constructed from all the factors that describe the processes, in chronological sequence, that formed uranium deposits thought to have a common origin. The field and laboratory evidence for the processes constitute a geologic-occurrence base that parallels the chronological sequence of events. The genetic model and the geologic-occurrence base are portrayed as two columns of an interactive matrix called the ''genetic-geologic model''. For each column, eight chronological stages are used to describe the overall formation of the uranium deposits. These stages consist of (1) precursor processes; (2) host-rock formation; (3) preparation of host-rock; (4) uranium-source development; (5) transport of uranium; (6) primary uranium deposition; (7) post-deposition modification; and (8) preservation. To apply the genetic-geological model to evaluate favourability, a question is posed that determines the presence or absence of each attribute listed under the geologic-occurrence base. By building a logic circuit of the attributes according to either their essential or non-essential nature, the resultant match between a well-documented control area and the test area may be determined. The degree of match is a measure of favourability for uranium occurrence as hypothesized in the genetic model

Genetic Process Mining: Alignment-based Process Model Mutation

NARCIS (Netherlands)

Eck, van M.L.; Buijs, J.C.A.M.; Dongen, van B.F.; Fournier, F.; Mendling, J.

2015-01-01

The Evolutionary Tree Miner (ETM) is a genetic process discovery algorithm that enables the user to guide the discovery process based on preferences with respect to four process model quality dimensions: replay fitness, precision, generalization and simplicity. Traditionally, the ETM algorithm uses

Estimation in a multiplicative mixed model involving a genetic relationship matrix

Directory of Open Access Journals (Sweden)

Eccleston John A

2009-04-01

Full Text Available Abstract Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA form. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments.

Genetic search feature selection for affective modeling

DEFF Research Database (Denmark)

Martínez, Héctor P.; Yannakakis, Georgios N.

2010-01-01

Automatic feature selection is a critical step towards the generation of successful computational models of affect. This paper presents a genetic search-based feature selection method which is developed as a global-search algorithm for improving the accuracy of the affective models built....... The method is tested and compared against sequential forward feature selection and random search in a dataset derived from a game survey experiment which contains bimodal input features (physiological and gameplay) and expressed pairwise preferences of affect. Results suggest that the proposed method...

Emerging technologies to create inducible and genetically defined porcine cancer models

Directory of Open Access Journals (Sweden)

Lawrence B Schook

2016-02-01

Full Text Available There is an emerging need for new animal models that address unmet translational cancer research requirements. Transgenic porcine models provide an exceptional opportunity due to their genetic, anatomic and physiological similarities with humans. Due to recent advances in the sequencing of domestic animal genomes and the development of new organism cloning technologies, it is now very feasible to utilize pigs as a malleable species, with similar anatomic and physiological features with humans, in which to develop cancer models. In this review, we discuss genetic modification technologies successfully used to produce porcine biomedical models, in particular the Cre-loxP System as well as major advances and perspectives the CRISPR/Cas9 System. Recent advancements in porcine tumor modeling and genome editing will bring porcine models to the forefront of translational cancer research.

Emerging Technologies to Create Inducible and Genetically Defined Porcine Cancer Models.

Science.gov (United States)

Schook, Lawrence B; Rund, Laurie; Begnini, Karine R; Remião, Mariana H; Seixas, Fabiana K; Collares, Tiago

2016-01-01

There is an emerging need for new animal models that address unmet translational cancer research requirements. Transgenic porcine models provide an exceptional opportunity due to their genetic, anatomic, and physiological similarities with humans. Due to recent advances in the sequencing of domestic animal genomes and the development of new organism cloning technologies, it is now very feasible to utilize pigs as a malleable species, with similar anatomic and physiological features with humans, in which to develop cancer models. In this review, we discuss genetic modification technologies successfully used to produce porcine biomedical models, in particular the Cre-loxP System as well as major advances and perspectives the CRISPR/Cas9 System. Recent advancements in porcine tumor modeling and genome editing will bring porcine models to the forefront of translational cancer research.

Quantitative genetics of Taura syndrome resistance in Pacific (Penaeus vannamei): A cure model approach

DEFF Research Database (Denmark)

Ødegård, Jørgen; Gitterle, Thomas; Madsen, Per

2011-01-01

cure survival model using Gibbs sampling, treating susceptibility and endurance as separate genetic traits. Results: Overall mortality at the end of test was 28%, while 38% of the population was considered susceptible to the disease. The estimated underlying heritability was high for susceptibility (0....... However, genetic evaluation of susceptibility based on the cure model showed clear associations with standard genetic evaluations that ignore the cure fraction for these data. Using the current testing design, genetic variation in observed survival time and absolute survival at the end of test were most...

Genetic hotels for the standard genetic code: evolutionary analysis based upon novel three-dimensional algebraic models.

Science.gov (United States)

José, Marco V; Morgado, Eberto R; Govezensky, Tzipe

2011-07-01

Herein, we rigorously develop novel 3-dimensional algebraic models called Genetic Hotels of the Standard Genetic Code (SGC). We start by considering the primeval RNA genetic code which consists of the 16 codons of type RNY (purine-any base-pyrimidine). Using simple algebraic operations, we show how the RNA code could have evolved toward the current SGC via two different intermediate evolutionary stages called Extended RNA code type I and II. By rotations or translations of the subset RNY, we arrive at the SGC via the former (type I) or via the latter (type II), respectively. Biologically, the Extended RNA code type I, consists of all codons of the type RNY plus codons obtained by considering the RNA code but in the second (NYR type) and third (YRN type) reading frames. The Extended RNA code type II, comprises all codons of the type RNY plus codons that arise from transversions of the RNA code in the first (YNY type) and third (RNR) nucleotide bases. Since the dimensions of remarkable subsets of the Genetic Hotels are not necessarily integer numbers, we also introduce the concept of algebraic fractal dimension. A general decoding function which maps each codon to its corresponding amino acid or the stop signals is also derived. The Phenotypic Hotel of amino acids is also illustrated. The proposed evolutionary paths are discussed in terms of the existing theories of the evolution of the SGC. The adoption of 3-dimensional models of the Genetic and Phenotypic Hotels will facilitate the understanding of the biological properties of the SGC.

Load-aware modeling for uplink cellular networks in a multi-channel environment

KAUST Repository

Alammouri, Ahmad; Elsawy, Hesham; Alouini, Mohamed-Slim

2014-01-01

We exploit tools from stochastic geometry to develop a tractable analytical approach for modeling uplink cellular networks. The developed model is load aware and accounts for per-user power control as well as the limited transmit power constraint

Medulloblastoma: Molecular Genetics and Animal Models

Directory of Open Access Journals (Sweden)

Corey Raffel

2004-07-01

Full Text Available Medulloblastoma is a primary brain tumor found in the cerebellum of children. The tumor occurs in association with two inherited cancer syndromes: Turcot syndrome and Gorlin syndrome. Insights into the molecular biology of the tumor have come from looking at alterations in the genes altered in these syndromes, PTC and APC, respectively. Murine models of medulloblastoma have been constructed based on these alterations. Additional murine models that, while mimicking the appearance of the human tumor, seem unrelated to the human tumor's molecular alterations have been made. In this review, the clinical picture, origin, molecular biology, murine models of medulloblastoma are discussed. Although a great deal has been discovered about this tumor, the genetic alterations responsible for tumor development in a majority of patients have yet to be described.

Genetic models of absence epilepsy: New concepts and insights

NARCIS (Netherlands)

Luijtelaar, E.L.J.M. van; Coenen, A.M.L.; Schwartzkroin, P.A.

2009-01-01

The discovery, development, and use of genetic rodent models of absence epilepsy have led to a new theory about the origin of absence seizures. A focal zone has been identified in the peri-oral region of the somatosensory cortex in WAG/Rij and GAERS – the two most commonly used models – from which

Dynamic modeling of genetic networks using genetic algorithm and S-system.

Science.gov (United States)

Kikuchi, Shinichi; Tominaga, Daisuke; Arita, Masanori; Takahashi, Katsutoshi; Tomita, Masaru

2003-03-22

The modeling of system dynamics of genetic networks, metabolic networks or signal transduction cascades from time-course data is formulated as a reverse-problem. Previous studies focused on the estimation of only network structures, and they were ineffective in inferring a network structure with feedback loops. We previously proposed a method to predict not only the network structure but also its dynamics using a Genetic Algorithm (GA) and an S-system formalism. However, it could predict only a small number of parameters and could rarely obtain essential structures. In this work, we propose a unified extension of the basic method. Notable improvements are as follows: (1) an additional term in its evaluation function that aims at eliminating futile parameters; (2) a crossover method called Simplex Crossover (SPX) to improve its optimization ability; and (3) a gradual optimization strategy to increase the number of predictable parameters. The proposed method is implemented as a C program called PEACE1 (Predictor by Evolutionary Algorithms and Canonical Equations 1). Its performance was compared with the basic method. The comparison showed that: (1) the convergence rate increased about 5-fold; (2) the optimization speed was raised about 1.5-fold; and (3) the number of predictable parameters was increased about 5-fold. Moreover, we successfully inferred the dynamics of a small genetic network constructed with 60 parameters for 5 network variables and feedback loops using only time-course data of gene expression.

Genetic mouse models relevant to schizophrenia: taking stock and looking forward.

Science.gov (United States)

Harrison, Paul J; Pritchett, David; Stumpenhorst, Katharina; Betts, Jill F; Nissen, Wiebke; Schweimer, Judith; Lane, Tracy; Burnet, Philip W J; Lamsa, Karri P; Sharp, Trevor; Bannerman, David M; Tunbridge, Elizabeth M

2012-03-01

Genetic mouse models relevant to schizophrenia complement, and have to a large extent supplanted, pharmacological and lesion-based rat models. The main attraction is that they potentially have greater construct validity; however, they share the fundamental limitations of all animal models of psychiatric disorder, and must also be viewed in the context of the uncertain and complex genetic architecture of psychosis. Some of the key issues, including the choice of gene to target, the manner of its manipulation, gene-gene and gene-environment interactions, and phenotypic characterization, are briefly considered in this commentary, illustrated by the relevant papers reported in this special issue. Copyright © 2011 Elsevier Ltd. All rights reserved.

Models for Estimating Genetic Parameters of Milk Production Traits Using Random Regression Models in Korean Holstein Cattle

Directory of Open Access Journals (Sweden)

C. I. Cho

2016-05-01

Full Text Available The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs, and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK, fat yield (FAT, protein yield (PROT, and solids-not-fat yield (SNF. The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP of the third to fifth order (L3–L5, fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order. The residual variances in the models were either homogeneous (HOM or heterogeneous (15 classes, HET15; 60 classes, HET60. A total of nine models (3 orders of polynomials×3 types of residual variance including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC and/or Schwarz Bayesian information criteria (BIC statistics to identify the model(s of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF and L4-HET15 (FAT, which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first

ENU mutagenesis to generate genetically modified rat models

NARCIS (Netherlands)

van Boxtel, R.; Gould, M.; Cuppen, E.; Smits, B.M.

2010-01-01

The rat is one of the most preferred model organisms in biomedical research and has been extremely useful for linking physiology and pathology to the genome. However, approaches to genetically modify specific genes in the rat germ line remain relatively scarce. To date, the most efficient approach

A review of animal models used to evaluate potential allergenicity of genetically modified organisms (GMOs)

DEFF Research Database (Denmark)

Marsteller, Nathan; Bøgh, Katrine Lindholm; Goodman, Richard E.

2017-01-01

Food safety regulators request prediction of allergenicity for newly expressed proteins in genetically modified (GM) crops and in novel foods. Some have suggested using animal models to assess potential allergenicity. A variety of animal models have been used in research to evaluate sensitisation...... of genetically modified organisms (GMOs).......Food safety regulators request prediction of allergenicity for newly expressed proteins in genetically modified (GM) crops and in novel foods. Some have suggested using animal models to assess potential allergenicity. A variety of animal models have been used in research to evaluate sensitisation...

Applications of Systems Genetics and Biology for Obesity Using Pig Models

DEFF Research Database (Denmark)

Kogelman, Lisette; Kadarmideen, Haja N.

2016-01-01

approach, a branch of systems biology. In this chapter, we will describe the state of the art of genetic studies on human obesity, using pig populations. We will describe the features of using the pig as a model for human obesity and briefly discuss the genetics of obesity, and we will focus on systems...

Genetic Modeling of Radiation Injury in Prostate Cancer Patients Treated with Radiotherapy

Science.gov (United States)

2017-10-01

AWARD NUMBER: W81XWH-15-1-0681 TITLE: Genetic Modeling of Radiation Injury in Prostate Cancer Patients Treated with Radiotherapy PRINCIPAL...TITLE AND SUBTITLE 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-15-1-0681Genetic Modeling of Radiation Injury in Prostate Cancer Patients Treated...effects, urinary morbidity, rectal injury, sexual dysfunction 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF

Equilibrium and non-equilibrium concepts in forest genetic modelling: population- and individually-based approaches

OpenAIRE

Kramer, Koen; van der Werf, D. C.

2010-01-01

The environment is changing and so are forests, in their functioning, in species composition, and in the species’ genetic composition. Many empirical and process-based models exist to support forest management. However, most of these models do not consider the impact of environmental changes and forest management on genetic diversity nor on the rate of adaptation of critical plant processes. How genetic diversity and rates of adaptation depend on management actions is a crucial next step in m...

Structured spatio-temporal shot-noise Cox point process models, with a view to modelling forest fires

DEFF Research Database (Denmark)

Møller, Jesper; Diaz-Avalos, Carlos

Spatio-temporal Cox point process models with a multiplicative structure for the driving random intensity, incorporating covariate information into temporal and spatial components, and with a residual term modelled by a shot-noise process, are considered. Such models are flexible and tractable fo...... dataset consisting of 2796 days and 5834 spatial locations of fires. The model is compared with a spatio-temporal log-Gaussian Cox point process model, and likelihood-based methods are discussed to some extent....

Structured Spatio-temporal shot-noise Cox point process models, with a view to modelling forest fires

DEFF Research Database (Denmark)

Møller, Jesper; Diaz-Avalos, Carlos

2010-01-01

Spatio-temporal Cox point process models with a multiplicative structure for the driving random intensity, incorporating covariate information into temporal and spatial components, and with a residual term modelled by a shot-noise process, are considered. Such models are flexible and tractable fo...... data set consisting of 2796 days and 5834 spatial locations of fires. The model is compared with a spatio-temporal log-Gaussian Cox point process model, and likelihood-based methods are discussed to some extent....

Model-based problem solving through symbolic regression via pareto genetic programming

NARCIS (Netherlands)

Vladislavleva, E.

2008-01-01

Pareto genetic programming methodology is extended by additional generic model selection and generation strategies that (1) drive the modeling engine to creation of models of reduced non-linearity and increased generalization capabilities, and (2) improve the effectiveness of the search for robust

Bacillus subtilis as a Platform for Molecular Characterisation of Regulatory Mechanisms of Enterococcus faecalis Resistance against Cell Wall Antibiotics

OpenAIRE

Fang, Chong; Stiegeler, Emanuel; Cook, Gregory M.; Mascher, Thorsten; Gebhard, Susanne

2014-01-01

To combat antibiotic resistance of Enterococcus faecalis, a better understanding of the molecular mechanisms, particularly of antibiotic detection, signal transduction and gene regulation is needed. Because molecular studies in this bacterium can be challenging, we aimed at exploiting the genetically highly tractable Gram-positive model organism Bacillus subtilis as a heterologous host. Two fundamentally different regulators of E. faecalis resistance against cell wall antibiotics, the bacitra...

Multicolor bleach-rate imaging enlightens in vivo sterol transport

DEFF Research Database (Denmark)

Wüstner, Daniel; Sage, Daniel

2011-01-01

, dehydroergosterol (DHE) in the genetically tractable model organism Caenorhabditis elegans (C. elegans). DHE is structurally very similar to cholesterol and ergosterol, two sterols used by the sterol-auxotroph nematode. We developed a new computational method measuring fluorophore bleaching kinetics at every pixel...... with a lysosomal marker, GFP-LMP1. Our new methods hold great promise for further studies on endosomal sterol transport in C. elegans....

Reframed Genome-Scale Metabolic Model to Facilitate Genetic Design and Integration with Expression Data.

Science.gov (United States)

Gu, Deqing; Jian, Xingxing; Zhang, Cheng; Hua, Qiang

2017-01-01

Genome-scale metabolic network models (GEMs) have played important roles in the design of genetically engineered strains and helped biologists to decipher metabolism. However, due to the complex gene-reaction relationships that exist in model systems, most algorithms have limited capabilities with respect to directly predicting accurate genetic design for metabolic engineering. In particular, methods that predict reaction knockout strategies leading to overproduction are often impractical in terms of gene manipulations. Recently, we proposed a method named logical transformation of model (LTM) to simplify the gene-reaction associations by introducing intermediate pseudo reactions, which makes it possible to generate genetic design. Here, we propose an alternative method to relieve researchers from deciphering complex gene-reactions by adding pseudo gene controlling reactions. In comparison to LTM, this new method introduces fewer pseudo reactions and generates a much smaller model system named as gModel. We showed that gModel allows two seldom reported applications: identification of minimal genomes and design of minimal cell factories within a modified OptKnock framework. In addition, gModel could be used to integrate expression data directly and improve the performance of the E-Fmin method for predicting fluxes. In conclusion, the model transformation procedure will facilitate genetic research based on GEMs, extending their applications.

Estimation of genetic parameters related to eggshell strength using random regression models.

Science.gov (United States)

Guo, J; Ma, M; Qu, L; Shen, M; Dou, T; Wang, K

2015-01-01

This study examined the changes in eggshell strength and the genetic parameters related to this trait throughout a hen's laying life using random regression. The data were collected from a crossbred population between 2011 and 2014, where the eggshell strength was determined repeatedly for 2260 hens. Using random regression models (RRMs), several Legendre polynomials were employed to estimate the fixed, direct genetic and permanent environment effects. The residual effects were treated as independently distributed with heterogeneous variance for each test week. The direct genetic variance was included with second-order Legendre polynomials and the permanent environment with third-order Legendre polynomials. The heritability of eggshell strength ranged from 0.26 to 0.43, the repeatability ranged between 0.47 and 0.69, and the estimated genetic correlations between test weeks was high at > 0.67. The first eigenvalue of the genetic covariance matrix accounted for about 97% of the sum of all the eigenvalues. The flexibility and statistical power of RRM suggest that this model could be an effective method to improve eggshell quality and to reduce losses due to cracked eggs in a breeding plan.

The evolution of menstruation: A new model for genetic assimilation

Science.gov (United States)

Emera, D.; Romero, R.; Wagner, G.

2012-01-01

Why do humans menstruate while most mammals do not? Here, we present our answer to this long-debated question, arguing that (i) menstruation occurs as a mechanistic consequence of hormone-induced differentiation of the endometrium (referred to as spontaneous decidualization, or SD); (ii) SD evolved because of maternal-fetal conflict; and (iii) SD evolved by genetic assimilation of the decidualization reaction, which is induced by the fetus in non-menstruating species. The idea that menstruation occurs as a consequence of SD has been proposed in the past, but here we present a novel hypothesis on how SD evolved. We argue that decidualization became genetically stabilized in menstruating lineages, allowing females to prepare for pregnancy without any signal from the fetus. We present three models for the evolution of SD by genetic assimilation, based on recent advances in our understanding of the mechanisms of endometrial differentiation and implantation. Testing these models will ultimately shed light on the evolutionary significance of menstruation, as well as on the etiology of human reproductive disorders like endometriosis and recurrent pregnancy loss. PMID:22057551

Application of the genetic algorithm to blume-emery-griffiths model: Test Cases

International Nuclear Information System (INIS)

Erdinc, A.

2004-01-01

The equilibrium properties of the Blume-Emery-Griffiths (BEO) model Hamiltonian with the arbitrary bilinear (1), biquadratic (K) and crystal field interaction (D) are studied using the genetic algorithm technique. Results are compared with lowest approximation of the cluster variation method (CVM), which is identical to the mean field approximation. We found that the genetic algorithm to be very efficient for fast search at the average fraction of the spins, especially in the early stages as the system is far from the equilibrium state. A combination of the genetic algorithm followed by one of the well-tested simulation techniques seems to be an optimal approach. The curvature of the inverse magnetic susceptibility is also presented for the stable state of the BEG model

Drosophila olfactory memory: single genes to complex neural circuits.

Science.gov (United States)

Keene, Alex C; Waddell, Scott

2007-05-01

A central goal of neuroscience is to understand how neural circuits encode memory and guide behaviour. Studying simple, genetically tractable organisms, such as Drosophila melanogaster, can illuminate principles of neural circuit organization and function. Early genetic dissection of D. melanogaster olfactory memory focused on individual genes and molecules. These molecular tags subsequently revealed key neural circuits for memory. Recent advances in genetic technology have allowed us to manipulate and observe activity in these circuits, and even individual neurons, in live animals. The studies have transformed D. melanogaster from a useful organism for gene discovery to an ideal model to understand neural circuit function in memory.

Unified Tractable Model for Large-Scale Networks Using Stochastic Geometry: Analysis and Design

KAUST Repository

Afify, Laila H.

2016-01-01

about the interferers symbols can be approximated via the Gaussian signaling approach. The developed mathematical model presents twofold analysis unification for uplink and downlink cellular networks literature. It aligns the tangible decoding error

Neurophysiology of Drosophila Models of Parkinson’s Disease

Directory of Open Access Journals (Sweden)

Ryan J. H. West

2015-01-01

Full Text Available We provide an insight into the role Drosophila has played in elucidating neurophysiological perturbations associated with Parkinson’s disease- (PD- related genes. Synaptic signalling deficits are observed in motor, central, and sensory systems. Given the neurological impact of disease causing mutations within these same genes in humans the phenotypes observed in fly are of significant interest. As such we observe four unique opportunities provided by fly nervous system models of Parkinson’s disease. Firstly, Drosophila models are instrumental in exploring the mechanisms of neurodegeneration, with several PD-related mutations eliciting related phenotypes including sensitivity to energy supply and vesicular deformities. These are leading to the identification of plausible cellular mechanisms, which may be specific to (dopaminergic neurons and synapses rather than general cellular phenotypes. Secondly, models show noncell autonomous signalling within the nervous system, offering the opportunity to develop our understanding of the way pathogenic signalling propagates, resembling Braak’s scheme of spreading pathology in PD. Thirdly, the models link physiological deficits to changes in synaptic structure. While the structure-function relationship is complex, the genetic tractability of Drosophila offers the chance to separate fundamental changes from downstream consequences. Finally, the strong neuronal phenotypes permit relevant first in vivo drug testing.

Modeling the Isentropic Head Value of Centrifugal Gas Compressor using Genetic Programming

Directory of Open Access Journals (Sweden)

Safiyullah Ferozkhan

2016-01-01

Full Text Available Gas compressor performance is vital in oil and gas industry because of the equipment criticality which requires continuous operations. Plant operators often face difficulties in predicting appropriate time for maintenance and would usually rely on time based predictive maintenance intervals as recommended by original equipment manufacturer (OEM. The objective of this work is to develop the computational model to find the isentropic head value using genetic programming. The isentropic head value is calculated from the OEM performance chart. Inlet mass flow rate and speed of the compressor are taken as the input value. The obtained results from the GP computational models show good agreement with experimental and target data with the average prediction error of 1.318%. The genetic programming computational model will assist machinery engineers to quantify performance deterioration of gas compressor and the results from this study will be then utilized to estimate future maintenance requirements based on the historical data. In general, this genetic programming modelling provides a powerful solution for gas compressor operators to realize predictive maintenance approach in their operations.

Evolutionary model with genetics, aging, and knowledge

Science.gov (United States)

Bustillos, Armando Ticona; de Oliveira, Paulo Murilo

2004-02-01

We represent a process of learning by using bit strings, where 1 bits represent the knowledge acquired by individuals. Two ways of learning are considered: individual learning by trial and error, and social learning by copying knowledge from other individuals or from parents in the case of species with parental care. The age-structured bit string allows us to study how knowledge is accumulated during life and its influence over the genetic pool of a population after many generations. We use the Penna model to represent the genetic inheritance of each individual. In order to study how the accumulated knowledge influences the survival process, we include it to help individuals to avoid the various death situations. Modifications in the Verhulst factor do not show any special feature due to its random nature. However, by adding years to life as a function of the accumulated knowledge, we observe an improvement of the survival rates while the genetic fitness of the population becomes worse. In this latter case, knowledge becomes more important in the last years of life where individuals are threatened by diseases. Effects of offspring overprotection and differences between individual and social learning can also be observed. Sexual selection as a function of knowledge shows some effects when fidelity is imposed.

The red flour beetle as a model for bacterial oral infections.

Directory of Open Access Journals (Sweden)

Barbara Milutinović

Full Text Available Experimental infection systems are important for studying antagonistic interactions and coevolution between hosts and their pathogens. The red flour beetle Tribolium castaneum and the spore-forming bacterial insect pathogen Bacillus thuringiensis (Bt are widely used and tractable model organisms. However, they have not been employed yet as an efficient experimental system to study host-pathogen interactions. We used a high throughput oral infection protocol to infect T. castaneum insects with coleopteran specific B. thuringiensis bv. tenebrionis (Btt bacteria. We found that larval mortality depends on the dietary spore concentration and on the duration of exposure to the spores. Furthermore, differential susceptibility of larvae from different T. castaneum populations indicates that the host genetic background influences infection success. The recovery of high numbers of infectious spores from the cadavers indicates successful replication of bacteria in the host and suggests that Btt could establish infectious cycles in T. castaneum in nature. We were able to transfer plasmids from Btt to a non-pathogenic but genetically well-characterised Bt strain, which was thereafter able to successfully infect T. castaneum, suggesting that factors residing on the plasmids are important for the virulence of Btt. The availability of a genetically accessible strain will provide an ideal model for more in-depth analyses of pathogenicity factors during oral infections. Combined with the availability of the full genome sequence of T. castaneum, this system will enable analyses of host responses during infection, as well as addressing basic questions concerning host-parasite coevolution.

A Stochastic Geometry Model for Multi-hop Highway Vehicular Communication

KAUST Repository

Farooq, Muhammad Junaid; Elsawy, Hesham; Alouini, Mohamed-Slim

2015-01-01

dissemination. This paper exploits stochastic geometry to develop a tractable and accurate modeling framework to characterize the multi-hop transmissions for vehicular networks in a multi-lane highway setup. In particular, we study the tradeoffs between per

Modeling congenital disease and inborn errors of development in Drosophila melanogaster

Science.gov (United States)

Moulton, Matthew J.; Letsou, Anthea

2016-01-01

ABSTRACT Fly models that faithfully recapitulate various aspects of human disease and human health-related biology are being used for research into disease diagnosis and prevention. Established and new genetic strategies in Drosophila have yielded numerous substantial successes in modeling congenital disorders or inborn errors of human development, as well as neurodegenerative disease and cancer. Moreover, although our ability to generate sequence datasets continues to outpace our ability to analyze these datasets, the development of high-throughput analysis platforms in Drosophila has provided access through the bottleneck in the identification of disease gene candidates. In this Review, we describe both the traditional and newer methods that are facilitating the incorporation of Drosophila into the human disease discovery process, with a focus on the models that have enhanced our understanding of human developmental disorders and congenital disease. Enviable features of the Drosophila experimental system, which make it particularly useful in facilitating the much anticipated move from genotype to phenotype (understanding and predicting phenotypes directly from the primary DNA sequence), include its genetic tractability, the low cost for high-throughput discovery, and a genome and underlying biology that are highly evolutionarily conserved. In embracing the fly in the human disease-gene discovery process, we can expect to speed up and reduce the cost of this process, allowing experimental scales that are not feasible and/or would be too costly in higher eukaryotes. PMID:26935104

A Genetic Animal Model of Alcoholism for Screening Medications to Treat Addiction

Science.gov (United States)

Bell, Richard L.; Hauser, Sheketha; Rodd, Zachary A.; Liang, Tiebing; Sari, Youssef; McClintick, Jeanette; Rahman, Shafiqur; Engleman, Eric A.

2016-01-01

The purpose of this review is to present up-to-date pharmacological, genetic and behavioral findings from the alcohol-preferring P rat and summarize similar past work. Behaviorally, the focus will be on how the P rat meets criteria put forth for a valid animal model of alcoholism with a highlight on its use as an animal model of polysubstance abuse, including alcohol, nicotine and psychostimulants. Pharmacologically and genetically, the focus will be on the neurotransmitter and neuropeptide systems that have received the most attention: cholinergic, dopaminergic, GABAergic, glutamatergic, serotonergic, noradrenergic, corticotrophin releasing hormone, opioid, and neuropeptide Y. Herein we sought to place the P rat’s behavioral and neurochemical phenotypes, and to some extent its genotype, in the context of the clinical literature. After reviewing the findings thus far, this paper discusses future directions for expanding the use of this genetic animal model of alcoholism to identify molecular targets for treating drug addiction in general. PMID:27055615

Genetic enhancement of macroautophagy in vertebrate models of neurodegenerative diseases.

Science.gov (United States)

Ejlerskov, Patrick; Ashkenazi, Avraham; Rubinsztein, David C

2018-04-03

Most of the neurodegenerative diseases that afflict humans manifest with the intraneuronal accumulation of toxic proteins that are aggregate-prone. Extensive data in cell and neuronal models support the concept that such proteins, like mutant huntingtin or alpha-synuclein, are substrates for macroautophagy (hereafter autophagy). Furthermore, autophagy-inducing compounds lower the levels of such proteins and ameliorate their toxicity in diverse animal models of neurodegenerative diseases. However, most of these compounds also have autophagy-independent effects and it is important to understand if similar benefits are seen with genetic strategies that upregulate autophagy, as this strengthens the validity of this strategy in such diseases. Here we review studies in vertebrate models using genetic manipulations of core autophagy genes and describe how these improve pathology and neurodegeneration, supporting the validity of autophagy upregulation as a target for certain neurodegenerative diseases. Copyright © 2018 Elsevier Inc. All rights reserved.

The genetic basis of local adaptation for pathogenic fungi in agricultural ecosystems.

Science.gov (United States)

Croll, Daniel; McDonald, Bruce A

2017-04-01

Local adaptation plays a key role in the evolutionary trajectory of host-pathogen interactions. However, the genetic architecture of local adaptation in host-pathogen systems is poorly understood. Fungal plant pathogens in agricultural ecosystems provide highly tractable models to quantify phenotypes and map traits to corresponding genomic loci. The outcome of crop-pathogen interactions is thought to be governed largely by gene-for-gene interactions. However, recent studies showed that virulence can be governed by quantitative trait loci and that many abiotic factors contribute to the outcome of the interaction. After introducing concepts of local adaptation and presenting examples from wild plant pathosystems, we focus this review on a major pathogen of wheat, Zymoseptoria tritici, to show how a multitude of traits can affect local adaptation. Zymoseptoria tritici adapted to different thermal environments across its distribution range, indicating that thermal adaptation may limit effective dispersal to different climates. The application of fungicides led to the rapid evolution of multiple, independent resistant populations. The degree of colony melanization showed strong pleiotropic effects with other traits, including trade-offs with colony growth rates and fungicide sensitivity. The success of the pathogen on its host can be assessed quantitatively by counting pathogen reproductive structures and measuring host damage based on necrotic lesions. Interestingly, these two traits can be weakly correlated and depend both on host and pathogen genotypes. Quantitative trait mapping studies showed that the genetic architecture of locally adapted traits varies from single loci with large effects to many loci with small individual effects. We discuss how local adaptation could hinder or accelerate the development of epidemics in agricultural ecosystems. © 2016 John Wiley & Sons Ltd.

Application of hierarchical genetic models to Raven and WAIS subtests: a Dutch twin study.

Science.gov (United States)

Rijsdijk, Frühling V; Vernon, P A; Boomsma, Dorret I

2002-05-01

Hierarchical models of intelligence are highly informative and widely accepted. Application of these models to twin data, however, is sparse. This paper addresses the question of how a genetic hierarchical model fits the Wechsler Adult Intelligence Scale (WAIS) subtests and the Raven Standard Progressive test score, collected in 194 18-year-old Dutch twin pairs. We investigated whether first-order group factors possess genetic and environmental variance independent of the higher-order general factor and whether the hierarchical structure is significant for all sources of variance. A hierarchical model with the 3 Cohen group-factors (verbal comprehension, perceptual organisation and freedom-from-distractibility) and a higher-order g factor showed the best fit to the phenotypic data and to additive genetic influences (A), whereas the unique environmental source of variance (E) could be modeled by a single general factor and specifics. There was no evidence for common environmental influences. The covariation among the WAIS group factors and the covariation between the group factors and the Raven is predominantly influenced by a second-order genetic factor and strongly support the notion of a biological basis of g.

Linear Mixed Models in Statistical Genetics

NARCIS (Netherlands)

R. de Vlaming (Ronald)

2017-01-01

markdownabstractOne of the goals of statistical genetics is to elucidate the genetic architecture of phenotypes (i.e., observable individual characteristics) that are affected by many genetic variants (e.g., single-nucleotide polymorphisms; SNPs). A particular aim is to identify specific SNPs that

Models for genetic evaluations of claw health traits in Spanish dairy cattle.

Science.gov (United States)

Pérez-Cabal, M A; Charfeddine, N

2015-11-01

Genetic parameters of 7 claw health traits from Spanish dairy cattle were estimated and the predictive ability of linear and ordinal threshold models were compared and assessed. This study included data on interdigital and digital dermatitis (DE), sole ulcer (SU), white line disease (WL), interdigital hyperplasia (IH), interdigital phlegmon (IP), and chronic laminitis (CL) collected between July 2012 and June 2013 from 834 dairy herds visited by 21 trained trimmers. An overall claw disorder (OCD) was also considered an indicator the absence or the presence of at least 1 of the 6 disorders. Claw health traits were scored as categorical traits with 3 degrees of severity (nonaffected, mild, and severe disorder). Genetic parameters were estimated by fitting both a standard linear model and an ordinal threshold animal model. Around 21% of cows had at least 1 claw disorder, and the most frequent disorders were SU, DE, WL, and CL. Heritabilities of claw disorders estimated with a linear model ranged from 0.01 (IP) to 0.05 (OCD), whereas estimates from the ordinal threshold models ranged from 0.06 to 0.39 (for IP and IH, respectively). Repeatabilities of claw health estimated with the linear model varied from 0.03 to 0.18 and estimates with the ordinal threshold model ranged from 0.33 to 0.69. The global trait OCD was correlated with all disorders, except for IH and IP when the linear model was fitted. Two different genetic backgrounds of claw disorders were found. Digital dermatitis showed positive correlations with IH and IP, whereas SU was positively correlated with WL and CL. The predictive ability of the models was assessed using mean squared error and Pearson correlation between the real observation and the corresponding prediction using cross-validation. Regardless of the claw health status, the linear model led to smaller mean squared error. However, differences in predictive ability were found when predicting nonaffected and affected animals. For most traits

Evolution of the archaeal and mammalian information processing systems: towards an archaeal model for human disease.

Science.gov (United States)

Lyu, Zhe; Whitman, William B

2017-01-01

Current evolutionary models suggest that Eukaryotes originated from within Archaea instead of being a sister lineage. To test this model of ancient evolution, we review recent studies and compare the three major information processing subsystems of replication, transcription and translation in the Archaea and Eukaryotes. Our hypothesis is that if the Eukaryotes arose within the archaeal radiation, their information processing systems will appear to be one of kind and not wholly original. Within the Eukaryotes, the mammalian or human systems are emphasized because of their importance in understanding health. Biochemical as well as genetic studies provide strong evidence for the functional similarity of archaeal homologs to the mammalian information processing system and their dissimilarity to the bacterial systems. In many independent instances, a simple archaeal system is functionally equivalent to more elaborate eukaryotic homologs, suggesting that evolution of complexity is likely an central feature of the eukaryotic information processing system. Because fewer components are often involved, biochemical characterizations of the archaeal systems are often easier to interpret. Similarly, the archaeal cell provides a genetically and metabolically simpler background, enabling convenient studies on the complex information processing system. Therefore, Archaea could serve as a parsimonious and tractable host for studying human diseases that arise in the information processing systems.

On modeling animal movements using Brownian motion with measurement error.

Science.gov (United States)

Pozdnyakov, Vladimir; Meyer, Thomas; Wang, Yu-Bo; Yan, Jun

2014-02-01

Modeling animal movements with Brownian motion (or more generally by a Gaussian process) has a long tradition in ecological studies. The recent Brownian bridge movement model (BBMM), which incorporates measurement errors, has been quickly adopted by ecologists because of its simplicity and tractability. We discuss some nontrivial properties of the discrete-time stochastic process that results from observing a Brownian motion with added normal noise at discrete times. In particular, we demonstrate that the observed sequence of random variables is not Markov. Consequently the expected occupation time between two successively observed locations does not depend on just those two observations; the whole path must be taken into account. Nonetheless, the exact likelihood function of the observed time series remains tractable; it requires only sparse matrix computations. The likelihood-based estimation procedure is described in detail and compared to the BBMM estimation.

Setaria viridis as a model system to advance millet genetics and genomics

Directory of Open Access Journals (Sweden)

Pu Huang

2016-11-01

Full Text Available Millet is a common name for a group of polyphyletic, small-seeded cereal crops that include pearl, finger and foxtail millet. Millet species are an important source of calories for many societies, often in developing countries. Compared to major cereal crops such as rice and maize, millets are generally better adapted to dry and hot environments. Despite their food security value, the genetic architecture of agronomically important traits in millets, including both morphological traits and climate resilience remains poorly studied. These complex traits have been challenging to dissect in large part because of the lack of sufficient genetic tools and resources. In this article, we review the phylogenetic relationship among various millet species and discuss the value of a genetic model system for millet research. We propose that a broader adoption of green foxtail (Setaria viridis as a model system for millets could greatly accelerate the pace of gene discovery in the millets, and summarize available and emerging resources in S. viridis and its domesticated relative S. italica. These resources have value in forward genetics, reverse genetics and high throughput phenotyping. We describe methods and strategies to best utilize these resources to facilitate the genetic dissection of complex traits. We envision that coupling cutting-edge technologies and the use of S. viridis for gene discovery will accelerate genetic research in millets in general. This will enable strategies and provide opportunities to increase productivity, especially in the semi-arid tropics of Asia and Africa where millets are staple food crop.

Setaria viridis as a Model System to Advance Millet Genetics and Genomics.

Science.gov (United States)

Huang, Pu; Shyu, Christine; Coelho, Carla P; Cao, Yingying; Brutnell, Thomas P

2016-01-01

Millet is a common name for a group of polyphyletic, small-seeded cereal crops that include pearl, finger and foxtail millet. Millet species are an important source of calories for many societies, often in developing countries. Compared to major cereal crops such as rice and maize, millets are generally better adapted to dry and hot environments. Despite their food security value, the genetic architecture of agronomically important traits in millets, including both morphological traits and climate resilience remains poorly studied. These complex traits have been challenging to dissect in large part because of the lack of sufficient genetic tools and resources. In this article, we review the phylogenetic relationship among various millet species and discuss the value of a genetic model system for millet research. We propose that a broader adoption of green foxtail ( Setaria viridis ) as a model system for millets could greatly accelerate the pace of gene discovery in the millets, and summarize available and emerging resources in S. viridis and its domesticated relative S. italica . These resources have value in forward genetics, reverse genetics and high throughput phenotyping. We describe methods and strategies to best utilize these resources to facilitate the genetic dissection of complex traits. We envision that coupling cutting-edge technologies and the use of S. viridis for gene discovery will accelerate genetic research in millets in general. This will enable strategies and provide opportunities to increase productivity, especially in the semi-arid tropics of Asia and Africa where millets are staple food crops.

Setaria viridis as a Model System to Advance Millet Genetics and Genomics

Science.gov (United States)

Huang, Pu; Shyu, Christine; Coelho, Carla P.; Cao, Yingying; Brutnell, Thomas P.

2016-01-01

Millet is a common name for a group of polyphyletic, small-seeded cereal crops that include pearl, finger and foxtail millet. Millet species are an important source of calories for many societies, often in developing countries. Compared to major cereal crops such as rice and maize, millets are generally better adapted to dry and hot environments. Despite their food security value, the genetic architecture of agronomically important traits in millets, including both morphological traits and climate resilience remains poorly studied. These complex traits have been challenging to dissect in large part because of the lack of sufficient genetic tools and resources. In this article, we review the phylogenetic relationship among various millet species and discuss the value of a genetic model system for millet research. We propose that a broader adoption of green foxtail (Setaria viridis) as a model system for millets could greatly accelerate the pace of gene discovery in the millets, and summarize available and emerging resources in S. viridis and its domesticated relative S. italica. These resources have value in forward genetics, reverse genetics and high throughput phenotyping. We describe methods and strategies to best utilize these resources to facilitate the genetic dissection of complex traits. We envision that coupling cutting-edge technologies and the use of S. viridis for gene discovery will accelerate genetic research in millets in general. This will enable strategies and provide opportunities to increase productivity, especially in the semi-arid tropics of Asia and Africa where millets are staple food crops. PMID:27965689

Recent developments in computer modeling add ecological realism to landscape genetics

Science.gov (United States)

Background / Question / Methods A factor limiting the rate of progress in landscape genetics has been the shortage of spatial models capable of linking life history attributes such as dispersal behavior to complex dynamic landscape features. The recent development of new models...

A Unifying Model for the Analysis of Phenotypic, Genetic and Geographic Data

DEFF Research Database (Denmark)

Guillot, Gilles; Rena, Sabrina; Ledevin, Ronan

2012-01-01

Recognition of evolutionary units (species, populations) requires integrating several kinds of data such as genetic or phenotypic markers or spatial information, in order to get a comprehensive view concerning the dierentiation of the units. We propose a statistical model with a double original...... advantage: (i) it incorporates information about the spatial distribution of the samples, with the aim to increase inference power and to relate more explicitly observed patterns to geography; and (ii) it allows one to analyze genetic and phenotypic data within a unied model and inference framework, thus...... an intricate case of inter- and intra-species dierentiation based on an original data-set of georeferenced genetic and morphometric markers obtained on Myodes voles from Sweden. A computer program is made available as an extension of the R package Geneland....

Exploring the tractability of the capped hose model

NARCIS (Netherlands)

Bosman, T. (Thomas); N.K. Olver (Neil)

2017-01-01

textabstractRobust network design concerns the design of networks to support uncertain or varying traffic patterns. An especially important case is the VPN problem, where the total traffic emanating from any node is bounded, but there are no further constraints on the traffic pattern. Recently,

Genetic Algorithms for a Parameter Estimation of a Fermentation Process Model: A Comparison

Directory of Open Access Journals (Sweden)

Olympia Roeva

2005-12-01

Full Text Available In this paper the problem of a parameter estimation using genetic algorithms is examined. A case study considering the estimation of 6 parameters of a nonlinear dynamic model of E. coli fermentation is presented as a test problem. The parameter estimation problem is stated as a nonlinear programming problem subject to nonlinear differential-algebraic constraints. This problem is known to be frequently ill-conditioned and multimodal. Thus, traditional (gradient-based local optimization methods fail to arrive satisfied solutions. To overcome their limitations, the use of different genetic algorithms as stochastic global optimization methods is explored. These algorithms are proved to be very suitable for the optimization of highly non-linear problems with many variables. Genetic algorithms can guarantee global optimality and robustness. These facts make them advantageous in use for parameter identification of fermentation models. A comparison between simple, modified and multi-population genetic algorithms is presented. The best result is obtained using the modified genetic algorithm. The considered algorithms converged very closely to the cost value but the modified algorithm is in times faster than other two.

Amniotic Fluid Stem Cells: A Novel Source for Modeling of Human Genetic Diseases

Directory of Open Access Journals (Sweden)

Ivana Antonucci

2016-04-01

Full Text Available In recent years, great interest has been devoted to the use of Induced Pluripotent Stem cells (iPS for modeling of human genetic diseases, due to the possibility of reprogramming somatic cells of affected patients into pluripotent cells, enabling differentiation into several cell types, and allowing investigations into the molecular mechanisms of the disease. However, the protocol of iPS generation still suffers from technical limitations, showing low efficiency, being expensive and time consuming. Amniotic Fluid Stem cells (AFS represent a potential alternative novel source of stem cells for modeling of human genetic diseases. In fact, by means of prenatal diagnosis, a number of fetuses affected by chromosomal or Mendelian diseases can be identified, and the amniotic fluid collected for genetic testing can be used, after diagnosis, for the isolation, culture and differentiation of AFS cells. This can provide a useful stem cell model for the investigation of the molecular basis of the diagnosed disease without the necessity of producing iPS, since AFS cells show some features of pluripotency and are able to differentiate in cells derived from all three germ layers “in vitro”. In this article, we describe the potential benefits provided by using AFS cells in the modeling of human genetic diseases.

Genetic mouse models of brain ageing and Alzheimer's disease.

Science.gov (United States)

Bilkei-Gorzo, Andras

2014-05-01

Progression of brain ageing is influenced by a complex interaction of genetic and environmental factors. Analysis of genetically modified animals with uniform genetic backgrounds in a standardised, controlled environment enables the dissection of critical determinants of brain ageing on a molecular level. Human and animal studies suggest that increased load of damaged macromolecules, efficacy of DNA maintenance, mitochondrial activity, and cellular stress defences are critical determinants of brain ageing. Surprisingly, mouse lines with genetic impairment of anti-oxidative capacity generally did not show enhanced cognitive ageing but rather an increased sensitivity to oxidative challenge. Mouse lines with impaired mitochondrial activity had critically short life spans or severe and rapidly progressing neurodegeneration. Strains with impaired clearance in damaged macromolecules or defects in the regulation of cellular stress defences showed alterations in the onset and progression of cognitive decline. Importantly, reduced insulin/insulin-like growth factor signalling generally increased life span but impaired cognitive functions revealing a complex interaction between ageing of the brain and of the body. Brain ageing is accompanied by an increased risk of developing Alzheimer's disease. Transgenic mouse models expressing high levels of mutant human amyloid precursor protein showed a number of symptoms and pathophysiological processes typical for early phase of Alzheimer's disease. Generally, therapeutic strategies effective against Alzheimer's disease in humans were also active in the Tg2576, APP23, APP/PS1 and 5xFAD lines, but a large number of false positive findings were also reported. The 3xtg AD model likely has the highest face and construct validity but further studies are needed. Copyright © 2013 Elsevier Inc. All rights reserved.

Identifying genetic signatures of selection in a non-model species, alpine gentian (Gentiana nivalis L.), using a landscape genetic approach

DEFF Research Database (Denmark)

Bothwell, H.; Bisbing, S.; Therkildsen, Nina Overgaard

2013-01-01

It is generally accepted that most plant populations are locally adapted. Yet, understanding how environmental forces give rise to adaptive genetic variation is a challenge in conservation genetics and crucial to the preservation of species under rapidly changing climatic conditions. Environmental...... loci, we compared outlier locus detection methods with a recently-developed landscape genetic approach. We analyzed 157 loci from samples of the alpine herb Gentiana nivalis collected across the European Alps. Principle coordinates of neighbor matrices (PCNM), eigenvectors that quantify multi...... variables identified eight more potentially adaptive loci than models run without spatial variables. 3) When compared to outlier detection methods, the landscape genetic approach detected four of the same loci plus 11 additional loci. 4) Temperature, precipitation, and solar radiation were the three major...

Application of genetic algorithm in radio ecological models parameter determination

Energy Technology Data Exchange (ETDEWEB)

Pantelic, G. [Institute of Occupatioanl Health and Radiological Protection ' Dr Dragomir Karajovic' , Belgrade (Serbia)

2006-07-01

The method of genetic algorithms was used to determine the biological half-life of 137 Cs in cow milk after the accident in Chernobyl. Methodologically genetic algorithms are based on the fact that natural processes tend to optimize themselves and therefore this method should be more efficient in providing optimal solutions in the modeling of radio ecological and environmental events. The calculated biological half-life of 137 Cs in milk is (32 {+-} 3) days and transfer coefficient from grass to milk is (0.019 {+-} 0.005). (authors)

Organoid Models of Human and Mouse Ductal Pancreatic Cancer

NARCIS (Netherlands)

Boj, Sylvia F.; Hwang, Chang-Il; Baker, Lindsey A.; Chio, Iok In Christine; Engle, Dannielle D.; Corbo, Vincenzo; Jager, Myrthe; Ponz-Sarvise, Mariano; Tiriac, Herve; Spector, Mona S.; Gracanin, Ana; Oni, Tobiloba; Yu, Kenneth H.; van Boxtel, Ruben; Huch, Meritxell; Rivera, Keith D.; Wilson, John P.; Feigin, Michael E.; Oehlund, Daniel; Handly-Santana, Abram; Ardito-Abraham, Christine M.; Ludwig, Michael; Elyada, Ela; Alagesan, Brinda; Biffi, Giulia; Yordanov, Georgi N.; Delcuze, Bethany; Creighton, Brianna; Wright, Kevin; Park, Youngkyu; Morsink, Folkert H. M.; Molenaar, IQ; Borel Rinkes, Inne H.; Cuppen, Edwin; Hao, Yuan; Jin, Ying; Nijman, Isaac J.; Iacobuzio-Donahue, Christine; Leach, Steven D.; Pappin, Darryl J.; Hammell, Molly; Klimstra, David S.; Basturk, Olca; Hruban, Ralph H.; Offerhaus, George Johan; Vries, Robert G. J.; Clevers, Hans; Tuveson, David A.

2015-01-01

Pancreatic cancer is one of the most lethal malignancies due to its late diagnosis and limited response to treatment. Tractable methods to identify and interrogate pathways involved in pancreatic tumorigenesis are urgently needed. We established organoid models from normal and neoplastic murine and

Long-Term Adult Feline Liver Organoid Cultures for Disease Modeling of Hepatic Steatosis

NARCIS (Netherlands)

Kruitwagen, H.S. (Hedwig S.); Oosterhoff, L.A. (Loes A.); Vernooij, I.G.W.H. (Ingrid G.W.H.); Schrall, I.M. (Ingrid M.); M.E. van Wolferen (Monique); Bannink, F. (Farah); Roesch, C. (Camille); van Uden, L. (Lisa); Molenaar, M.R. (Martijn R.); J.B. Helms (J. Bernd); G.C.M. Grinwis (Guy C.); M.M.A. Verstegen (Monique); L.J.W. van der Laan (Luc); M. Huch (Meritxell); N. Geijsen (Niels); R.G.J. Vries (Robert); H.C. Clevers (Hans); J. Rothuizen (J.); B.A. Schotanus (Baukje A.); C. Penning (Corine); B. Spee (B.)

2017-01-01

textabstractHepatic steatosis is a highly prevalent liver disease, yet research is hampered by the lack of tractable cellular and animal models. Steatosis also occurs in cats, where it can cause severe hepatic failure. Previous studies demonstrate the potential of liver organoids for modeling

Simulated evolution applied to study the genetic code optimality using a model of codon reassignments.

Science.gov (United States)

Santos, José; Monteagudo, Angel

2011-02-21

As the canonical code is not universal, different theories about its origin and organization have appeared. The optimization or level of adaptation of the canonical genetic code was measured taking into account the harmful consequences resulting from point mutations leading to the replacement of one amino acid for another. There are two basic theories to measure the level of optimization: the statistical approach, which compares the canonical genetic code with many randomly generated alternative ones, and the engineering approach, which compares the canonical code with the best possible alternative. Here we used a genetic algorithm to search for better adapted hypothetical codes and as a method to guess the difficulty in finding such alternative codes, allowing to clearly situate the canonical code in the fitness landscape. This novel proposal of the use of evolutionary computing provides a new perspective in the open debate between the use of the statistical approach, which postulates that the genetic code conserves amino acid properties far better than expected from a random code, and the engineering approach, which tends to indicate that the canonical genetic code is still far from optimal. We used two models of hypothetical codes: one that reflects the known examples of codon reassignment and the model most used in the two approaches which reflects the current genetic code translation table. Although the standard code is far from a possible optimum considering both models, when the more realistic model of the codon reassignments was used, the evolutionary algorithm had more difficulty to overcome the efficiency of the canonical genetic code. Simulated evolution clearly reveals that the canonical genetic code is far from optimal regarding its optimization. Nevertheless, the efficiency of the canonical code increases when mistranslations are taken into account with the two models, as indicated by the fact that the best possible codes show the patterns of the

Simulated evolution applied to study the genetic code optimality using a model of codon reassignments

Directory of Open Access Journals (Sweden)

Monteagudo Ángel

2011-02-01

Full Text Available Abstract Background As the canonical code is not universal, different theories about its origin and organization have appeared. The optimization or level of adaptation of the canonical genetic code was measured taking into account the harmful consequences resulting from point mutations leading to the replacement of one amino acid for another. There are two basic theories to measure the level of optimization: the statistical approach, which compares the canonical genetic code with many randomly generated alternative ones, and the engineering approach, which compares the canonical code with the best possible alternative. Results Here we used a genetic algorithm to search for better adapted hypothetical codes and as a method to guess the difficulty in finding such alternative codes, allowing to clearly situate the canonical code in the fitness landscape. This novel proposal of the use of evolutionary computing provides a new perspective in the open debate between the use of the statistical approach, which postulates that the genetic code conserves amino acid properties far better than expected from a random code, and the engineering approach, which tends to indicate that the canonical genetic code is still far from optimal. We used two models of hypothetical codes: one that reflects the known examples of codon reassignment and the model most used in the two approaches which reflects the current genetic code translation table. Although the standard code is far from a possible optimum considering both models, when the more realistic model of the codon reassignments was used, the evolutionary algorithm had more difficulty to overcome the efficiency of the canonical genetic code. Conclusions Simulated evolution clearly reveals that the canonical genetic code is far from optimal regarding its optimization. Nevertheless, the efficiency of the canonical code increases when mistranslations are taken into account with the two models, as indicated by the

A Model for Understanding the Genetic Basis for Disparity in Prostate Cancer Risk

Science.gov (United States)

2017-10-01

AWARD NUMBER: W81XWH-15-1-0529 TITLE: A Model for Understanding the Genetic Basis for Disparity in Prostate Cancer Risk PRINCIPAL INVESTIGATOR...AND SUBTITLE A Model for Understanding the Genetic Basis for Disparity in Prostate Cancer Risk 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-15-1...STATEMENT Approved for Public Release; Distribution Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Prostate cancer is the most commonly diagnosed cancer in

Identifying genetic variants that affect viability in large cohorts.

Directory of Open Access Journals (Sweden)

Hakhamanesh Mostafavi

2017-09-01

Full Text Available A number of open questions in human evolutionary genetics would become tractable if we were able to directly measure evolutionary fitness. As a step towards this goal, we developed a method to examine whether individual genetic variants, or sets of genetic variants, currently influence viability. The approach consists in testing whether the frequency of an allele varies across ages, accounting for variation in ancestry. We applied it to the Genetic Epidemiology Research on Adult Health and Aging (GERA cohort and to the parents of participants in the UK Biobank. Across the genome, we found only a few common variants with large effects on age-specific mortality: tagging the APOE ε4 allele and near CHRNA3. These results suggest that when large, even late-onset effects are kept at low frequency by purifying selection. Testing viability effects of sets of genetic variants that jointly influence 1 of 42 traits, we detected a number of strong signals. In participants of the UK Biobank of British ancestry, we found that variants that delay puberty timing are associated with a longer parental life span (P~6.2 × 10-6 for fathers and P~2.0 × 10-3 for mothers, consistent with epidemiological studies. Similarly, variants associated with later age at first birth are associated with a longer maternal life span (P~1.4 × 10-3. Signals are also observed for variants influencing cholesterol levels, risk of coronary artery disease (CAD, body mass index, as well as risk of asthma. These signals exhibit consistent effects in the GERA cohort and among participants of the UK Biobank of non-British ancestry. We also found marked differences between males and females, most notably at the CHRNA3 locus, and variants associated with risk of CAD and cholesterol levels. Beyond our findings, the analysis serves as a proof of principle for how upcoming biomedical data sets can be used to learn about selection effects in contemporary humans.

Chemical Genomics and Emerging DNA Technologies in the Identification of Drug Mechanisms and Drug Targets

DEFF Research Database (Denmark)

Olsen, Louise Cathrine Braun; Færgeman, Nils J.

2012-01-01

and validate therapeutic targets and to discover drug candidates for rapidly and effectively generating new interventions for human diseases. The recent emergence of genomic technologies and their application on genetically tractable model organisms like Drosophila melanogaster,Caenorhabditis elegans...... critical roles in the genomic age of biological research and drug discovery. In the present review we discuss how simple biological model organisms can be used as screening platforms in combination with emerging genomic technologies to advance the identification of potential drugs and their molecular...

A genetic algorithm for optimizing multi-pole Debye models of tissue dielectric properties

International Nuclear Information System (INIS)

Clegg, J; Robinson, M P

2012-01-01

Models of tissue dielectric properties (permittivity and conductivity) enable the interactions of tissues and electromagnetic fields to be simulated, which has many useful applications in microwave imaging, radio propagation, and non-ionizing radiation dosimetry. Parametric formulae are available, based on a multi-pole model of tissue dispersions, but although they give the dielectric properties over a wide frequency range, they do not convert easily to the time domain. An alternative is the multi-pole Debye model which works well in both time and frequency domains. Genetic algorithms are an evolutionary approach to optimization, and we found that this technique was effective at finding the best values of the multi-Debye parameters. Our genetic algorithm optimized these parameters to fit to either a Cole–Cole model or to measured data, and worked well over wide or narrow frequency ranges. Over 10 Hz–10 GHz the best fits for muscle, fat or bone were each found for ten dispersions or poles in the multi-Debye model. The genetic algorithm is a fast and effective method of developing tissue models that compares favourably with alternatives such as the rational polynomial fit. (paper)

Modeling cellular networks in fading environments with dominant specular components

KAUST Repository

Alammouri, Ahmad; Elsawy, Hesham; Salem, Ahmed Sultan; Di Renzo, Marco; Alouini, Mohamed-Slim

2016-01-01

to the Nakagami-m fading in some special cases. However, neither the Rayleigh nor the Nakagami-m accounts for dominant specular components (DSCs) which may appear in realistic fading channels. In this paper, we present a tractable model for cellular networks

Genetic Evaluation and Ranking of Different Animal Models Using ...

African Journals Online (AJOL)

An animal model utilizes all relationships available in a given data set. Estimates for variance components for additive direct, additive maternal, maternal environmental and direct environmental effects, and their covariances between direct and maternal genetic effects for post weaning growth traits have been obtained with ...

Genetic modelling in schizophrenia according to HLA typing.

Science.gov (United States)

Smeraldi, E; Macciardi, F; Gasperini, M; Orsini, A; Bellodi, L; Fabio, G; Morabito, A

1986-09-01

Studying families of schizophrenic patients, we observed that the risk of developing the overt form of the illness could be enhanced by some factors. Among these various factors we focused our attention on a biological variable, namely the presence or the absence of particular HLA antigens: partitioning our schizophrenic patients according to their HLA structure (i.e. those with HLA-A1 or CRAG-A1 antigens and those with HLA-non-CRAG-A1 antigens, respectively), revealed different illness distribution in the two groups. From a genetic point of view, this finding suggests the presence of heterogeneity in the hypothetical liability system related to schizophrenia and we evaluated the heterogeneity hypothesis by applying alternative genetic models to our data, trying to detect more biologically homogeneous subgroups of the disease.

Genetic algorithm based optimization of advanced solar cell designs modeled in Silvaco AtlasTM

OpenAIRE

Utsler, James

2006-01-01

A genetic algorithm was used to optimize the power output of multi-junction solar cells. Solar cell operation was modeled using the Silvaco ATLASTM software. The output of the ATLASTM simulation runs served as the input to the genetic algorithm. The genetic algorithm was run as a diffusing computation on a network of eighteen dual processor nodes. Results showed that the genetic algorithm produced better power output optimizations when compared with the results obtained using the hill cli...

Forecasting Shaharchay River Flow in Lake Urmia Basin using Genetic Programming and M5 Model Tree

Directory of Open Access Journals (Sweden)

S. Samadianfard

2017-01-01

Full Text Available Introduction: Precise prediction of river flows is the key factor for proper planning and management of water resources. Thus, obtaining the reliable methods for predicting river flows has great importance in water resource engineering. In the recent years, applications of intelligent methods such as artificial neural networks, fuzzy systems and genetic programming in water science and engineering have been grown extensively. These mentioned methods are able to model nonlinear process of river flows without any need to geometric properties. A huge number of studies have been reported in the field of using intelligent methods in water resource engineering. For example, Noorani and Salehi (23 presented a model for predicting runoff in Lighvan basin using adaptive neuro-fuzzy network and compared the performance of it with neural network and fuzzy inference methods in east Azerbaijan, Iran. Nabizadeh et al. (21 used fuzzy inference system and adaptive neuro-fuzzy inference system in order to predict river flow in Lighvan river. Khalili et al. (13 proposed a BL-ARCH method for prediction of flows in Shaharchay River in Urmia. Khu et al. (16 used genetic programming for runoff prediction in Orgeval catchment in France. Firat and Gungor (11 evaluated the fuzzy-neural model for predicting Mendes river flow in Turkey. The goal of present study is comparing the performance of genetic programming and M5 model trees for prediction of Shaharchay river flow in the basin of Lake Urmia and obtaining a comprehensive insight of their abilities. Materials and Methods: Shaharchay river as a main source of providing drinking water of Urmia city and agricultural needs of surrounding lands and finally one of the main input sources of Lake Urmia is quite important in the region. For obtaining the predetermined goals of present study, average monthly flows of Shaharchay River in Band hydrometric station has been gathered from 1951 to 2011. Then, two third of mentioned

Application of genetic algorithm in radio ecological models parameter determination

International Nuclear Information System (INIS)

Pantelic, G.

2006-01-01

The method of genetic algorithms was used to determine the biological half-life of 137 Cs in cow milk after the accident in Chernobyl. Methodologically genetic algorithms are based on the fact that natural processes tend to optimize themselves and therefore this method should be more efficient in providing optimal solutions in the modeling of radio ecological and environmental events. The calculated biological half-life of 137 Cs in milk is (32 ± 3) days and transfer coefficient from grass to milk is (0.019 ± 0.005). (authors)

Fluid Dynamic Models for Bhattacharyya-Based Discriminant Analysis.

Science.gov (United States)

Noh, Yung-Kyun; Hamm, Jihun; Park, Frank Chongwoo; Zhang, Byoung-Tak; Lee, Daniel D

2018-01-01

Classical discriminant analysis attempts to discover a low-dimensional subspace where class label information is maximally preserved under projection. Canonical methods for estimating the subspace optimize an information-theoretic criterion that measures the separation between the class-conditional distributions. Unfortunately, direct optimization of the information-theoretic criteria is generally non-convex and intractable in high-dimensional spaces. In this work, we propose a novel, tractable algorithm for discriminant analysis that considers the class-conditional densities as interacting fluids in the high-dimensional embedding space. We use the Bhattacharyya criterion as a potential function that generates forces between the interacting fluids, and derive a computationally tractable method for finding the low-dimensional subspace that optimally constrains the resulting fluid flow. We show that this model properly reduces to the optimal solution for homoscedastic data as well as for heteroscedastic Gaussian distributions with equal means. We also extend this model to discover optimal filters for discriminating Gaussian processes and provide experimental results and comparisons on a number of datasets.

The Mouse Lemur, a Genetic Model Organism for Primate Biology, Behavior, and Health.

Science.gov (United States)

Ezran, Camille; Karanewsky, Caitlin J; Pendleton, Jozeph L; Sholtz, Alex; Krasnow, Maya R; Willick, Jason; Razafindrakoto, Andriamahery; Zohdy, Sarah; Albertelli, Megan A; Krasnow, Mark A

2017-06-01

Systematic genetic studies of a handful of diverse organisms over the past 50 years have transformed our understanding of biology. However, many aspects of primate biology, behavior, and disease are absent or poorly modeled in any of the current genetic model organisms including mice. We surveyed the animal kingdom to find other animals with advantages similar to mice that might better exemplify primate biology, and identified mouse lemurs ( Microcebus spp.) as the outstanding candidate. Mouse lemurs are prosimian primates, roughly half the genetic distance between mice and humans. They are the smallest, fastest developing, and among the most prolific and abundant primates in the world, distributed throughout the island of Madagascar, many in separate breeding populations due to habitat destruction. Their physiology, behavior, and phylogeny have been studied for decades in laboratory colonies in Europe and in field studies in Malagasy rainforests, and a high quality reference genome sequence has recently been completed. To initiate a classical genetic approach, we developed a deep phenotyping protocol and have screened hundreds of laboratory and wild mouse lemurs for interesting phenotypes and begun mapping the underlying mutations, in collaboration with leading mouse lemur biologists. We also seek to establish a mouse lemur gene "knockout" library by sequencing the genomes of thousands of mouse lemurs to identify null alleles in most genes from the large pool of natural genetic variants. As part of this effort, we have begun a citizen science project in which students across Madagascar explore the remarkable biology around their schools, including longitudinal studies of the local mouse lemurs. We hope this work spawns a new model organism and cultivates a deep genetic understanding of primate biology and health. We also hope it establishes a new and ethical method of genetics that bridges biological, behavioral, medical, and conservation disciplines, while

Genetic parameters for racing records in trotters using linear and generalized linear models.

Science.gov (United States)

Suontama, M; van der Werf, J H J; Juga, J; Ojala, M

2012-09-01

Heritability and repeatability and genetic and phenotypic correlations were estimated for trotting race records with linear and generalized linear models using 510,519 records on 17,792 Finnhorses and 513,161 records on 25,536 Standardbred trotters. Heritability and repeatability were estimated for single racing time and earnings traits with linear models, and logarithmic scale was used for racing time and fourth-root scale for earnings to correct for nonnormality. Generalized linear models with a gamma distribution were applied for single racing time and with a multinomial distribution for single earnings traits. In addition, genetic parameters for annual earnings were estimated with linear models on the observed and fourth-root scales. Racing success traits of single placings, winnings, breaking stride, and disqualifications were analyzed using generalized linear models with a binomial distribution. Estimates of heritability were greatest for racing time, which ranged from 0.32 to 0.34. Estimates of heritability were low for single earnings with all distributions, ranging from 0.01 to 0.09. Annual earnings were closer to normal distribution than single earnings. Heritability estimates were moderate for annual earnings on the fourth-root scale, 0.19 for Finnhorses and 0.27 for Standardbred trotters. Heritability estimates for binomial racing success variables ranged from 0.04 to 0.12, being greatest for winnings and least for breaking stride. Genetic correlations among racing traits were high, whereas phenotypic correlations were mainly low to moderate, except correlations between racing time and earnings were high. On the basis of a moderate heritability and moderate to high repeatability for racing time and annual earnings, selection of horses for these traits is effective when based on a few repeated records. Because of high genetic correlations, direct selection for racing time and annual earnings would also result in good genetic response in racing success.

Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction.

Directory of Open Access Journals (Sweden)

Yiming Hu

2017-06-01

Full Text Available Accurate prediction of disease risk based on genetic factors is an important goal in human genetics research and precision medicine. Advanced prediction models will lead to more effective disease prevention and treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome-wide association studies (GWAS in the past decade, accuracy of genetic risk prediction remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. In this work, we introduce PleioPred, a principled framework that leverages pleiotropy and functional annotations in genetic risk prediction for complex diseases. PleioPred uses GWAS summary statistics as its input, and jointly models multiple genetically correlated diseases and a variety of external information including linkage disequilibrium and diverse functional annotations to increase the accuracy of risk prediction. Through comprehensive simulations and real data analyses on Crohn's disease, celiac disease and type-II diabetes, we demonstrate that our approach can substantially increase the accuracy of polygenic risk prediction and risk population stratification, i.e. PleioPred can significantly better separate type-II diabetes patients with early and late onset ages, illustrating its potential clinical application. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.

Genetic models of absence epilepsy: New concepts and insights

NARCIS (Netherlands)

Luijtelaar, E.L.J.M. van; Stein, J.

2017-01-01

The discovery, development and use of genetic rodent models of absence epilepsy have led to a new theory about the origin of absence seizures, which has gained impact within the international epilepsy community. A focal zone has been identified in the perioral region of the somatosensory cortex in

Revised models and genetic parameter estimates for production and ...

African Journals Online (AJOL)

Genetic parameters for production and reproduction traits in the Elsenburg Dormer sheep stud were estimated using records of 11743 lambs born between 1943 and 2002. An animal model with direct and maternal additive, maternal permanent and temporary environmental effects was fitted for traits considered traits of the ...

Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data.

Science.gov (United States)

Fu, Changhe; Deng, Su; Jin, Guangxu; Wang, Xinxin; Yu, Zu-Guo

2017-09-21

Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data. We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high. We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously

Random regret minimization : Exploration of a new choice model for environmental and resource economics

NARCIS (Netherlands)

Thiene, M.; Boeri, M.; Chorus, C.G.

2011-01-01

This paper introduces the discrete choice model-paradigm of Random Regret Minimization (RRM) to the field of environmental and resource economics. The RRM-approach has been very recently developed in the context of travel demand modelling and presents a tractable, regret-based alternative to the

Predicting ecosystem functioning from plant traits: Results from a multi-scale ecophsiological modeling approach

NARCIS (Netherlands)

Wijk, van M.T.

2007-01-01

Ecosystem functioning is the result of processes working at a hierarchy of scales. The representation of these processes in a model that is mathematically tractable and ecologically meaningful is a big challenge. In this paper I describe an individual based model (PLACO¿PLAnt COmpetition) that

Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier.

Science.gov (United States)

Pandey, Daya Shankar; Pan, Indranil; Das, Saptarshi; Leahy, James J; Kwapinski, Witold

2015-03-01

A multi-gene genetic programming technique is proposed as a new method to predict syngas yield production and the lower heating value for municipal solid waste gasification in a fluidized bed gasifier. The study shows that the predicted outputs of the municipal solid waste gasification process are in good agreement with the experimental dataset and also generalise well to validation (untrained) data. Published experimental datasets are used for model training and validation purposes. The results show the effectiveness of the genetic programming technique for solving complex nonlinear regression problems. The multi-gene genetic programming are also compared with a single-gene genetic programming model to show the relative merits and demerits of the technique. This study demonstrates that the genetic programming based data-driven modelling strategy can be a good candidate for developing models for other types of fuels as well. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modelling the genetic risk in age-related macular degeneration.

Directory of Open Access Journals (Sweden)

Felix Grassmann

Full Text Available Late-stage age-related macular degeneration (AMD is a common sight-threatening disease of the central retina affecting approximately 1 in 30 Caucasians. Besides age and smoking, genetic variants from several gene loci have reproducibly been associated with this condition and likely explain a large proportion of disease. Here, we developed a genetic risk score (GRS for AMD based on 13 risk variants from eight gene loci. The model exhibited good discriminative accuracy, area-under-curve (AUC of the receiver-operating characteristic of 0.820, which was confirmed in a cross-validation approach. Noteworthy, younger AMD patients aged below 75 had a significantly higher mean GRS (1.87, 95% CI: 1.69-2.05 than patients aged 75 and above (1.45, 95% CI: 1.36-1.54. Based on five equally sized GRS intervals, we present a risk classification with a relative AMD risk of 64.0 (95% CI: 14.11-1131.96 for individuals in the highest category (GRS 3.44-5.18, 0.5% of the general population compared to subjects with the most common genetic background (GRS -0.05-1.70, 40.2% of general population. The highest GRS category identifies AMD patients with a sensitivity of 7.9% and a specificity of 99.9% when compared to the four lower categories. Modeling a general population around 85 years of age, 87.4% of individuals in the highest GRS category would be expected to develop AMD by that age. In contrast, only 2.2% of individuals in the two lowest GRS categories which represent almost 50% of the general population are expected to manifest AMD. Our findings underscore the large proportion of AMD cases explained by genetics particularly for younger AMD patients. The five-category risk classification could be useful for therapeutic stratification or for diagnostic testing purposes once preventive treatment is available.

Genetic Diseases and Genetic Determinism Models in French Secondary School Biology Textbooks

Science.gov (United States)

Castera, Jeremy; Bruguiere, Catherine; Clement, Pierre

2008-01-01

The presentation of genetic diseases in French secondary school biology textbooks is analysed to determine the major conceptions taught in the field of human genetics. References to genetic diseases, and the processes by which they are explained (monogeny, polygeny, chromosomal anomaly and environmental influence) are studied in recent French…

Behavioral phenotypes of genetic mouse models of autism.

Science.gov (United States)

Kazdoba, T M; Leach, P T; Crawley, J N

2016-01-01

More than a hundred de novo single gene mutations and copy-number variants have been implicated in autism, each occurring in a small subset of cases. Mutant mouse models with syntenic mutations offer research tools to gain an understanding of the role of each gene in modulating biological and behavioral phenotypes relevant to autism. Knockout, knockin and transgenic mice incorporating risk gene mutations detected in autism spectrum disorder and comorbid neurodevelopmental disorders are now widely available. At present, autism spectrum disorder is diagnosed solely by behavioral criteria. We developed a constellation of mouse behavioral assays designed to maximize face validity to the types of social deficits and repetitive behaviors that are central to an autism diagnosis. Mouse behavioral assays for associated symptoms of autism, which include cognitive inflexibility, anxiety, hyperactivity, and unusual reactivity to sensory stimuli, are frequently included in the phenotypic analyses. Over the past 10 years, we and many other laboratories around the world have employed these and additional behavioral tests to phenotype a large number of mutant mouse models of autism. In this review, we highlight mouse models with mutations in genes that have been identified as risk genes for autism, which work through synaptic mechanisms and through the mTOR signaling pathway. Robust, replicated autism-relevant behavioral outcomes in a genetic mouse model lend credence to a causal role for specific gene contributions and downstream biological mechanisms in the etiology of autism. © 2015 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

Something worth dyeing for

DEFF Research Database (Denmark)

Elle, Ida Coordt; Olsen, Louise Cathrine Braun; Pultz, Dennis

2010-01-01

The nematode Caenorhabditis elegans (C. elegans) has during the last decade emerged as an invaluable eukaryotic model organism to understand the metabolic and neuro-endocrine regulation of lipid accumulation. The fundamental pathways of food intake, digestion, metabolism, and signalling...... are evolutionary conserved between mammals and worms making C. elegans a genetically and metabolically extremely tractable model to decipher new regulatory mechanisms of lipid storage and to understand how nutritional and genetic perturbations can lead to obesity and other metabolic diseases. Besides providing...... an overview of the most important regulatory mechanisms of lipid accumulation in C. elegans, we also critically assess the current methodologies to monitor lipid storage and content as various methods differ in their applicability, consistency, and simplicity....

Teaching genetics using hands-on models, problem solving, and inquiry-based methods

Science.gov (United States)

Hoppe, Stephanie Ann

Teaching genetics can be challenging because of the difficulty of the content and misconceptions students might hold. This thesis focused on using hands-on model activities, problem solving, and inquiry-based teaching/learning methods in order to increase student understanding in an introductory biology class in the area of genetics. Various activities using these three methods were implemented into the classes to address any misconceptions and increase student learning of the difficult concepts. The activities that were implemented were shown to be successful based on pre-post assessment score comparison. The students were assessed on the subjects of inheritance patterns, meiosis, and protein synthesis and demonstrated growth in all of the areas. It was found that hands-on models, problem solving, and inquiry-based activities were more successful in learning concepts in genetics and the students were more engaged than tradition styles of lecture.

Analysis of a genetically structured variance heterogeneity model using the Box-Cox transformation.

Science.gov (United States)

Yang, Ye; Christensen, Ole F; Sorensen, Daniel

2011-02-01

Over recent years, statistical support for the presence of genetic factors operating at the level of the environmental variance has come from fitting a genetically structured heterogeneous variance model to field or experimental data in various species. Misleading results may arise due to skewness of the marginal distribution of the data. To investigate how the scale of measurement affects inferences, the genetically structured heterogeneous variance model is extended to accommodate the family of Box-Cox transformations. Litter size data in rabbits and pigs that had previously been analysed in the untransformed scale were reanalysed in a scale equal to the mode of the marginal posterior distribution of the Box-Cox parameter. In the rabbit data, the statistical evidence for a genetic component at the level of the environmental variance is considerably weaker than that resulting from an analysis in the original metric. In the pig data, the statistical evidence is stronger, but the coefficient of correlation between additive genetic effects affecting mean and variance changes sign, compared to the results in the untransformed scale. The study confirms that inferences on variances can be strongly affected by the presence of asymmetry in the distribution of data. We recommend that to avoid one important source of spurious inferences, future work seeking support for a genetic component acting on environmental variation using a parametric approach based on normality assumptions confirms that these are met.

New insights into the endophenotypic status of cognition in bipolar disorder: genetic modelling study of twins and siblings.

Science.gov (United States)

Georgiades, Anna; Rijsdijk, Fruhling; Kane, Fergus; Rebollo-Mesa, Irene; Kalidindi, Sridevi; Schulze, Katja K; Stahl, Daniel; Walshe, Muriel; Sahakian, Barbara J; McDonald, Colm; Hall, Mei-Hua; Murray, Robin M; Kravariti, Eugenia

2016-06-01

Twin studies have lacked statistical power to apply advanced genetic modelling techniques to the search for cognitive endophenotypes for bipolar disorder. To quantify the shared genetic variability between bipolar disorder and cognitive measures. Structural equation modelling was performed on cognitive data collected from 331 twins/siblings of varying genetic relatedness, disease status and concordance for bipolar disorder. Using a parsimonious AE model, verbal episodic and spatial working memory showed statistically significant genetic correlations with bipolar disorder (rg = |0.23|-|0.27|), which lost statistical significance after covarying for affective symptoms. Using an ACE model, IQ and visual-spatial learning showed statistically significant genetic correlations with bipolar disorder (rg = |0.51|-|1.00|), which remained significant after covarying for affective symptoms. Verbal episodic and spatial working memory capture a modest fraction of the bipolar diathesis. IQ and visual-spatial learning may tap into genetic substrates of non-affective symptomatology in bipolar disorder. © The Royal College of Psychiatrists 2016.

Genetic parameters for direct and maternal calving ease in Walloon dairy cattle based on linear and threshold models.

Science.gov (United States)

Vanderick, S; Troch, T; Gillon, A; Glorieux, G; Gengler, N

2014-12-01

Calving ease scores from Holstein dairy cattle in the Walloon Region of Belgium were analysed using univariate linear and threshold animal models. Variance components and derived genetic parameters were estimated from a data set including 33,155 calving records. Included in the models were season, herd and sex of calf × age of dam classes × group of calvings interaction as fixed effects, herd × year of calving, maternal permanent environment and animal direct and maternal additive genetic as random effects. Models were fitted with the genetic correlation between direct and maternal additive genetic effects either estimated or constrained to zero. Direct heritability for calving ease was approximately 8% with linear models and approximately 12% with threshold models. Maternal heritabilities were approximately 2 and 4%, respectively. Genetic correlation between direct and maternal additive effects was found to be not significantly different from zero. Models were compared in terms of goodness of fit and predictive ability. Criteria of comparison such as mean squared error, correlation between observed and predicted calving ease scores as well as between estimated breeding values were estimated from 85,118 calving records. The results provided few differences between linear and threshold models even though correlations between estimated breeding values from subsets of data for sires with progeny from linear model were 17 and 23% greater for direct and maternal genetic effects, respectively, than from threshold model. For the purpose of genetic evaluation for calving ease in Walloon Holstein dairy cattle, the linear animal model without covariance between direct and maternal additive effects was found to be the best choice. © 2014 Blackwell Verlag GmbH.

Genetic evaluation of calf and heifer survival in Iranian Holstein cattle using linear and threshold models.

Science.gov (United States)

Forutan, M; Ansari Mahyari, S; Sargolzaei, M

2015-02-01

Calf and heifer survival are important traits in dairy cattle affecting profitability. This study was carried out to estimate genetic parameters of survival traits in female calves at different age periods, until nearly the first calving. Records of 49,583 female calves born during 1998 and 2009 were considered in five age periods as days 1-30, 31-180, 181-365, 366-760 and full period (day 1-760). Genetic components were estimated based on linear and threshold sire models and linear animal models. The models included both fixed effects (month of birth, dam's parity number, calving ease and twin/single) and random effects (herd-year, genetic effect of sire or animal and residual). Rates of death were 2.21, 3.37, 1.97, 4.14 and 12.4% for the above periods, respectively. Heritability estimates were very low ranging from 0.48 to 3.04, 0.62 to 3.51 and 0.50 to 4.24% for linear sire model, animal model and threshold sire model, respectively. Rank correlations between random effects of sires obtained with linear and threshold sire models and with linear animal and sire models were 0.82-0.95 and 0.61-0.83, respectively. The estimated genetic correlations between the five different periods were moderate and only significant for 31-180 and 181-365 (r(g) = 0.59), 31-180 and 366-760 (r(g) = 0.52), and 181-365 and 366-760 (r(g) = 0.42). The low genetic correlations in current study would suggest that survival at different periods may be affected by the same genes with different expression or by different genes. Even though the additive genetic variations of survival traits were small, it might be possible to improve these traits by traditional or genomic selection. © 2014 Blackwell Verlag GmbH.

Genetic Aspects of Autism Spectrum Disorders: Insights from Animal Models

Directory of Open Access Journals (Sweden)

Swati eBanerjee

2014-02-01

Full Text Available Autism spectrum disorders (ASD are a complex neurodevelopmental disorder that display a triad of core behavioral deficits including restricted interests, often accompanied by repetitive behavior, deficits in language and communication, and an inability to engage in reciprocal social interactions. ASD is among the most heritable disorders but is not a simple disorder with a singular pathology and has a rather complex etiology. It is interesting to note that perturbations in synaptic growth, development and stability underlie a variety of neuropsychiatric disorders, including ASD, schizophrenia, epilepsy and intellectual disability. Biological characterization of an increasing repertoire of synaptic mutants in various model organisms indicates synaptic dysfunction as causal in the pathophysiology of ASD. Our understanding of the genes and genetic pathways that contribute towards the formation, stabilization and maintenance of functional synapses coupled with an in-depth phenotypic analysis of the cellular and behavioral characteristics is therefore essential to unraveling the pathogenesis of these disorders. In this review, we discuss the genetic aspects of ASD emphasizing on the well conserved set of genes and genetic pathways implicated in this disorder, many of which contribute to synapse assembly and maintenance across species. We also review how fundamental research using animal models is providing key insights into the various facets of human ASD.

On stochastic geometry modeling of cellular uplink transmission with truncated channel inversion power control

KAUST Repository

Elsawy, Hesham; Hossain, Ekram

2014-01-01

Using stochastic geometry, we develop a tractable uplink modeling paradigm for outage probability and spectral efficiency in both single and multi-tier cellular wireless networks. The analysis accounts for per user equipment (UE) power control

The BioC-BioGRID corpus: full text articles annotated for curation of protein–protein and genetic interactions

Science.gov (United States)

Kim, Sun; Chatr-aryamontri, Andrew; Chang, Christie S.; Oughtred, Rose; Rust, Jennifer; Wilbur, W. John; Comeau, Donald C.; Dolinski, Kara; Tyers, Mike

2017-01-01

A great deal of information on the molecular genetics and biochemistry of model organisms has been reported in the scientific literature. However, this data is typically described in free text form and is not readily amenable to computational analyses. To this end, the BioGRID database systematically curates the biomedical literature for genetic and protein interaction data. This data is provided in a standardized computationally tractable format and includes structured annotation of experimental evidence. BioGRID curation necessarily involves substantial human effort by expert curators who must read each publication to extract the relevant information. Computational text-mining methods offer the potential to augment and accelerate manual curation. To facilitate the development of practical text-mining strategies, a new challenge was organized in BioCreative V for the BioC task, the collaborative Biocurator Assistant Task. This was a non-competitive, cooperative task in which the participants worked together to build BioC-compatible modules into an integrated pipeline to assist BioGRID curators. As an integral part of this task, a test collection of full text articles was developed that contained both biological entity annotations (gene/protein and organism/species) and molecular interaction annotations (protein–protein and genetic interactions (PPIs and GIs)). This collection, which we call the BioC-BioGRID corpus, was annotated by four BioGRID curators over three rounds of annotation and contains 120 full text articles curated in a dataset representing two major model organisms, namely budding yeast and human. The BioC-BioGRID corpus contains annotations for 6409 mentions of genes and their Entrez Gene IDs, 186 mentions of organism names and their NCBI Taxonomy IDs, 1867 mentions of PPIs and 701 annotations of PPI experimental evidence statements, 856 mentions of GIs and 399 annotations of GI evidence statements. The purpose, characteristics and possible future

The Sphagnome Project: enabling ecological and evolutionary insights through a genus-level sequencing project.

Science.gov (United States)

Weston, David J; Turetsky, Merritt R; Johnson, Matthew G; Granath, Gustaf; Lindo, Zoë; Belyea, Lisa R; Rice, Steven K; Hanson, David T; Engelhardt, Katharina A M; Schmutz, Jeremy; Dorrepaal, Ellen; Euskirchen, Eugénie S; Stenøien, Hans K; Szövényi, Péter; Jackson, Michelle; Piatkowski, Bryan T; Muchero, Wellington; Norby, Richard J; Kostka, Joel E; Glass, Jennifer B; Rydin, Håkan; Limpens, Juul; Tuittila, Eeva-Stiina; Ullrich, Kristian K; Carrell, Alyssa; Benscoter, Brian W; Chen, Jin-Gui; Oke, Tobi A; Nilsson, Mats B; Ranjan, Priya; Jacobson, Daniel; Lilleskov, Erik A; Clymo, R S; Shaw, A Jonathan

2018-01-01

Considerable progress has been made in ecological and evolutionary genetics with studies demonstrating how genes underlying plant and microbial traits can influence adaptation and even 'extend' to influence community structure and ecosystem level processes. Progress in this area is limited to model systems with deep genetic and genomic resources that often have negligible ecological impact or interest. Thus, important linkages between genetic adaptations and their consequences at organismal and ecological scales are often lacking. Here we introduce the Sphagnome Project, which incorporates genomics into a long-running history of Sphagnum research that has documented unparalleled contributions to peatland ecology, carbon sequestration, biogeochemistry, microbiome research, niche construction, and ecosystem engineering. The Sphagnome Project encompasses a genus-level sequencing effort that represents a new type of model system driven not only by genetic tractability, but by ecologically relevant questions and hypotheses. © 2017 UT-Battelle New Phytologist © 2017 New Phytologist Trust.

Quadratic Term Structure Models in Discrete Time

OpenAIRE

Marco Realdon

2006-01-01

This paper extends the results on quadratic term structure models in continuos time to the discrete time setting. The continuos time setting can be seen as a special case of the discrete time one. Recursive closed form solutions for zero coupon bonds are provided even in the presence of multiple correlated underlying factors. Pricing bond options requires simple integration. Model parameters may well be time dependent without scuppering such tractability. Model estimation does not require a r...

Use of Genetic Models to Study the Urinary Concentrating Mechanism

DEFF Research Database (Denmark)

Olesen, Emma Tina Bisgaard; Kortenoeven, Marleen L.A.; Fenton, Robert A.

2015-01-01

technology is providing critical new information about urinary concentrating processes and thus mechanisms for maintaining body water homeostasis. In this chapter we provide a brief overview of genetic mouse model generation, and then summarize findings in transgenic and knockout mice pertinent to our...

Application of genetic algorithm in modeling on-wafer inductors for up to 110 Ghz

Science.gov (United States)

Liu, Nianhong; Fu, Jun; Liu, Hui; Cui, Wenpu; Liu, Zhihong; Liu, Linlin; Zhou, Wei; Wang, Quan; Guo, Ao

2018-05-01

In this work, the genetic algorithm has been introducted into parameter extraction for on-wafer inductors for up to 110 GHz millimeter-wave operations, and nine independent parameters of the equivalent circuit model are optimized together. With the genetic algorithm, the model with the optimized parameters gives a better fitting accuracy than the preliminary parameters without optimization. Especially, the fitting accuracy of the Q value achieves a significant improvement after the optimization.

Analysis of conditional genetic effects and variance components in developmental genetics.

Science.gov (United States)

Zhu, J

1995-12-01

A genetic model with additive-dominance effects and genotype x environment interactions is presented for quantitative traits with time-dependent measures. The genetic model for phenotypic means at time t conditional on phenotypic means measured at previous time (t-1) is defined. Statistical methods are proposed for analyzing conditional genetic effects and conditional genetic variance components. Conditional variances can be estimated by minimum norm quadratic unbiased estimation (MINQUE) method. An adjusted unbiased prediction (AUP) procedure is suggested for predicting conditional genetic effects. A worked example from cotton fruiting data is given for comparison of unconditional and conditional genetic variances and additive effects.

Advanced technologies for genetically manipulating the silkworm Bombyx mori, a model Lepidopteran insect

Science.gov (United States)

Xu, Hanfu; O'Brochta, David A.

2015-01-01

Genetic technologies based on transposon-mediated transgenesis along with several recently developed genome-editing technologies have become the preferred methods of choice for genetically manipulating many organisms. The silkworm, Bombyx mori, is a Lepidopteran insect of great economic importance because of its use in silk production and because it is a valuable model insect that has greatly enhanced our understanding of the biology of insects, including many agricultural pests. In the past 10 years, great advances have been achieved in the development of genetic technologies in B. mori, including transposon-based technologies that rely on piggyBac-mediated transgenesis and genome-editing technologies that rely on protein- or RNA-guided modification of chromosomes. The successful development and application of these technologies has not only facilitated a better understanding of B. mori and its use as a silk production system, but also provided valuable experiences that have contributed to the development of similar technologies in non-model insects. This review summarizes the technologies currently available for use in B. mori, their application to the study of gene function and their use in genetically modifying B. mori for biotechnology applications. The challenges, solutions and future prospects associated with the development and application of genetic technologies in B. mori are also discussed. PMID:26108630

Mapping of the stochastic Lotka-Volterra model to models of population genetics and game theory

Science.gov (United States)

Constable, George W. A.; McKane, Alan J.

2017-08-01

The relationship between the M -species stochastic Lotka-Volterra competition (SLVC) model and the M -allele Moran model of population genetics is explored via timescale separation arguments. When selection for species is weak and the population size is large but finite, precise conditions are determined for the stochastic dynamics of the SLVC model to be mappable to the neutral Moran model, the Moran model with frequency-independent selection, and the Moran model with frequency-dependent selection (equivalently a game-theoretic formulation of the Moran model). We demonstrate how these mappings can be used to calculate extinction probabilities and the times until a species' extinction in the SLVC model.

Genetic Algorithms Principles Towards Hidden Markov Model

Directory of Open Access Journals (Sweden)

Nabil M. Hewahi

2011-10-01

Full Text Available In this paper we propose a general approach based on Genetic Algorithms (GAs to evolve Hidden Markov Models (HMM. The problem appears when experts assign probability values for HMM, they use only some limited inputs. The assigned probability values might not be accurate to serve in other cases related to the same domain. We introduce an approach based on GAs to find
out the suitable probability values for the HMM to be mostly correct in more cases than what have been used to assign the probability values.

Chemical event chain model of coupled genetic oscillators.

Science.gov (United States)

Jörg, David J; Morelli, Luis G; Jülicher, Frank

2018-03-01

We introduce a stochastic model of coupled genetic oscillators in which chains of chemical events involved in gene regulation and expression are represented as sequences of Poisson processes. We characterize steady states by their frequency, their quality factor, and their synchrony by the oscillator cross correlation. The steady state is determined by coupling and exhibits stochastic transitions between different modes. The interplay of stochasticity and nonlinearity leads to isolated regions in parameter space in which the coupled system works best as a biological pacemaker. Key features of the stochastic oscillations can be captured by an effective model for phase oscillators that are coupled by signals with distributed delays.

Chemical event chain model of coupled genetic oscillators

Science.gov (United States)

Jörg, David J.; Morelli, Luis G.; Jülicher, Frank

2018-03-01

We introduce a stochastic model of coupled genetic oscillators in which chains of chemical events involved in gene regulation and expression are represented as sequences of Poisson processes. We characterize steady states by their frequency, their quality factor, and their synchrony by the oscillator cross correlation. The steady state is determined by coupling and exhibits stochastic transitions between different modes. The interplay of stochasticity and nonlinearity leads to isolated regions in parameter space in which the coupled system works best as a biological pacemaker. Key features of the stochastic oscillations can be captured by an effective model for phase oscillators that are coupled by signals with distributed delays.

Human X-chromosome inactivation pattern distributions fit a model of genetically influenced choice better than models of completely random choice

Science.gov (United States)

Renault, Nisa K E; Pritchett, Sonja M; Howell, Robin E; Greer, Wenda L; Sapienza, Carmen; Ørstavik, Karen Helene; Hamilton, David C

2013-01-01

In eutherian mammals, one X-chromosome in every XX somatic cell is transcriptionally silenced through the process of X-chromosome inactivation (XCI). Females are thus functional mosaics, where some cells express genes from the paternal X, and the others from the maternal X. The relative abundance of the two cell populations (X-inactivation pattern, XIP) can have significant medical implications for some females. In mice, the ‘choice' of which X to inactivate, maternal or paternal, in each cell of the early embryo is genetically influenced. In humans, the timing of XCI choice and whether choice occurs completely randomly or under a genetic influence is debated. Here, we explore these questions by analysing the distribution of XIPs in large populations of normal females. Models were generated to predict XIP distributions resulting from completely random or genetically influenced choice. Each model describes the discrete primary distribution at the onset of XCI, and the continuous secondary distribution accounting for changes to the XIP as a result of development and ageing. Statistical methods are used to compare models with empirical data from Danish and Utah populations. A rigorous data treatment strategy maximises information content and allows for unbiased use of unphased XIP data. The Anderson–Darling goodness-of-fit statistics and likelihood ratio tests indicate that a model of genetically influenced XCI choice better fits the empirical data than models of completely random choice. PMID:23652377

An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications

International Nuclear Information System (INIS)

Blaifi, S.; Moulahoum, S.; Colak, I.; Merrouche, W.

2016-01-01

Highlights: • We proposed a developed dynamic battery model suitable for photovoltaic systems. • We used genetic algorithm optimization method to find parameters that gives minimized error. • The validation was carried out with real measurements from stand-alone photovoltaic string. - Abstract: Modeling of batteries in photovoltaic systems has been a major issue related to the random dynamic regime imposed by the changes of solar irradiation and ambient temperature added to the complexity of battery electrochemical and electrical behaviors. However, various approaches have been proposed to model the battery behavior by predicting from detailed electrochemical, electrical or analytical models to high-level stochastic models. In this paper, an improvement of dynamic electrical battery model is proposed by automatic parameter extraction using genetic algorithm in order to give usefulness and future implementation for practical application. It is highlighted that the enhancement of 21 values of the parameters of CEIMAT model presents a good agreement with real measurements for different modes like charge or discharge and various conditions.

Panel 4: Recent Advances in Otitis Media in Molecular Biology, Biochemistry, Genetics, and Animal Models

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Li, Jian-Dong; Hermansson, Ann; Ryan, Allen F.; Bakaletz, Lauren O.; Brown, Steve D.; Cheeseman, Michael T.; Juhn, Steven K.; Jung, Timothy T. K.; Lim, David J.; Lim, Jae Hyang; Lin, Jizhen; Moon, Sung-Kyun; Post, J. Christopher

2014-01-01

Background Otitis media (OM) is the most common childhood bacterial infection and also the leading cause of conductive hearing loss in children. Currently, there is an urgent need for developing novel therapeutic agents for treating OM based on full understanding of molecular pathogenesis in the areas of molecular biology, biochemistry, genetics, and animal model studies in OM. Objective To provide a state-of-the-art review concerning recent advances in OM in the areas of molecular biology, biochemistry, genetics, and animal model studies and to discuss the future directions of OM studies in these areas. Data Sources and Review Methods A structured search of the current literature (since June 2007). The authors searched PubMed for published literature in the areas of molecular biology, biochemistry, genetics, and animal model studies in OM. Results Over the past 4 years, significant progress has been made in the areas of molecular biology, biochemistry, genetics, and animal model studies in OM. These studies brought new insights into our understanding of the molecular and biochemical mechanisms underlying the molecular pathogenesis of OM and helped identify novel therapeutic targets for OM. Conclusions and Implications for Practice Our understanding of the molecular pathogenesis of OM has been significantly advanced, particularly in the areas of inflammation, innate immunity, mucus overproduction, mucosal hyperplasia, middle ear and inner ear interaction, genetics, genome sequencing, and animal model studies. Although these studies are still in their experimental stages, they help identify new potential therapeutic targets. Future preclinical and clinical studies will help to translate these exciting experimental research findings into clinical applications. PMID:23536532

Genetic evaluation of European quails by random regression models

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Flaviana Miranda Gonçalves

2012-09-01

Full Text Available The objective of this study was to compare different random regression models, defined from different classes of heterogeneity of variance combined with different Legendre polynomial orders for the estimate of (covariance of quails. The data came from 28,076 observations of 4,507 female meat quails of the LF1 lineage. Quail body weights were determined at birth and 1, 14, 21, 28, 35 and 42 days of age. Six different classes of residual variance were fitted to Legendre polynomial functions (orders ranging from 2 to 6 to determine which model had the best fit to describe the (covariance structures as a function of time. According to the evaluated criteria (AIC, BIC and LRT, the model with six classes of residual variances and of sixth-order Legendre polynomial was the best fit. The estimated additive genetic variance increased from birth to 28 days of age, and dropped slightly from 35 to 42 days. The heritability estimates decreased along the growth curve and changed from 0.51 (1 day to 0.16 (42 days. Animal genetic and permanent environmental correlation estimates between weights and age classes were always high and positive, except for birth weight. The sixth order Legendre polynomial, along with the residual variance divided into six classes was the best fit for the growth rate curve of meat quails; therefore, they should be considered for breeding evaluation processes by random regression models.

Genetic Programming for Automatic Hydrological Modelling

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Chadalawada, Jayashree; Babovic, Vladan

2017-04-01

One of the recent challenges for the hydrologic research community is the need for the development of coupled systems that involves the integration of hydrologic, atmospheric and socio-economic relationships. This poses a requirement for novel modelling frameworks that can accurately represent complex systems, given, the limited understanding of underlying processes, increasing volume of data and high levels of uncertainity. Each of the existing hydrological models vary in terms of conceptualization and process representation and is the best suited to capture the environmental dynamics of a particular hydrological system. Data driven approaches can be used in the integration of alternative process hypotheses in order to achieve a unified theory at catchment scale. The key steps in the implementation of integrated modelling framework that is influenced by prior understanding and data, include, choice of the technique for the induction of knowledge from data, identification of alternative structural hypotheses, definition of rules, constraints for meaningful, intelligent combination of model component hypotheses and definition of evaluation metrics. This study aims at defining a Genetic Programming based modelling framework that test different conceptual model constructs based on wide range of objective functions and evolves accurate and parsimonious models that capture dominant hydrological processes at catchment scale. In this paper, GP initializes the evolutionary process using the modelling decisions inspired from the Superflex framework [Fenicia et al., 2011] and automatically combines them into model structures that are scrutinized against observed data using statistical, hydrological and flow duration curve based performance metrics. The collaboration between data driven and physical, conceptual modelling paradigms improves the ability to model and manage hydrologic systems. Fenicia, F., D. Kavetski, and H. H. Savenije (2011), Elements of a flexible approach

Wavelet-linear genetic programming: A new approach for modeling monthly streamflow

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Ravansalar, Masoud; Rajaee, Taher; Kisi, Ozgur

2017-06-01

The streamflows are important and effective factors in stream ecosystems and its accurate prediction is an essential and important issue in water resources and environmental engineering systems. A hybrid wavelet-linear genetic programming (WLGP) model, which includes a discrete wavelet transform (DWT) and a linear genetic programming (LGP) to predict the monthly streamflow (Q) in two gauging stations, Pataveh and Shahmokhtar, on the Beshar River at the Yasuj, Iran were used in this study. In the proposed WLGP model, the wavelet analysis was linked to the LGP model where the original time series of streamflow were decomposed into the sub-time series comprising wavelet coefficients. The results were compared with the single LGP, artificial neural network (ANN), a hybrid wavelet-ANN (WANN) and Multi Linear Regression (MLR) models. The comparisons were done by some of the commonly utilized relevant physical statistics. The Nash coefficients (E) were found as 0.877 and 0.817 for the WLGP model, for the Pataveh and Shahmokhtar stations, respectively. The comparison of the results showed that the WLGP model could significantly increase the streamflow prediction accuracy in both stations. Since, the results demonstrate a closer approximation of the peak streamflow values by the WLGP model, this model could be utilized for the simulation of cumulative streamflow data prediction in one month ahead.

Nature and nurture: environmental influences on a genetic rat model of depression.

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Mehta-Raghavan, N S; Wert, S L; Morley, C; Graf, E N; Redei, E E

2016-03-29

In this study, we sought to learn whether adverse events such as chronic restraint stress (CRS), or 'nurture' in the form of environmental enrichment (EE), could modify depression-like behavior and blood biomarker transcript levels in a genetic rat model of depression. The Wistar Kyoto More Immobile (WMI) is a genetic model of depression that aided in the identification of blood transcriptomic markers, which successfully distinguished adolescent and adult subjects with major depressive disorders from their matched no-disorder controls. Here, we followed the effects of CRS and EE in adult male WMIs and their genetically similar control strain, the Wistar Kyoto Less Immobile (WLI), that does not show depression-like behavior, by measuring the levels of these transcripts in the blood and hippocampus. In WLIs, increased depression-like behavior and transcriptomic changes were present in response to CRS, but in WMIs no behavioral or additive transcriptomic changes occurred. Environmental enrichment decreased both the inherent depression-like behavior in the WMIs and the behavioral difference between WMIs and WLIs, but did not reverse basal transcript level differences between the strains. The inverse behavioral change induced by CRS and EE in the WLIs did not result in parallel inverse expression changes of the transcriptomic markers, suggesting that these behavioral responses to the environment work via separate molecular pathways. In contrast, 'trait' transcriptomic markers with expression differences inherent and unchanging between the strains regardless of the environment suggest that in our model, environmental and genetic etiologies of depression work through independent molecular mechanisms.

Developing genetic epidemiological models to predict risk for nasopharyngeal carcinoma in high-risk population of China.

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Hong-Lian Ruan

Full Text Available To date, the only established model for assessing risk for nasopharyngeal carcinoma (NPC relies on the sero-status of the Epstein-Barr virus (EBV. By contrast, the risk assessment models proposed here include environmental risk factors, family history of NPC, and information on genetic variants. The models were developed using epidemiological and genetic data from a large case-control study, which included 1,387 subjects with NPC and 1,459 controls of Cantonese origin. The predictive accuracy of the models were then assessed by calculating the area under the receiver-operating characteristic curves (AUC. To compare the discriminatory improvement of models with and without genetic information, we estimated the net reclassification improvement (NRI and integrated discrimination index (IDI. Well-established environmental risk factors for NPC include consumption of salted fish and preserved vegetables and cigarette smoking (in pack years. The environmental model alone shows modest discriminatory ability (AUC = 0.68; 95% CI: 0.66, 0.70, which is only slightly increased by the addition of data on family history of NPC (AUC = 0.70; 95% CI: 0.68, 0.72. With the addition of data on genetic variants, however, our model's discriminatory ability rises to 0.74 (95% CI: 0.72, 0.76. The improvements in NRI and IDI also suggest the potential usefulness of considering genetic variants when screening for NPC in endemic areas. If these findings are confirmed in larger cohort and population-based case-control studies, use of the new models to analyse data from NPC-endemic areas could well lead to earlier detection of NPC.

Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis

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Broddrick, Jared T.; Rubin, Benjamin E.; Welkie, David G.; Du, Niu; Mih, Nathan; Diamond, Spencer; Lee, Jenny J.; Golden, Susan S.; Palsson, Bernhard O.

2016-01-01

The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. Here, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting in the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology. PMID:27911809

A genetic algorithm for solving supply chain network design model

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Firoozi, Z.; Ismail, N.; Ariafar, S. H.; Tang, S. H.; Ariffin, M. K. M. A.

2013-09-01

Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.

Estimating the actual subject-specific genetic correlations in behavior genetics.

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Molenaar, Peter C M

2012-10-01

Generalization of the standard behavior longitudinal genetic factor model for the analysis of interindividual phenotypic variation to a genetic state space model for the analysis of intraindividual variation enables the possibility to estimate subject-specific heritabilities.

A Tri-Part Model for Genetics Literacy: Exploring Undergraduate Student Reasoning about Authentic Genetics Dilemmas

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Shea, Nicole A.; Duncan, Ravit Golan; Stephenson, Celeste

2015-01-01

Genetics literacy is becoming increasingly important as advancements in our application of genetic technologies such as stem cell research, cloning, and genetic screening become more prevalent. Very few studies examine how genetics literacy is applied when reasoning about authentic genetic dilemmas. However, there is evidence that situational…

[Analysis of genetic models and gene effects on main agronomy characters in rapeseed].

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Li, J; Qiu, J; Tang, Z; Shen, L

1992-01-01

According to four different genetic models, the genetic patterns of 8 agronomy traits were analysed by using the data of 24 generations which included positive and negative cross of 81008 x Tower, both of the varieties are of good quality. The results showed that none of 8 characters could fit in with additive-dominance models. Epistasis was found in all of these characters, and it has significant effect on generation means. Seed weight/plant and some other main yield characters are controlled by duplicate interaction genes. The interaction between triple genes or multiple genes needs to be utilized in yield heterosis.

Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

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Maria Gabriela Campolina Diniz Peixoto

2014-05-01

Full Text Available The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524 of test-day milk yield (TDMY from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects, whereas the contemporary group, calving age (linear and quadratic effects and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

Genetic algorithms used for PWRs refuel management automatic optimization: a new modelling

International Nuclear Information System (INIS)

Chapot, Jorge Luiz C.; Schirru, Roberto; Silva, Fernando Carvalho da

1996-01-01

A Genetic Algorithms-based system, linking the computer codes GENESIS 5.0 and ANC through the interface ALGER, has been developed aiming the PWRs fuel management optimization. An innovative codification, the Lists Model, has been incorporated to the genetic system, which avoids the use of variants of the standard crossover operator and generates only valid loading patterns in the core. The GENESIS/ALGER/ANC system has been successfully tested in an optimization study for Angra-1 second cycle. (author)

Genetic analyses of partial egg production in Japanese quail using multi-trait random regression models.

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Karami, K; Zerehdaran, S; Barzanooni, B; Lotfi, E

2017-12-01

1. The aim of the present study was to estimate genetic parameters for average egg weight (EW) and egg number (EN) at different ages in Japanese quail using multi-trait random regression (MTRR) models. 2. A total of 8534 records from 900 quail, hatched between 2014 and 2015, were used in the study. Average weekly egg weights and egg numbers were measured from second until sixth week of egg production. 3. Nine random regression models were compared to identify the best order of the Legendre polynomials (LP). The most optimal model was identified by the Bayesian Information Criterion. A model with second order of LP for fixed effects, second order of LP for additive genetic effects and third order of LP for permanent environmental effects (MTRR23) was found to be the best. 4. According to the MTRR23 model, direct heritability for EW increased from 0.26 in the second week to 0.53 in the sixth week of egg production, whereas the ratio of permanent environment to phenotypic variance decreased from 0.48 to 0.1. Direct heritability for EN was low, whereas the ratio of permanent environment to phenotypic variance decreased from 0.57 to 0.15 during the production period. 5. For each trait, estimated genetic correlations among weeks of egg production were high (from 0.85 to 0.98). Genetic correlations between EW and EN were low and negative for the first two weeks, but they were low and positive for the rest of the egg production period. 6. In conclusion, random regression models can be used effectively for analysing egg production traits in Japanese quail. Response to selection for increased egg weight would be higher at older ages because of its higher heritability and such a breeding program would have no negative genetic impact on egg production.

Strains and Stressors: An Analysis of Touchscreen Learning in Genetically Diverse Mouse Strains

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Graybeal, Carolyn; Bachu, Munisa; Mozhui, Khyobeni; Saksida, Lisa M.; Bussey, Timothy J.; Sagalyn, Erica; Williams, Robert W.; Holmes, Andrew

2014-01-01

Touchscreen-based systems are growing in popularity as a tractable, translational approach for studying learning and cognition in rodents. However, while mouse strains are well known to differ in learning across various settings, performance variation between strains in touchscreen learning has not been well described. The selection of appropriate genetic strains and backgrounds is critical to the design of touchscreen-based studies and provides a basis for elucidating genetic factors moderating behavior. Here we provide a quantitative foundation for visual discrimination and reversal learning using touchscreen assays across a total of 35 genotypes. We found significant differences in operant performance and learning, including faster reversal learning in DBA/2J compared to C57BL/6J mice. We then assessed DBA/2J and C57BL/6J for differential sensitivity to an environmental insult by testing for alterations in reversal learning following exposure to repeated swim stress. Stress facilitated reversal learning (selectively during the late stage of reversal) in C57BL/6J, but did not affect learning in DBA/2J. To dissect genetic factors underlying these differences, we phenotyped a family of 27 BXD strains generated by crossing C57BL/6J and DBA/2J. There was marked variation in discrimination, reversal and extinction learning across the BXD strains, suggesting this task may be useful for identifying underlying genetic differences. Moreover, different measures of touchscreen learning were only modestly correlated in the BXD strains, indicating that these processes are comparatively independent at both genetic and phenotypic levels. Finally, we examined the behavioral structure of learning via principal component analysis of the current data, plus an archival dataset, totaling 765 mice. This revealed 5 independent factors suggestive of “reversal learning,” “motivation-related late reversal learning,” “discrimination learning,” “speed to respond,” and �

Cost optimization model and its heuristic genetic algorithms

International Nuclear Information System (INIS)

Liu Wei; Wang Yongqing; Guo Jilin

1999-01-01

Interest and escalation are large quantity in proportion to the cost of nuclear power plant construction. In order to optimize the cost, the mathematics model of cost optimization for nuclear power plant construction was proposed, which takes the maximum net present value as the optimization goal. The model is based on the activity networks of the project and is an NP problem. A heuristic genetic algorithms (HGAs) for the model was introduced. In the algorithms, a solution is represented with a string of numbers each of which denotes the priority of each activity for assigned resources. The HGAs with this encoding method can overcome the difficulty which is harder to get feasible solutions when using the traditional GAs to solve the model. The critical path of the activity networks is figured out with the concept of predecessor matrix. An example was computed with the HGAP programmed in C language. The results indicate that the model is suitable for the objectiveness, the algorithms is effective to solve the model

A genetic model of progressively partial melting for uranium-bearing granites in south China

International Nuclear Information System (INIS)

Zhai Jianping.

1989-01-01

A genetic model of progressively partial and enrichment mechanism of uranium during partial melting of the sources of material studied and the significance of the genetic model in search of uranium deposits is elaborated. This model accounts better for some geological and geochemical features of uranium-bearing granties and suspects the traditional idea that igneous uranium-bearing granites were formed by fusion of U-rich strata surrounding these granites. Finally this paper points out that the infuence of U-rich strata of wall rocks of granites over uranium-bearing granites depends on variation of water solubility in the magma and assimilation of magma to wall rocks during its ascending and crystallization

Genetic complexity in a Drosophila model of diabetes-associated misfolded human proinsulin.

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Park, Soo-Young; Ludwig, Michael Z; Tamarina, Natalia A; He, Bin Z; Carl, Sarah H; Dickerson, Desiree A; Barse, Levi; Arun, Bharath; Williams, Calvin L; Miles, Cecelia M; Philipson, Louis H; Steiner, Donald F; Bell, Graeme I; Kreitman, Martin

2014-02-01

Drosophila melanogaster has been widely used as a model of human Mendelian disease, but its value in modeling complex disease has received little attention. Fly models of complex disease would enable high-resolution mapping of disease-modifying loci and the identification of novel targets for therapeutic intervention. Here, we describe a fly model of permanent neonatal diabetes mellitus and explore the complexity of this model. The approach involves the transgenic expression of a misfolded mutant of human preproinsulin, hINS(C96Y), which is a cause of permanent neonatal diabetes. When expressed in fly imaginal discs, hINS(C96Y) causes a reduction of adult structures, including the eye, wing, and notum. Eye imaginal discs exhibit defects in both the structure and the arrangement of ommatidia. In the wing, expression of hINS(C96Y) leads to ectopic expression of veins and mechano-sensory organs, indicating disruption of wild-type signaling processes regulating cell fates. These readily measurable "disease" phenotypes are sensitive to temperature, gene dose, and sex. Mutant (but not wild-type) proinsulin expression in the eye imaginal disc induces IRE1-mediated XBP1 alternative splicing, a signal for endoplasmic reticulum stress response activation, and produces global change in gene expression. Mutant hINS transgene tester strains, when crossed to stocks from the Drosophila Genetic Reference Panel, produce F1 adults with a continuous range of disease phenotypes and large broad-sense heritability. Surprisingly, the severity of mutant hINS-induced disease in the eye is not correlated with that in the notum in these crosses, nor with eye reduction phenotypes caused by the expression of two dominant eye mutants acting in two different eye development pathways, Drop (Dr) or Lobe (L), when crossed into the same genetic backgrounds. The tissue specificity of genetic variability for mutant hINS-induced disease has, therefore, its own distinct signature. The genetic dominance

Genetic evolution, plasticity, and bet-hedging as adaptive responses to temporally autocorrelated fluctuating selection: A quantitative genetic model.

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Tufto, Jarle

2015-08-01

Adaptive responses to autocorrelated environmental fluctuations through evolution in mean reaction norm elevation and slope and an independent component of the phenotypic variance are analyzed using a quantitative genetic model. Analytic approximations expressing the mutual dependencies between all three response modes are derived and solved for the joint evolutionary outcome. Both genetic evolution in reaction norm elevation and plasticity are favored by slow temporal fluctuations, with plasticity, in the absence of microenvironmental variability, being the dominant evolutionary outcome for reasonable parameter values. For fast fluctuations, tracking of the optimal phenotype through genetic evolution and plasticity is limited. If residual fluctuations in the optimal phenotype are large and stabilizing selection is strong, selection then acts to increase the phenotypic variance (bet-hedging adaptive). Otherwise, canalizing selection occurs. If the phenotypic variance increases with plasticity through the effect of microenvironmental variability, this shifts the joint evolutionary balance away from plasticity in favor of genetic evolution. If microenvironmental deviations experienced by each individual at the time of development and selection are correlated, however, more plasticity evolves. The adaptive significance of evolutionary fluctuations in plasticity and the phenotypic variance, transient evolution, and the validity of the analytic approximations are investigated using simulations. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

Human Urine-Derived Renal Progenitors for Personalized Modeling of Genetic Kidney Disorders.

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Lazzeri, Elena; Ronconi, Elisa; Angelotti, Maria Lucia; Peired, Anna; Mazzinghi, Benedetta; Becherucci, Francesca; Conti, Sara; Sansavini, Giulia; Sisti, Alessandro; Ravaglia, Fiammetta; Lombardi, Duccio; Provenzano, Aldesia; Manonelles, Anna; Cruzado, Josep M; Giglio, Sabrina; Roperto, Rosa Maria; Materassi, Marco; Lasagni, Laura; Romagnani, Paola

2015-08-01

The critical role of genetic and epigenetic factors in the pathogenesis of kidney disorders is gradually becoming clear, and the need for disease models that recapitulate human kidney disorders in a personalized manner is paramount. In this study, we describe a method to select and amplify renal progenitor cultures from the urine of patients with kidney disorders. Urine-derived human renal progenitors exhibited phenotype and functional properties identical to those purified from kidney tissue, including the capacity to differentiate into tubular cells and podocytes, as demonstrated by confocal microscopy, Western blot analysis of podocyte-specific proteins, and scanning electron microscopy. Lineage tracing studies performed with conditional transgenic mice, in which podocytes are irreversibly tagged upon tamoxifen treatment (NPHS2.iCreER;mT/mG), that were subjected to doxorubicin nephropathy demonstrated that renal progenitors are the only urinary cell population that can be amplified in long-term culture. To validate the use of these cells for personalized modeling of kidney disorders, renal progenitors were obtained from (1) the urine of children with nephrotic syndrome and carrying potentially pathogenic mutations in genes encoding for podocyte proteins and (2) the urine of children without genetic alterations, as validated by next-generation sequencing. Renal progenitors obtained from patients carrying pathogenic mutations generated podocytes that exhibited an abnormal cytoskeleton structure and functional abnormalities compared with those obtained from patients with proteinuria but without genetic mutations. The results of this study demonstrate that urine-derived patient-specific renal progenitor cultures may be an innovative research tool for modeling of genetic kidney disorders. Copyright © 2015 by the American Society of Nephrology.

Genetic Programming and Standardization in Water Temperature Modelling

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Maritza Arganis

2009-01-01

Full Text Available An application of Genetic Programming (an evolutionary computational tool without and with standardization data is presented with the aim of modeling the behavior of the water temperature in a river in terms of meteorological variables that are easily measured, to explore their explanatory power and to emphasize the utility of the standardization of variables in order to reduce the effect of those with large variance. Recorded data corresponding to the water temperature behavior at the Ebro River, Spain, are used as analysis case, showing a performance improvement on the developed model when data are standardized. This improvement is reflected in a reduction of the mean square error. Finally, the models obtained in this document were applied to estimate the water temperature in 2004, in order to provide evidence about their applicability to forecasting purposes.

Consequences of the genetic threshold model for observing partial migration under climate change scenarios.

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Cobben, Marleen M P; van Noordwijk, Arie J

2017-10-01

Migration is a widespread phenomenon across the animal kingdom as a response to seasonality in environmental conditions. Partially migratory populations are populations that consist of both migratory and residential individuals. Such populations are very common, yet their stability has long been debated. The inheritance of migratory activity is currently best described by the threshold model of quantitative genetics. The inclusion of such a genetic threshold model for migratory behavior leads to a stable zone in time and space of partially migratory populations under a wide range of demographic parameter values, when assuming stable environmental conditions and unlimited genetic diversity. Migratory species are expected to be particularly sensitive to global warming, as arrival at the breeding grounds might be increasingly mistimed as a result of the uncoupling of long-used cues and actual environmental conditions, with decreasing reproduction as a consequence. Here, we investigate the consequences for migratory behavior and the stability of partially migratory populations under five climate change scenarios and the assumption of a genetic threshold value for migratory behavior in an individual-based model. The results show a spatially and temporally stable zone of partially migratory populations after different lengths of time in all scenarios. In the scenarios in which the species expands its range from a particular set of starting populations, the genetic diversity and location at initialization determine the species' colonization speed across the zone of partial migration and therefore across the entire landscape. Abruptly changing environmental conditions after model initialization never caused a qualitative change in phenotype distributions, or complete extinction. This suggests that climate change-induced shifts in species' ranges as well as changes in survival probabilities and reproductive success can be met with flexibility in migratory behavior at the

Efficient multiple-trait association and estimation of genetic correlation using the matrix-variate linear mixed model.

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Furlotte, Nicholas A; Eskin, Eleazar

2015-05-01

Multiple-trait association mapping, in which multiple traits are used simultaneously in the identification of genetic variants affecting those traits, has recently attracted interest. One class of approaches for this problem builds on classical variance component methodology, utilizing a multitrait version of a linear mixed model. These approaches both increase power and provide insights into the genetic architecture of multiple traits. In particular, it is possible to estimate the genetic correlation, which is a measure of the portion of the total correlation between traits that is due to additive genetic effects. Unfortunately, the practical utility of these methods is limited since they are computationally intractable for large sample sizes. In this article, we introduce a reformulation of the multiple-trait association mapping approach by defining the matrix-variate linear mixed model. Our approach reduces the computational time necessary to perform maximum-likelihood inference in a multiple-trait model by utilizing a data transformation. By utilizing a well-studied human cohort, we show that our approach provides more than a 10-fold speedup, making multiple-trait association feasible in a large population cohort on the genome-wide scale. We take advantage of the efficiency of our approach to analyze gene expression data. By decomposing gene coexpression into a genetic and environmental component, we show that our method provides fundamental insights into the nature of coexpressed genes. An implementation of this method is available at http://genetics.cs.ucla.edu/mvLMM. Copyright © 2015 by the Genetics Society of America.

System Response Analysis and Model Order Reduction, Using Conventional Method, Bond Graph Technique and Genetic Programming

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Lubna Moin

2009-04-01

Full Text Available This research paper basically explores and compares the different modeling and analysis techniques and than it also explores the model order reduction approach and significance. The traditional modeling and simulation techniques for dynamic systems are generally adequate for single-domain systems only, but the Bond Graph technique provides new strategies for reliable solutions of multi-domain system. They are also used for analyzing linear and non linear dynamic production system, artificial intelligence, image processing, robotics and industrial automation. This paper describes a unique technique of generating the Genetic design from the tree structured transfer function obtained from Bond Graph. This research work combines bond graphs for model representation with Genetic programming for exploring different ideas on design space tree structured transfer function result from replacing typical bond graph element with their impedance equivalent specifying impedance lows for Bond Graph multiport. This tree structured form thus obtained from Bond Graph is applied for generating the Genetic Tree. Application studies will identify key issues and importance for advancing this approach towards becoming on effective and efficient design tool for synthesizing design for Electrical system. In the first phase, the system is modeled using Bond Graph technique. Its system response and transfer function with conventional and Bond Graph method is analyzed and then a approach towards model order reduction is observed. The suggested algorithm and other known modern model order reduction techniques are applied to a 11th order high pass filter [1], with different approach. The model order reduction technique developed in this paper has least reduction errors and secondly the final model retains structural information. The system response and the stability analysis of the system transfer function taken by conventional and by Bond Graph method is compared and

Genetic human prion disease modelled in PrP transgenic Drosophila.

Science.gov (United States)

Thackray, Alana M; Cardova, Alzbeta; Wolf, Hanna; Pradl, Lydia; Vorberg, Ina; Jackson, Walker S; Bujdoso, Raymond

2017-09-20

Inherited human prion diseases, such as fatal familial insomnia (FFI) and familial Creutzfeldt-Jakob disease (fCJD), are associated with autosomal dominant mutations in the human prion protein gene PRNP and accumulation of PrP Sc , an abnormal isomer of the normal host protein PrP C , in the brain of affected individuals. PrP Sc is the principal component of the transmissible neurotoxic prion agent. It is important to identify molecular pathways and cellular processes that regulate prion formation and prion-induced neurotoxicity. This will allow identification of possible therapeutic interventions for individuals with, or at risk from, genetic human prion disease. Increasingly, Drosophila has been used to model human neurodegenerative disease. An important unanswered question is whether genetic prion disease with concomitant spontaneous prion formation can be modelled in Drosophila We have used pUAST/PhiC31-mediated site-directed mutagenesis to generate Drosophila transgenic for murine or hamster PrP (prion protein) that carry single-codon mutations associated with genetic human prion disease. Mouse or hamster PrP harbouring an FFI (D178N) or fCJD (E200K) mutation showed mild Proteinase K resistance when expressed in Drosophila Adult Drosophila transgenic for FFI or fCJD variants of mouse or hamster PrP displayed a spontaneous decline in locomotor ability that increased in severity as the flies aged. Significantly, this mutant PrP-mediated neurotoxic fly phenotype was transferable to recipient Drosophila that expressed the wild-type form of the transgene. Collectively, our novel data are indicative of the spontaneous formation of a PrP-dependent neurotoxic phenotype in FFI- or CJD-PrP transgenic Drosophila and show that inherited human prion disease can be modelled in this invertebrate host. © 2017 The Author(s).

Application of hierarchical genetic models to Raven and WAIS subtests: a Dutch twin study

NARCIS (Netherlands)

Rijsdijk, F.V.; Vernon, P.A.; Boomsma, D.I.

2002-01-01

Hierarchical models of intelligence are highly informative and widely accepted. Application of these models to twin data, however, is sparse. This paper addresses the question of how a genetic hierarchical model fits the Wechsler Adult Intelligence Scale (WAIS) subtests and the Raven Standard

Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm-Artificial Neural Network.

Science.gov (United States)

Ramadan Suleiman, Ahmed; Nehdi, Moncef L

2017-02-07

This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm-artificial neural network (GA-ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA-ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials.

Modeling AEC—New Approaches to Study Rare Genetic Disorders

Science.gov (United States)

Koch, Peter J.; Dinella, Jason; Fete, Mary; Siegfried, Elaine C.; Koster, Maranke I.

2015-01-01

Ankyloblepharon-ectodermal defects-cleft lip/palate (AEC) syndrome is a rare monogenetic disorder that is characterized by severe abnormalities in ectoderm-derived tissues, such as skin and its appendages. A major cause of morbidity among affected infants is severe and chronic skin erosions. Currently, supportive care is the only available treatment option for AEC patients. Mutations in TP63, a gene that encodes key regulators of epidermal development, are the genetic cause of AEC. However, it is currently not clear how mutations in TP63 lead to the various defects seen in the patients’ skin. In this review, we will discuss current knowledge of the AEC disease mechanism obtained by studying patient tissue and genetically engineered mouse models designed to mimic aspects of the disorder. We will then focus on new approaches to model AEC, including the use of patient cells and stem cell technology to replicate the disease in a human tissue culture model. The latter approach will advance our understanding of the disease and will allow for the development of new in vitro systems to identify drugs for the treatment of skin erosions in AEC patients. Further, the use of stem cell technology, in particular induced pluripotent stem cells (iPSC), will enable researchers to develop new therapeutic approaches to treat the disease using the patient’s own cells (autologous keratinocyte transplantation) after correction of the disease-causing mutations. PMID:24665072

Molecular genetics of cancer and tumorigenesis: Drosophila models

Institute of Scientific and Technical Information of China (English)

Wu-Min Deng

2011-01-01

Why do some cells not respond to normal control of cell division and become tumorous? Which signals trigger some tumor cells to migrate and colonize other tissues? What genetic factors are responsible for tumorigenesis and cancer development? What environmental factors play a role in cancer formation and progression? In how many ways can our bodies prevent and restrict the growth of cancerous cells?How can we identify and deliver effective drugs to fight cancer? In the fight against cancer,which kills more people than any other disease,these and other questions have long interested researchers from a diverse range of fields.To answer these questions and to fight cancer more effectively,we must increase our understanding of basic cancer biology.Model organisms,including the fruit fly Drosophila melanogaster,have played instrumental roles in our understanding of this devastating disease and the search for effective cures.Drosophila and its highly effective,easy-touse,and ever-expanding genetic tools have contributed toand enriched our knowledge of cancer and tumor formation tremendously.

Potential of zebrafish as a model for exploring the role of the amygdala in emotional memory and motivational behavior.

Science.gov (United States)

Perathoner, Simon; Cordero-Maldonado, Maria Lorena; Crawford, Alexander D

2016-06-01

Emotion is a key aspect of behavior, enabling humans and animals to assign either positive or negative values to sensory inputs and thereby to make appropriate decisions. Classical experiments in mammalian models, mainly in primates and rodents, have shown that the amygdala is essential for appetitive and aversive associative processing and that dysfunction of this brain region leads to various psychiatric conditions, including depression, generalized anxiety disorder, panic disorder, phobias, autism, and posttraumatic stress disorder. In the past 2 decades, the zebrafish (Danio rerio; Cyprinidae) has emerged as a versatile, reliable vertebrate model organism for the in vivo study of development, gene function, and numerous aspects of human pathologies. Small size, high fecundity, rapid external development, transparency, genetic tractability, and high genetic and physiologic homology with humans are among the factors that have contributed to the success with this small fish in different biomedical research areas. Recent findings indicate that, despite the anatomical differences in the brain structure of teleosts and tetrapods, fish possess a structure homologous to the mammalian amygdala, a hypothesis that is supported by the expression of molecular markers, analyses of neuronal projections in different brain areas, and behavioral studies. This Review summarizes this evidence and highlights a number of relevant bioassays in zebrafish to study emotional memory and motivational behavior. © 2016 Wiley Periodicals, Inc.

Experiencing the genetic body: parents' encounters with pediatric clinical genetics.

Science.gov (United States)

Raspberry, Kelly; Skinner, Debra

2007-01-01

Because of advancements in genetic research and technologies, the clinical practice of genetics is becoming a prevalent component of biomedicine. As the genetic basis for more and more diseases are found, it is possible that ways of experiencing health, illness, identity, kin relations, and the body are becoming geneticized, or understood within a genetic model of disease. Yet, other models and relations that go beyond genetic explanations also shape interpretations of health and disease. This article explores how one group of individuals for whom genetic disorder is highly relevant formulates their views of the body in light of genetic knowledge. Using data from an ethnographic study of 106 parents or potential parents of children with known or suspected genetic disorders who were referred to a pediatric genetic counseling and evaluation clinic in the southeastern United States, we find that these parents do, to some degree, perceive of their children's disorders in terms of a genetic body that encompasses two principal qualities: a sense of predetermined health and illness and an awareness of a profound historicity that reaches into the past and extends into the present and future. They experience this genetic body as both fixed and historical, but they also express ideas of a genetic body made less deterministic by their own efforts and future possibilities. This account of parents' experiences with genetics and clinical practice contributes to a growing body of work on the ways in which genetic information and technologies are transforming popular and medical notions of the body, and with it, health, illness, kinship relations, and personal and social identities.

Evaluating the Genetics of Common Variable Immunodeficiency: Monogenetic Model and Beyond

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Guillem de Valles-Ibáñez

2018-05-01

Full Text Available Common variable immunodeficiency (CVID is the most frequent symptomatic primary immunodeficiency characterized by recurrent infections, hypogammaglobulinemia and poor response to vaccines. Its diagnosis is made based on clinical and immunological criteria, after exclusion of other diseases that can cause similar phenotypes. Currently, less than 20% of cases of CVID have a known underlying genetic cause. We have analyzed whole-exome sequencing and copy number variants data of 36 children and adolescents diagnosed with CVID and healthy relatives to estimate the proportion of monogenic cases. We have replicated an association of CVID to p.C104R in TNFRSF13B and reported the second case of homozygous patient to date. Our results also identify five causative genetic variants in LRBA, CTLA4, NFKB1, and PIK3R1, as well as other very likely causative variants in PRKCD, MAPK8, or DOCK8 among others. We experimentally validate the effect of the LRBA stop-gain mutation which abolishes protein production and downregulates the expression of CTLA4, and of the frameshift indel in CTLA4 producing expression downregulation of the protein. Our results indicate a monogenic origin of at least 15–24% of the CVID cases included in the study. The proportion of monogenic patients seems to be lower in CVID than in other PID that have also been analyzed by whole exome or targeted gene panels sequencing. Regardless of the exact proportion of CVID monogenic cases, other genetic models have to be considered for CVID. We propose that because of its prevalence and other features as intermediate penetrancies and phenotypic variation within families, CVID could fit with other more complex genetic scenarios. In particular, in this work, we explore the possibility of CVID being originated by an oligogenic model with the presence of heterozygous mutations in interacting proteins or by the accumulation of detrimental variants in particular immunological pathways, as well as

Unified Tractable Model for Large-Scale Networks Using Stochastic Geometry: Analysis and Design

KAUST Repository

Afify, Laila H.

2016-12-01

The ever-growing demands for wireless technologies necessitate the evolution of next generation wireless networks that fulfill the diverse wireless users requirements. However, upscaling existing wireless networks implies upscaling an intrinsic component in the wireless domain; the aggregate network interference. Being the main performance limiting factor, it becomes crucial to develop a rigorous analytical framework to accurately characterize the out-of-cell interference, to reap the benefits of emerging networks. Due to the different network setups and key performance indicators, it is essential to conduct a comprehensive study that unifies the various network configurations together with the different tangible performance metrics. In that regard, the focus of this thesis is to present a unified mathematical paradigm, based on Stochastic Geometry, for large-scale networks with different antenna/network configurations. By exploiting such a unified study, we propose an efficient automated network design strategy to satisfy the desired network objectives. First, this thesis studies the exact aggregate network interference characterization, by accounting for each of the interferers signals in the large-scale network. Second, we show that the information about the interferers symbols can be approximated via the Gaussian signaling approach. The developed mathematical model presents twofold analysis unification for uplink and downlink cellular networks literature. It aligns the tangible decoding error probability analysis with the abstract outage probability and ergodic rate analysis. Furthermore, it unifies the analysis for different antenna configurations, i.e., various multiple-input multiple-output (MIMO) systems. Accordingly, we propose a novel reliable network design strategy that is capable of appropriately adjusting the network parameters to meet desired design criteria. In addition, we discuss the diversity-multiplexing tradeoffs imposed by differently favored

Estimating Ambiguity Preferences and Perceptions in Multiple Prior Models: Evidence from the Field

NARCIS (Netherlands)

S.G. Dimmock (Stephen); R.R.P. Kouwenberg (Roy); O.S. Mitchell (Olivia); K. Peijnenburg (Kim)

2015-01-01

markdownabstractWe develop a tractable method to estimate multiple prior models of decision-making under ambiguity. In a representative sample of the U.S. population, we measure ambiguity attitudes in the gain and loss domains. We find that ambiguity aversion is common for uncertain events of

Genetic parameters for body condition score, body weight, milk yield, and fertility estimated using random regression models.

Science.gov (United States)

Berry, D P; Buckley, F; Dillon, P; Evans, R D; Rath, M; Veerkamp, R F

2003-11-01

Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields from 8725 multiparous Holstein-Friesian cows. A cubic random regression was sufficient to model the changing genetic variances for BCS, BW, and milk across different days in milk. The genetic correlations between BCS and fertility changed little over the lactation; genetic correlations between BCS and interval to first service and between BCS and pregnancy rate to first service varied from -0.47 to -0.31, and from 0.15 to 0.38, respectively. This suggests that maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in midlactation when the genetic variance for BCS is largest. Selection for increased BW resulted in shorter intervals to first service, but more services and poorer pregnancy rates; genetic correlations between BW and pregnancy rate to first service varied from -0.52 to -0.45. Genetic selection for higher lactation milk yield alone through selection on increased milk yield in early lactation is likely to have a more deleterious effect on genetic merit for fertility than selection on higher milk yield in late lactation.

The State of Synapses in Fragile X Syndrome

OpenAIRE

Pfeiffer, Brad E.; Huber, Kimberly M.

2009-01-01

Fragile X Syndrome is the most common inherited form of mental retardation and a leading genetic cause of autism. There is increasing evidence in both FXS and other forms of autism that alterations in synapse number, structure and function are associated and contribute to these prevalent diseases. FXS is caused by loss of function of the Fmr1 gene which encodes the RNA binding protein, FMRP. Therefore, FXS is a tractable model to understand synaptic dysfunction in cognitive disorders. FMRP is...

Weather Derivatives and Stochastic Modelling of Temperature

Directory of Open Access Journals (Sweden)

Fred Espen Benth

2011-01-01

Full Text Available We propose a continuous-time autoregressive model for the temperature dynamics with volatility being the product of a seasonal function and a stochastic process. We use the Barndorff-Nielsen and Shephard model for the stochastic volatility. The proposed temperature dynamics is flexible enough to model temperature data accurately, and at the same time being analytically tractable. Futures prices for commonly traded contracts at the Chicago Mercantile Exchange on indices like cooling- and heating-degree days and cumulative average temperatures are computed, as well as option prices on them.

Genetic correlations between body condition scores and fertility in dairy cattle using bivariate random regression models.

Science.gov (United States)

De Haas, Y; Janss, L L G; Kadarmideen, H N

2007-10-01

Genetic correlations between body condition score (BCS) and fertility traits in dairy cattle were estimated using bivariate random regression models. BCS was recorded by the Swiss Holstein Association on 22,075 lactating heifers (primiparous cows) from 856 sires. Fertility data during first lactation were extracted for 40,736 cows. The fertility traits were days to first service (DFS), days between first and last insemination (DFLI), calving interval (CI), number of services per conception (NSPC) and conception rate to first insemination (CRFI). A bivariate model was used to estimate genetic correlations between BCS as a longitudinal trait by random regression components, and daughter's fertility at the sire level as a single lactation measurement. Heritability of BCS was 0.17, and heritabilities for fertility traits were low (0.01-0.08). Genetic correlations between BCS and fertility over the lactation varied from: -0.45 to -0.14 for DFS; -0.75 to 0.03 for DFLI; from -0.59 to -0.02 for CI; from -0.47 to 0.33 for NSPC and from 0.08 to 0.82 for CRFI. These results show (genetic) interactions between fat reserves and reproduction along the lactation trajectory of modern dairy cows, which can be useful in genetic selection as well as in management. Maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in mid lactation when the genetic variance for BCS is largest, and the genetic correlations between BCS and fertility is strongest.

Empirical valence bond models for reactive potential energy surfaces: a parallel multilevel genetic program approach.

Science.gov (United States)

Bellucci, Michael A; Coker, David F

2011-07-28

We describe a new method for constructing empirical valence bond potential energy surfaces using a parallel multilevel genetic program (PMLGP). Genetic programs can be used to perform an efficient search through function space and parameter space to find the best functions and sets of parameters that fit energies obtained by ab initio electronic structure calculations. Building on the traditional genetic program approach, the PMLGP utilizes a hierarchy of genetic programming on two different levels. The lower level genetic programs are used to optimize coevolving populations in parallel while the higher level genetic program (HLGP) is used to optimize the genetic operator probabilities of the lower level genetic programs. The HLGP allows the algorithm to dynamically learn the mutation or combination of mutations that most effectively increase the fitness of the populations, causing a significant increase in the algorithm's accuracy and efficiency. The algorithm's accuracy and efficiency is tested against a standard parallel genetic program with a variety of one-dimensional test cases. Subsequently, the PMLGP is utilized to obtain an accurate empirical valence bond model for proton transfer in 3-hydroxy-gamma-pyrone in gas phase and protic solvent. © 2011 American Institute of Physics

Parameters Calculation of ZnO Surge Arrester Models by Genetic Algorithms

Directory of Open Access Journals (Sweden)

A. Bayadi

2006-09-01

Full Text Available This paper proposes to provide a new technique based on the genetic algorithm to obtain the best possible series of values of the parameters of the ZnO surge arresters models. The validity of the predicted parameters is then checked by comparing the results predicted with the experimental results available in the literature. Using the ATP-EMTP package an application of the arrester model on network system studies is presented and discussed.

Comparing ESC and iPSC?Based Models for Human Genetic Disorders

OpenAIRE

Halevy, Tomer; Urbach, Achia

2014-01-01

Traditionally, human disorders were studied using animal models or somatic cells taken from patients. Such studies enabled the analysis of the molecular mechanisms of numerous disorders, and led to the discovery of new treatments. Yet, these systems are limited or even irrelevant in modeling multiple genetic diseases. The isolation of human embryonic stem cells (ESCs) from diseased blastocysts, the derivation of induced pluripotent stem cells (iPSCs) from patients’ somatic cells, and the ne...

Long-Term Adult Feline Liver Organoid Cultures for Disease Modeling of Hepatic Steatosis

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Hedwig S. Kruitwagen

2017-04-01

Full Text Available Summary: Hepatic steatosis is a highly prevalent liver disease, yet research is hampered by the lack of tractable cellular and animal models. Steatosis also occurs in cats, where it can cause severe hepatic failure. Previous studies demonstrate the potential of liver organoids for modeling genetic diseases. To examine the possibility of using organoids to model steatosis, we established a long-term feline liver organoid culture with adult liver stem cell characteristics and differentiation potential toward hepatocyte-like cells. Next, organoids from mouse, human, dog, and cat liver were provided with fatty acids. Lipid accumulation was observed in all organoids and interestingly, feline liver organoids accumulated more lipid droplets than human organoids. Finally, we demonstrate effects of interference with β-oxidation on lipid accumulation in feline liver organoids. In conclusion, feline liver organoids can be successfully cultured and display a predisposition for lipid accumulation, making them an interesting model in hepatic steatosis research. : In this study Kruitwagen and colleagues establish and characterize a feline liver organoid culture, which has adult stem cell properties and can be differentiated toward hepatocyte-like cells. They propose liver organoids as a tool to model hepatic steatosis and show that feline liver organoids accumulate more lipids than human organoids when provided with excess fatty acids. Keywords: feline liver organoids, adult liver stem cells, hepatic steatosis, disease modeling, feline hepatic lipidosis, species differences

Research on prediction of agricultural machinery total power based on grey model optimized by genetic algorithm

Science.gov (United States)

Xie, Yan; Li, Mu; Zhou, Jin; Zheng, Chang-zheng

2009-07-01

Agricultural machinery total power is an important index to reflex and evaluate the level of agricultural mechanization. It is the power source of agricultural production, and is the main factors to enhance the comprehensive agricultural production capacity expand production scale and increase the income of the farmers. Its demand is affected by natural, economic, technological and social and other "grey" factors. Therefore, grey system theory can be used to analyze the development of agricultural machinery total power. A method based on genetic algorithm optimizing grey modeling process is introduced in this paper. This method makes full use of the advantages of the grey prediction model and characteristics of genetic algorithm to find global optimization. So the prediction model is more accurate. According to data from a province, the GM (1, 1) model for predicting agricultural machinery total power was given based on the grey system theories and genetic algorithm. The result indicates that the model can be used as agricultural machinery total power an effective tool for prediction.

Genetic technologies for extremely thermophilic microorganisms of Sulfolobus, the only genetically tractable genus of crenarchaea

DEFF Research Database (Denmark)

Peng, Nan; Han, Wenyuan; Li, Yingjun

2017-01-01

Archaea represents the third domain of life, with the information-processing machineries more closely resembling those of eukaryotes than the machineries of the bacterial counterparts but sharing metabolic pathways with organisms of Bacteria, the sister prokaryotic phylum. Archaeal organisms also...

Programming strategy for efficient modeling of dynamics in a population of heterogeneous cells.

Science.gov (United States)

Hald, Bjørn Olav; Garkier Hendriksen, Morten; Sørensen, Preben Graae

2013-05-15

Heterogeneity is a ubiquitous property of biological systems. Even in a genetically identical population of a single cell type, cell-to-cell differences are observed. Although the functional behavior of a given population is generally robust, the consequences of heterogeneity are fairly unpredictable. In heterogeneous populations, synchronization of events becomes a cardinal problem-particularly for phase coherence in oscillating systems. The present article presents a novel strategy for construction of large-scale simulation programs of heterogeneous biological entities. The strategy is designed to be tractable, to handle heterogeneity and to handle computational cost issues simultaneously, primarily by writing a generator of the 'model to be simulated'. We apply the strategy to model glycolytic oscillations among thousands of yeast cells coupled through the extracellular medium. The usefulness is illustrated through (i) benchmarking, showing an almost linear relationship between model size and run time, and (ii) analysis of the resulting simulations, showing that contrary to the experimental situation, synchronous oscillations are surprisingly hard to achieve, underpinning the need for tools to study heterogeneity. Thus, we present an efficient strategy to model the biological heterogeneity, neglected by ordinary mean-field models. This tool is well posed to facilitate the elucidation of the physiologically vital problem of synchronization. The complete python code is available as Supplementary Information. bjornhald@gmail.com or pgs@kiku.dk Supplementary data are available at Bioinformatics online.

Poisson versus threshold models for genetic analysis of clinical mastitis in US Holsteins.

Science.gov (United States)

Vazquez, A I; Weigel, K A; Gianola, D; Bates, D M; Perez-Cabal, M A; Rosa, G J M; Chang, Y M

2009-10-01

Typically, clinical mastitis is coded as the presence or absence of disease in a given lactation, and records are analyzed with either linear models or binary threshold models. Because the presence of mastitis may include cows with multiple episodes, there is a loss of information when counts are treated as binary responses. Poisson models are appropriated for random variables measured as the number of events, and although these models are used extensively in studying the epidemiology of mastitis, they have rarely been used for studying the genetic aspects of mastitis. Ordinal threshold models are pertinent for ordered categorical responses; although one can hypothesize that the number of clinical mastitis episodes per animal reflects a continuous underlying increase in mastitis susceptibility, these models have rarely been used in genetic analysis of mastitis. The objective of this study was to compare probit, Poisson, and ordinal threshold models for the genetic evaluation of US Holstein sires for clinical mastitis. Mastitis was measured as a binary trait or as the number of mastitis cases. Data from 44,908 first-parity cows recorded in on-farm herd management software were gathered, edited, and processed for the present study. The cows were daughters of 1,861 sires, distributed over 94 herds. Predictive ability was assessed via a 5-fold cross-validation using 2 loss functions: mean squared error of prediction (MSEP) as the end point and a cost difference function. The heritability estimates were 0.061 for mastitis measured as a binary trait in the probit model and 0.085 and 0.132 for the number of mastitis cases in the ordinal threshold and Poisson models, respectively; because of scale differences, only the probit and ordinal threshold models are directly comparable. Among healthy animals, MSEP was smallest for the probit model, and the cost function was smallest for the ordinal threshold model. Among diseased animals, MSEP and the cost function were smallest

An Adaptive Agent-Based Model of Homing Pigeons: A Genetic Algorithm Approach

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Francis Oloo

2017-01-01

Full Text Available Conventionally, agent-based modelling approaches start from a conceptual model capturing the theoretical understanding of the systems of interest. Simulation outcomes are then used “at the end” to validate the conceptual understanding. In today’s data rich era, there are suggestions that models should be data-driven. Data-driven workflows are common in mathematical models. However, their application to agent-based models is still in its infancy. Integration of real-time sensor data into modelling workflows opens up the possibility of comparing simulations against real data during the model run. Calibration and validation procedures thus become automated processes that are iteratively executed during the simulation. We hypothesize that incorporation of real-time sensor data into agent-based models improves the predictive ability of such models. In particular, that such integration results in increasingly well calibrated model parameters and rule sets. In this contribution, we explore this question by implementing a flocking model that evolves in real-time. Specifically, we use genetic algorithms approach to simulate representative parameters to describe flight routes of homing pigeons. The navigation parameters of pigeons are simulated and dynamically evaluated against emulated GPS sensor data streams and optimised based on the fitness of candidate parameters. As a result, the model was able to accurately simulate the relative-turn angles and step-distance of homing pigeons. Further, the optimised parameters could replicate loops, which are common patterns in flight tracks of homing pigeons. Finally, the use of genetic algorithms in this study allowed for a simultaneous data-driven optimization and sensitivity analysis.

A Comparison of Telephone Genetic Counseling and In-Person Genetic Counseling from the Genetic Counselor's Perspective.

Science.gov (United States)

Burgess, Kelly R; Carmany, Erin P; Trepanier, Angela M

2016-02-01

Growing demand for and limited geographic access to genetic counseling services is increasing the need for alternative service delivery models (SDM) like telephone genetic counseling (TGC). Little research has been done on genetic counselors' perspectives of the practice of TGC. We created an anonymous online survey to assess whether telephone genetic counselors believed the tasks identified in the ABGC (American Board of Genetic Counseling) Practice Analysis were performed similarly or differently in TGC compared to in person genetic counseling (IPGC). If there were differences noted, we sought to determine the nature of the differences and if additional training might be needed to address them. Eighty eight genetic counselors with experience in TGC completed some or all of the survey. Respondents identified differences in 13 (14.8%) of the 88 tasks studied. The tasks identified as most different in TGC were: "establishing rapport through verbal and nonverbal interactions" (60.2%; 50/83 respondents identified the task as different), "recognizing factors affecting the counseling interaction" (47.8%; 32/67), "assessing client/family emotions, support, etc." (40.1%; 27/66) and "educating clients about basic genetic concepts" (35.6%; 26/73). A slight majority (53.8%; 35/65) felt additional training was needed to communicate information without visual aids and more effectively perform psychosocial assessments. In summary, although a majority of genetic counseling tasks are performed similarly between TGC and IPGC, TGC counselors recognize that specific training in the TGC model may be needed to address the key differences.

Understanding the Spatial Scale of Genetic Connectivity at Sea: Unique Insights from a Land Fish and a Meta-Analysis.

Directory of Open Access Journals (Sweden)

Georgina M Cooke

Full Text Available Quantifying the spatial scale of population connectivity is important for understanding the evolutionary potential of ecologically divergent populations and for designing conservation strategies to preserve those populations. For marine organisms like fish, the spatial scale of connectivity is generally set by a pelagic larval phase. This has complicated past estimates of connectivity because detailed information on larval movements are difficult to obtain. Genetic approaches provide a tractable alternative and have the added benefit of estimating directly the reproductive isolation of populations. In this study, we leveraged empirical estimates of genetic differentiation among populations with simulations and a meta-analysis to provide a general estimate of the spatial scale of genetic connectivity in marine environments. We used neutral genetic markers to first quantify the genetic differentiation of ecologically-isolated adult populations of a land dwelling fish, the Pacific leaping blenny (Alticus arnoldorum, where marine larval dispersal is the only probable means of connectivity among populations. We then compared these estimates to simulations of a range of marine dispersal scenarios and to collated FST and distance data from the literature for marine fish across diverse spatial scales. We found genetic connectivity at sea was extensive among marine populations and in the case of A. arnoldorum, apparently little affected by the presence of ecological barriers. We estimated that ~5000 km (with broad confidence intervals ranging from 810-11,692 km was the spatial scale at which evolutionarily meaningful barriers to gene flow start to occur at sea, although substantially shorter distances are also possible for some taxa. In general, however, such a large estimate of connectivity has important implications for the evolutionary and conservation potential of many marine fish communities.

Genetic coding and united-hypercomplex systems in the models of algebraic biology.

Science.gov (United States)

Petoukhov, Sergey V

2017-08-01

Structured alphabets of DNA and RNA in their matrix form of representations are connected with Walsh functions and a new type of systems of multidimensional numbers. This type generalizes systems of complex numbers and hypercomplex numbers, which serve as the basis of mathematical natural sciences and many technologies. The new systems of multi-dimensional numbers have interesting mathematical properties and are called in a general case as "systems of united-hypercomplex numbers" (or briefly "U-hypercomplex numbers"). They can be widely used in models of multi-parametrical systems in the field of algebraic biology, artificial life, devices of biological inspired artificial intelligence, etc. In particular, an application of U-hypercomplex numbers reveals hidden properties of genetic alphabets under cyclic permutations in their doublets and triplets. A special attention is devoted to the author's hypothesis about a multi-linguistic in DNA-sequences in a relation with an ensemble of U-numerical sub-alphabets. Genetic multi-linguistic is considered as an important factor to provide noise-immunity properties of the multi-channel genetic coding. Our results attest to the conformity of the algebraic properties of the U-numerical systems with phenomenological properties of the DNA-alphabets and with the complementary device of the double DNA-helix. It seems that in the modeling field of algebraic biology the genetic-informational organization of living bodies can be considered as a set of united-hypercomplex numbers in some association with the famous slogan of Pythagoras "the numbers rule the world". Copyright © 2017 Elsevier B.V. All rights reserved.

Deficits in fine motor skills in a genetic animal model of ADHD

Directory of Open Access Journals (Sweden)

Qian Yu

2010-09-01

Full Text Available Abstract Background In an attempt to model some behavioral aspects of Attention Deficit/Hyperactivity Disorder (ADHD, we examined whether an existing genetic animal model of ADHD is valid for investigating not only locomotor hyperactivity, but also more complex motor coordination problems displayed by the majority of children with ADHD. Methods We subjected young adolescent Spontaneously Hypertensive Rats (SHRs, the most commonly used genetic animal model of ADHD, to a battery of tests for motor activity, gross motor coordination, and skilled reaching. Wistar (WIS rats were used as controls. Results Similar to children with ADHD, young adolescent SHRs displayed locomotor hyperactivity in a familiar, but not in a novel environment. They also had lower performance scores in a complex skilled reaching task when compared to WIS rats, especially in the most sensitive measure of skilled performance (i.e., single attempt success. In contrast, their gross motor performance on a Rota-Rod test was similar to that of WIS rats. Conclusion The results support the notion that the SHR strain is a useful animal model system to investigate potential molecular mechanisms underlying fine motor skill problems in children with ADHD.

OPTIMIZATION OF LAND USE SUITABILITY FOR AGRICULTURE USING INTEGRATED GEOSPATIAL MODEL AND GENETIC ALGORITHMS

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S. B. Mansor

2012-08-01

Full Text Available In this study, a geospatial model for land use allocation was developed from the view of simulating the biological autonomous adaptability to environment and the infrastructural preference. The model was developed based on multi-agent genetic algorithm. The model was customized to accommodate the constraint set for the study area, namely the resource saving and environmental-friendly. The model was then applied to solve the practical multi-objective spatial optimization allocation problems of land use in the core region of Menderjan Basin in Iran. The first task was to study the dominant crops and economic suitability evaluation of land. Second task was to determine the fitness function for the genetic algorithms. The third objective was to optimize the land use map using economical benefits. The results has indicated that the proposed model has much better performance for solving complex multi-objective spatial optimization allocation problems and it is a promising method for generating land use alternatives for further consideration in spatial decision-making.

Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers.

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Guosheng Su

Full Text Available Non-additive genetic variation is usually ignored when genome-wide markers are used to study the genetic architecture and genomic prediction of complex traits in human, wild life, model organisms or farm animals. However, non-additive genetic effects may have an important contribution to total genetic variation of complex traits. This study presented a genomic BLUP model including additive and non-additive genetic effects, in which additive and non-additive genetic relation matrices were constructed from information of genome-wide dense single nucleotide polymorphism (SNP markers. In addition, this study for the first time proposed a method to construct dominance relationship matrix using SNP markers and demonstrated it in detail. The proposed model was implemented to investigate the amounts of additive genetic, dominance and epistatic variations, and assessed the accuracy and unbiasedness of genomic predictions for daily gain in pigs. In the analysis of daily gain, four linear models were used: 1 a simple additive genetic model (MA, 2 a model including both additive and additive by additive epistatic genetic effects (MAE, 3 a model including both additive and dominance genetic effects (MAD, and 4 a full model including all three genetic components (MAED. Estimates of narrow-sense heritability were 0.397, 0.373, 0.379 and 0.357 for models MA, MAE, MAD and MAED, respectively. Estimated dominance variance and additive by additive epistatic variance accounted for 5.6% and 9.5% of the total phenotypic variance, respectively. Based on model MAED, the estimate of broad-sense heritability was 0.506. Reliabilities of genomic predicted breeding values for the animals without performance records were 28.5%, 28.8%, 29.2% and 29.5% for models MA, MAE, MAD and MAED, respectively. In addition, models including non-additive genetic effects improved unbiasedness of genomic predictions.

Evolution of host innate defence: insights from Caenorhabditis elegans and primitive invertebrates.

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Irazoqui, Javier E; Urbach, Jonathan M; Ausubel, Frederick M

2010-01-01

The genetically tractable model organism Caenorhabditis elegans was first used to model bacterial virulence in vivo a decade ago. Since then, great strides have been made in identifying the host response pathways that are involved in its defence against infection. Strikingly, C. elegans seems to detect, and respond to, infection without the involvement of its homologue of Toll-like receptors, in contrast to the well-established role for these proteins in innate immunity in mammals. What, therefore, do we know about host defence mechanisms in C. elegans and what can they tell us about innate immunity in higher organisms?

Evolution of host innate defence: insights from C. elegans and primitive invertebrates

Science.gov (United States)

Irazoqui, Javier E.; Urbach, Jonathan M.; Ausubel, Frederick M.

2010-01-01

Preface The genetically tractable model organism Caenorhabditis elegans was first used to model bacterial virulence in vivo a decade ago. Since then, great strides have been made in the identification of host response pathways that are involved in the defence against infection. Strikingly, C. elegans seems to detect and respond to infection without the involvement of its Toll-like receptor homologue, in contrast to the well-established role for these proteins in innate immunity in mammals. What, therefore, do we know about host defence mechanisms in C. elegans, and what can they tell us about innate immunity in higher organisms? PMID:20029447

Using Genetically Engineered Animal Models in the Postgenomic Era to Understand Gene Function in Alcoholism

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Reilly, Matthew T.; Harris, R. Adron; Noronha, Antonio

2012-01-01

Over the last 50 years, researchers have made substantial progress in identifying genetic variations that underlie the complex phenotype of alcoholism. Not much is known, however, about how this genetic variation translates into altered biological function. Genetic animal models recapitulating specific characteristics of the human condition have helped elucidate gene function and the genetic basis of disease. In particular, major advances have come from the ability to manipulate genes through a variety of genetic technologies that provide an unprecedented capacity to determine gene function in the living organism and in alcohol-related behaviors. Even newer genetic-engineering technologies have given researchers the ability to control when and where a specific gene or mutation is activated or deleted, allowing investigators to narrow the role of the gene’s function to circumscribed neural pathways and across development. These technologies are important for all areas of neuroscience, and several public and private initiatives are making a new generation of genetic-engineering tools available to the scientific community at large. Finally, high-throughput “next-generation sequencing” technologies are set to rapidly increase knowledge of the genome, epigenome, and transcriptome, which, combined with genetically engineered mouse mutants, will enhance insight into biological function. All of these resources will provide deeper insight into the genetic basis of alcoholism. PMID:23134044

The BioC-BioGRID corpus: full text articles annotated for curation of protein-protein and genetic interactions.

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Islamaj Dogan, Rezarta; Kim, Sun; Chatr-Aryamontri, Andrew; Chang, Christie S; Oughtred, Rose; Rust, Jennifer; Wilbur, W John; Comeau, Donald C; Dolinski, Kara; Tyers, Mike

2017-01-01

A great deal of information on the molecular genetics and biochemistry of model organisms has been reported in the scientific literature. However, this data is typically described in free text form and is not readily amenable to computational analyses. To this end, the BioGRID database systematically curates the biomedical literature for genetic and protein interaction data. This data is provided in a standardized computationally tractable format and includes structured annotation of experimental evidence. BioGRID curation necessarily involves substantial human effort by expert curators who must read each publication to extract the relevant information. Computational text-mining methods offer the potential to augment and accelerate manual curation. To facilitate the development of practical text-mining strategies, a new challenge was organized in BioCreative V for the BioC task, the collaborative Biocurator Assistant Task. This was a non-competitive, cooperative task in which the participants worked together to build BioC-compatible modules into an integrated pipeline to assist BioGRID curators. As an integral part of this task, a test collection of full text articles was developed that contained both biological entity annotations (gene/protein and organism/species) and molecular interaction annotations (protein-protein and genetic interactions (PPIs and GIs)). This collection, which we call the BioC-BioGRID corpus, was annotated by four BioGRID curators over three rounds of annotation and contains 120 full text articles curated in a dataset representing two major model organisms, namely budding yeast and human. The BioC-BioGRID corpus contains annotations for 6409 mentions of genes and their Entrez Gene IDs, 186 mentions of organism names and their NCBI Taxonomy IDs, 1867 mentions of PPIs and 701 annotations of PPI experimental evidence statements, 856 mentions of GIs and 399 annotations of GI evidence statements. The purpose, characteristics and possible future

Using zebrafish as a model to study the role of epigenetics in hearing loss.

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He, Yingzi; Bao, Beier; Li, Huawei

2017-09-01

The rapid progress of bioinformatics and high-throughput screening techniques in recent years has led to the identification of many candidate genes and small-molecule drugs that have the potential to make significant contributions to our understanding of the developmental and pathological processes of hearing, but it remains unclear how these genes and regulatory factors are coordinated. Increasing evidence suggests that epigenetic mechanisms are essential for establishing gene expression profiles and likely play an important role in the development of inner ear and in the pathology of hearing-associated diseases. Zebrafish are a valuable and tractable in vivo model organism for monitoring changes in the epigenome and for identifying new epigenetic processes and drug molecules that can influence vertebrate development. Areas covered: In this review, the authors focus on zebrafish as a model to summarize recent findings concerning the roles of epigenetics in the development, regeneration, and protection of hair cells. Expert opinion: Using the zebrafish model in combination with high-throughput screening and genome-editing technologies to investigate the function of epigenetics in hearing is crucial to help us better understand the molecular and genetic mechanisms of auditory development and function. It will also contribute to the development of new strategies to restore hearing loss.

Comparison of whole-genome prediction models for traits with contrasting genetic architecture in a diversity panel of maize inbred lines

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Riedelsheimer Christian

2012-09-01

Full Text Available Abstract Background There is increasing empirical evidence that whole-genome prediction (WGP is a powerful tool for predicting line and hybrid performance in maize. However, there is a lack of knowledge about the sensitivity of WGP models towards the genetic architecture of the trait. Whereas previous studies exclusively focused on highly polygenic traits, important agronomic traits such as disease resistances, nutrifunctional or climate adaptational traits have a genetic architecture which is either much less complex or unknown. For such cases, information about model robustness and guidelines for model selection are lacking. Here, we compared five WGP models with different assumptions about the distribution of the underlying genetic effects. As contrasting model traits, we chose three highly polygenic agronomic traits and three metabolites each with a major QTL explaining 22 to 30% of the genetic variance in a panel of 289 diverse maize inbred lines genotyped with 56,110 SNPs. Results We found the five WGP models to be remarkable robust towards trait architecture with the largest differences in prediction accuracies ranging between 0.05 and 0.14 for the same trait, most likely as the result of the high level of linkage disequilibrium prevailing in elite maize germplasm. Whereas RR-BLUP performed best for the agronomic traits, it was inferior to LASSO or elastic net for the three metabolites. We found the approach of genome partitioning of genetic variance, first applied in human genetics, as useful in guiding the breeder which model to choose, if prior knowledge of the trait architecture is lacking. Conclusions Our results suggest that in diverse germplasm of elite maize inbred lines with a high level of LD, WGP models differ only slightly in their accuracies, irrespective of the number and effects of QTL found in previous linkage or association mapping studies. However, small gains in prediction accuracies can be achieved if the WGP model is

Molecular Population Genetics.

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Casillas, Sònia; Barbadilla, Antonio

2017-03-01

Molecular population genetics aims to explain genetic variation and molecular evolution from population genetics principles. The field was born 50 years ago with the first measures of genetic variation in allozyme loci, continued with the nucleotide sequencing era, and is currently in the era of population genomics. During this period, molecular population genetics has been revolutionized by progress in data acquisition and theoretical developments. The conceptual elegance of the neutral theory of molecular evolution or the footprint carved by natural selection on the patterns of genetic variation are two examples of the vast number of inspiring findings of population genetics research. Since the inception of the field, Drosophila has been the prominent model species: molecular variation in populations was first described in Drosophila and most of the population genetics hypotheses were tested in Drosophila species. In this review, we describe the main concepts, methods, and landmarks of molecular population genetics, using the Drosophila model as a reference. We describe the different genetic data sets made available by advances in molecular technologies, and the theoretical developments fostered by these data. Finally, we review the results and new insights provided by the population genomics approach, and conclude by enumerating challenges and new lines of inquiry posed by increasingly large population scale sequence data. Copyright © 2017 Casillas and Barbadilla.

Genetic Algorithm-Based Model Order Reduction of Aeroservoelastic Systems with Consistant States

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Zhu, Jin; Wang, Yi; Pant, Kapil; Suh, Peter M.; Brenner, Martin J.

2017-01-01

This paper presents a model order reduction framework to construct linear parameter-varying reduced-order models of flexible aircraft for aeroservoelasticity analysis and control synthesis in broad two-dimensional flight parameter space. Genetic algorithms are used to automatically determine physical states for reduction and to generate reduced-order models at grid points within parameter space while minimizing the trial-and-error process. In addition, balanced truncation for unstable systems is used in conjunction with the congruence transformation technique to achieve locally optimal realization and weak fulfillment of state consistency across the entire parameter space. Therefore, aeroservoelasticity reduced-order models at any flight condition can be obtained simply through model interpolation. The methodology is applied to the pitch-plant model of the X-56A Multi-Use Technology Testbed currently being tested at NASA Armstrong Flight Research Center for flutter suppression and gust load alleviation. The present studies indicate that the reduced-order model with more than 12× reduction in the number of states relative to the original model is able to accurately predict system response among all input-output channels. The genetic-algorithm-guided approach exceeds manual and empirical state selection in terms of efficiency and accuracy. The interpolated aeroservoelasticity reduced order models exhibit smooth pole transition and continuously varying gains along a set of prescribed flight conditions, which verifies consistent state representation obtained by congruence transformation. The present model order reduction framework can be used by control engineers for robust aeroservoelasticity controller synthesis and novel vehicle design.

Modeling of genetic gain for single traits from marker-assisted seedling selection in clonally propagated crops

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Ru, Sushan; Hardner, Craig; Carter, Patrick A; Evans, Kate; Main, Dorrie; Peace, Cameron

2016-01-01

Seedling selection identifies superior seedlings as candidate cultivars based on predicted genetic potential for traits of interest. Traditionally, genetic potential is determined by phenotypic evaluation. With the availability of DNA tests for some agronomically important traits, breeders have the opportunity to include DNA information in their seedling selection operations—known as marker-assisted seedling selection. A major challenge in deploying marker-assisted seedling selection in clonally propagated crops is a lack of knowledge in genetic gain achievable from alternative strategies. Existing models based on additive effects considering seed-propagated crops are not directly relevant for seedling selection of clonally propagated crops, as clonal propagation captures all genetic effects, not just additive. This study modeled genetic gain from traditional and various marker-based seedling selection strategies on a single trait basis through analytical derivation and stochastic simulation, based on a generalized seedling selection scheme of clonally propagated crops. Various trait-test scenarios with a range of broad-sense heritability and proportion of genotypic variance explained by DNA markers were simulated for two populations with different segregation patterns. Both derived and simulated results indicated that marker-based strategies tended to achieve higher genetic gain than phenotypic seedling selection for a trait where the proportion of genotypic variance explained by marker information was greater than the broad-sense heritability. Results from this study provides guidance in optimizing genetic gain from seedling selection for single traits where DNA tests providing marker information are available. PMID:27148453

Automated Test Assembly for Cognitive Diagnosis Models Using a Genetic Algorithm

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Finkelman, Matthew; Kim, Wonsuk; Roussos, Louis A.

2009-01-01

Much recent psychometric literature has focused on cognitive diagnosis models (CDMs), a promising class of instruments used to measure the strengths and weaknesses of examinees. This article introduces a genetic algorithm to perform automated test assembly alongside CDMs. The algorithm is flexible in that it can be applied whether the goal is to…

Strategies for MCMC computation in quantitative genetics

DEFF Research Database (Denmark)

Waagepetersen, Rasmus; Ibanez, Noelia; Sorensen, Daniel

2006-01-01

Given observations of a trait and a pedigree for a group of animals, the basic model in quantitative genetics is a linear mixed model with genetic random effects. The correlation matrix of the genetic random effects is determined by the pedigree and is typically very highdimensional but with a sp......Given observations of a trait and a pedigree for a group of animals, the basic model in quantitative genetics is a linear mixed model with genetic random effects. The correlation matrix of the genetic random effects is determined by the pedigree and is typically very highdimensional...

Genetic analysis of partial egg production records in Japanese quail using random regression models.

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Abou Khadiga, G; Mahmoud, B Y F; Farahat, G S; Emam, A M; El-Full, E A

2017-08-01

The main objectives of this study were to detect the most appropriate random regression model (RRM) to fit the data of monthly egg production in 2 lines (selected and control) of Japanese quail and to test the consistency of different criteria of model choice. Data from 1,200 female Japanese quails for the first 5 months of egg production from 4 consecutive generations of an egg line selected for egg production in the first month (EP1) was analyzed. Eight RRMs with different orders of Legendre polynomials were compared to determine the proper model for analysis. All criteria of model choice suggested that the adequate model included the second-order Legendre polynomials for fixed effects, and the third-order for additive genetic effects and permanent environmental effects. Predictive ability of the best model was the highest among all models (ρ = 0.987). According to the best model fitted to the data, estimates of heritability were relatively low to moderate (0.10 to 0.17) showed a descending pattern from the first to the fifth month of production. A similar pattern was observed for permanent environmental effects with greater estimates in the first (0.36) and second (0.23) months of production than heritability estimates. Genetic correlations between separate production periods were higher (0.18 to 0.93) than their phenotypic counterparts (0.15 to 0.87). The superiority of the selected line over the control was observed through significant (P egg production in earlier ages (first and second months) than later ones. A methodology based on random regression animal models can be recommended for genetic evaluation of egg production in Japanese quail. © 2017 Poultry Science Association Inc.

Genetic correlations among body condition score, yield and fertility in multiparous cows using random regression models

OpenAIRE

Bastin, Catherine; Gillon, Alain; Massart, Xavier; Bertozzi, Carlo; Vanderick, Sylvie; Gengler, Nicolas

2010-01-01

Genetic correlations between body condition score (BCS) in lactation 1 to 3 and four economically important traits (days open, 305-days milk, fat, and protein yields recorded in the first 3 lactations) were estimated on about 12,500 Walloon Holstein cows using 4-trait random regression models. Results indicated moderate favorable genetic correlations between BCS and days open (from -0.46 to -0.62) and suggested the use of BCS for indirect selection on fertility. However, unfavorable genetic c...

The reduced kinome of Ostreococcus tauri: core eukaryotic signalling components in a tractable model species.

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Hindle, Matthew M; Martin, Sarah F; Noordally, Zeenat B; van Ooijen, Gerben; Barrios-Llerena, Martin E; Simpson, T Ian; Le Bihan, Thierry; Millar, Andrew J

2014-08-02

The current knowledge of eukaryote signalling originates from phenotypically diverse organisms. There is a pressing need to identify conserved signalling components among eukaryotes, which will lead to the transfer of knowledge across kingdoms. Two useful properties of a eukaryote model for signalling are (1) reduced signalling complexity, and (2) conservation of signalling components. The alga Ostreococcus tauri is described as the smallest free-living eukaryote. With less than 8,000 genes, it represents a highly constrained genomic palette. Our survey revealed 133 protein kinases and 34 protein phosphatases (1.7% and 0.4% of the proteome). We conducted phosphoproteomic experiments and constructed domain structures and phylogenies for the catalytic protein-kinases. For each of the major kinases families we review the completeness and divergence of O. tauri representatives in comparison to the well-studied kinomes of the laboratory models Arabidopsis thaliana and Saccharomyces cerevisiae, and of Homo sapiens. Many kinase clades in O. tauri were reduced to a single member, in preference to the loss of family diversity, whereas TKL and ABC1 clades were expanded. We also identified kinases that have been lost in A. thaliana but retained in O. tauri. For three, contrasting eukaryotic pathways - TOR, MAPK, and the circadian clock - we established the subset of conserved components and demonstrate conserved sites of substrate phosphorylation and kinase motifs. We conclude that O. tauri satisfies our two central requirements. Several of its kinases are more closely related to H. sapiens orthologs than S. cerevisiae is to H. sapiens. The greatly reduced kinome of O. tauri is therefore a suitable model for signalling in free-living eukaryotes.

Genetic basis of hindlimb loss in a naturally occurring vertebrate model

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Emily K. Don

2016-03-01

Full Text Available Here we genetically characterise pelvic finless, a naturally occurring model of hindlimb loss in zebrafish that lacks pelvic fin structures, which are homologous to tetrapod hindlimbs, but displays no other abnormalities. Using a hybrid positional cloning and next generation sequencing approach, we identified mutations in the nuclear localisation signal (NLS of T-box transcription factor 4 (Tbx4 that impair nuclear localisation of the protein, resulting in altered gene expression patterns during pelvic fin development and the failure of pelvic fin development. Using a TALEN-induced tbx4 knockout allele we confirm that mutations within the Tbx4 NLS (A78V; G79A are sufficient to disrupt pelvic fin development. By combining histological, genetic, and cellular approaches we show that the hindlimb initiation gene tbx4 has an evolutionarily conserved, essential role in pelvic fin development. In addition, our novel viable model of hindlimb deficiency is likely to facilitate the elucidation of the detailed molecular mechanisms through which Tbx4 functions during pelvic fin and hindlimb development.

A software tool to model genetic regulatory networks. Applications to the modeling of threshold phenomena and of spatial patterning in Drosophila.

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Rui Dilão

Full Text Available We present a general methodology in order to build mathematical models of genetic regulatory networks. This approach is based on the mass action law and on the Jacob and Monod operon model. The mathematical models are built symbolically by the Mathematica software package GeneticNetworks. This package accepts as input the interaction graphs of the transcriptional activators and repressors of a biological process and, as output, gives the mathematical model in the form of a system of ordinary differential equations. All the relevant biological parameters are chosen automatically by the software. Within this framework, we show that concentration dependent threshold effects in biology emerge from the catalytic properties of genes and its associated conservation laws. We apply this methodology to the segment patterning in Drosophila early development and we calibrate the genetic transcriptional network responsible for the patterning of the gap gene proteins Hunchback and Knirps, along the antero-posterior axis of the Drosophila embryo. In this approach, the zygotically produced proteins Hunchback and Knirps do not diffuse along the antero-posterior axis of the embryo of Drosophila, developing a spatial pattern due to concentration dependent thresholds. This shows that patterning at the gap genes stage can be explained by the concentration gradients along the embryo of the transcriptional regulators.

Dependent defaults and losses with factor copula models

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Ackerer Damien

2017-12-01

Full Text Available We present a class of flexible and tractable static factor models for the term structure of joint default probabilities, the factor copula models. These high-dimensional models remain parsimonious with paircopula constructions, and nest many standard models as special cases. The loss distribution of a portfolio of contingent claims can be exactly and efficiently computed when individual losses are discretely supported on a finite grid. Numerical examples study the key features affecting the loss distribution and multi-name credit derivatives prices. An empirical exercise illustrates the flexibility of our approach by fitting credit index tranche prices.

The true meaning of 'exotic species' as a model for genetically engineered organisms.

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Regal, P J

1993-03-15

The exotic or non-indigenous species model for deliberately introduced genetically engineered organisms (GEOs) has often been misunderstood or misrepresented. Yet proper comparisons of of ecologically competent GEOs to the patterns of adaptation of introduced species have been highly useful among scientists in attempting to determine how to apply biological theory to specific GEO risk issues, and in attempting to define the probabilities and scale of ecological risks with GEOs. In truth, the model predicts that most projects may be environmentally safe, but a significant minority may be very risky. The model includes a history of institutional follies that also should remind workers of the danger of oversimplifying biological issues, and warn against repeating the sorts of professional misjudgements that have too often been made in introducing organisms to new settings. We once expected that the non-indigenous species model would be refined by more analysis of species eruptions, ecological genetics, and the biology of select GEOs themselves, as outlined. But there has been political resistance to the effective regulation of GEOs, and a bureaucratic tendency to focus research agendas on narrow data collection. Thus there has been too little promotion by responsible agencies of studies to provide the broad conceptual base for truly science-based regulation. In its presently unrefined state, the non-indigenous species comparison would overestimate the risks of GEOs if it were (mis)applied to genetically disrupted, ecologically crippled GEOs, but in some cases of wild-type organisms with novel engineered traits, it could greatly underestimate the risks. Further analysis is urgently needed.

A modified Wright-Fisher model that incorporates Ne: A variant of the standard model with increased biological realism and reduced computational complexity.

Science.gov (United States)

Zhao, Lei; Gossmann, Toni I; Waxman, David

2016-03-21

achieved good accuracy in all cases considered. In summary, the present work extends the realism and tractability of an important model of evolutionary biology and population genetics. Copyright © 2016 Elsevier Ltd. All rights reserved.

Ripple-Spreading Network Model Optimization by Genetic Algorithm

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Xiao-Bing Hu

2013-01-01

Full Text Available Small-world and scale-free properties are widely acknowledged in many real-world complex network systems, and many network models have been developed to capture these network properties. The ripple-spreading network model (RSNM is a newly reported complex network model, which is inspired by the natural ripple-spreading phenomenon on clam water surface. The RSNM exhibits good potential for describing both spatial and temporal features in the development of many real-world networks where the influence of a few local events spreads out through nodes and then largely determines the final network topology. However, the relationships between ripple-spreading related parameters (RSRPs of RSNM and small-world and scale-free topologies are not as obvious or straightforward as in many other network models. This paper attempts to apply genetic algorithm (GA to tune the values of RSRPs, so that the RSNM may generate these two most important network topologies. The study demonstrates that, once RSRPs are properly tuned by GA, the RSNM is capable of generating both network topologies and therefore has a great flexibility to study many real-world complex network systems.

"Glowing head" mice: a genetic tool enabling reliable preclinical image-based evaluation of cancers in immunocompetent allografts.

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Chi-Ping Day

Full Text Available Preclinical therapeutic assessment currently relies on the growth response of established human cell lines xenografted into immunocompromised mice, a strategy that is generally not predictive of clinical outcomes. Immunocompetent genetically engineered mouse (GEM-derived tumor allograft models offer highly tractable preclinical alternatives and facilitate analysis of clinically promising immunomodulatory agents. Imageable reporters are essential for accurately tracking tumor growth and response, particularly for metastases. Unfortunately, reporters such as luciferase and GFP are foreign antigens in immunocompetent mice, potentially hindering tumor growth and confounding therapeutic responses. Here we assessed the value of reporter-tolerized GEMs as allograft recipients by targeting minimal expression of a luciferase-GFP fusion reporter to the anterior pituitary gland (dubbed the "Glowing Head" or GH mouse. The luciferase-GFP reporter expressed in tumor cells induced adverse immune responses in wildtype mouse, but not in GH mouse, as transplantation hosts. The antigenicity of optical reporters resulted in a decrease in both the growth and metastatic potential of the labeled tumor in wildtype mice as compared to the GH mice. Moreover, reporter expression can also alter the tumor response to chemotherapy or targeted therapy in a context-dependent manner. Thus the GH mice and experimental approaches vetted herein provide concept validation and a strategy for effective, reproducible preclinical evaluation of growth and response kinetics for traceable tumors.

Retracing the evolutionary path that led to flea-borne transmission of Yersinia pestis.

Science.gov (United States)

Sun, Yi-Cheng; Jarrett, Clayton O; Bosio, Christopher F; Hinnebusch, B Joseph

2014-05-14

Yersinia pestis is an arthropod-borne bacterial pathogen that evolved recently from Yersinia pseudotuberculosis, an enteric pathogen transmitted via the fecal-oral route. This radical ecological transition can be attributed to a few discrete genetic changes from a still-extant recent ancestor, thus providing a tractable case study in pathogen evolution and emergence. Here, we determined the genetic and mechanistic basis of the evolutionary adaptation of Y. pestis to flea-borne transmission. Remarkably, only four minor changes in the bacterial progenitor, representing one gene gain and three gene losses, enabled transmission by flea vectors. All three loss-of-function mutations enhanced cyclic-di-GMP-mediated bacterial biofilm formation in the flea foregut, which greatly increased transmissibility. Our results suggest a step-wise evolutionary model in which Y. pestis emerged as a flea-borne clone, with each genetic change incrementally reinforcing the transmission cycle. The model conforms well to the ecological theory of adaptive radiation. Copyright © 2014 Elsevier Inc. All rights reserved.

Risk adjustment model of credit life insurance using a genetic algorithm

Science.gov (United States)

Saputra, A.; Sukono; Rusyaman, E.

2018-03-01

In managing the risk of credit life insurance, insurance company should acknowledge the character of the risks to predict future losses. Risk characteristics can be learned in a claim distribution model. There are two standard approaches in designing the distribution model of claims over the insurance period i.e, collective risk model and individual risk model. In the collective risk model, the claim arises when risk occurs is called individual claim, accumulation of individual claim during a period of insurance is called an aggregate claim. The aggregate claim model may be formed by large model and a number of individual claims. How the measurement of insurance risk with the premium model approach and whether this approach is appropriate for estimating the potential losses occur in the future. In order to solve the problem Genetic Algorithm with Roulette Wheel Selection is used.

Bayesian Modeling for Genetic Anticipation in Presence of Mutational Heterogeneity: A Case Study in Lynch Syndrome

DEFF Research Database (Denmark)

Boonstra, Philip S; Mukherjee, Bhramar; Taylor, Jeremy M G

2011-01-01

Summary Genetic anticipation, described by earlier age of onset (AOO) and more aggressive symptoms in successive generations, is a phenomenon noted in certain hereditary diseases. Its extent may vary between families and/or between mutation subtypes known to be associated with the disease phenotype....... In this article, we posit a Bayesian approach to infer genetic anticipation under flexible random effects models for censored data that capture the effect of successive generations on AOO. Primary interest lies in the random effects. Misspecifying the distribution of random effects may result in incorrect...... to cause hereditary nonpolyposis colorectal cancer, also called Lynch syndrome (LS). We find evidence for a decrease in AOO between generations in this article. Our model predicts family-level anticipation effects that are potentially useful in genetic counseling clinics for high-risk families....

A genetic-algorithm-aided stochastic optimization model for regional air quality management under uncertainty.

Science.gov (United States)

Qin, Xiaosheng; Huang, Guohe; Liu, Lei

2010-01-01

A genetic-algorithm-aided stochastic optimization (GASO) model was developed in this study for supporting regional air quality management under uncertainty. The model incorporated genetic algorithm (GA) and Monte Carlo simulation techniques into a general stochastic chance-constrained programming (CCP) framework and allowed uncertainties in simulation and optimization model parameters to be considered explicitly in the design of least-cost strategies. GA was used to seek the optimal solution of the management model by progressively evaluating the performances of individual solutions. Monte Carlo simulation was used to check the feasibility of each solution. A management problem in terms of regional air pollution control was studied to demonstrate the applicability of the proposed method. Results of the case study indicated the proposed model could effectively communicate uncertainties into the optimization process and generate solutions that contained a spectrum of potential air pollutant treatment options with risk and cost information. Decision alternatives could be obtained by analyzing tradeoffs between the overall pollutant treatment cost and the system-failure risk due to inherent uncertainties.

Genetic analysis of body weights of individually fed beef bulls in South Africa using random regression models.

Science.gov (United States)

Selapa, N W; Nephawe, K A; Maiwashe, A; Norris, D

2012-02-08

The aim of this study was to estimate genetic parameters for body weights of individually fed beef bulls measured at centralized testing stations in South Africa using random regression models. Weekly body weights of Bonsmara bulls (N = 2919) tested between 1999 and 2003 were available for the analyses. The model included a fixed regression of the body weights on fourth-order orthogonal Legendre polynomials of the actual days on test (7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, and 84) for starting age and contemporary group effects. Random regressions on fourth-order orthogonal Legendre polynomials of the actual days on test were included for additive genetic effects and additional uncorrelated random effects of the weaning-herd-year and the permanent environment of the animal. Residual effects were assumed to be independently distributed with heterogeneous variance for each test day. Variance ratios for additive genetic, permanent environment and weaning-herd-year for weekly body weights at different test days ranged from 0.26 to 0.29, 0.37 to 0.44 and 0.26 to 0.34, respectively. The weaning-herd-year was found to have a significant effect on the variation of body weights of bulls despite a 28-day adjustment period. Genetic correlations amongst body weights at different test days were high, ranging from 0.89 to 1.00. Heritability estimates were comparable to literature using multivariate models. Therefore, random regression model could be applied in the genetic evaluation of body weight of individually fed beef bulls in South Africa.

Asymmetrical Damage Partitioning in Bacteria: A Model for the Evolution of Stochasticity, Determinism, and Genetic Assimilation.

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Chao, Lin; Rang, Camilla Ulla; Proenca, Audrey Menegaz; Chao, Jasper Ubirajara

2016-01-01

Non-genetic phenotypic variation is common in biological organisms. The variation is potentially beneficial if the environment is changing. If the benefit is large, selection can favor the evolution of genetic assimilation, the process by which the expression of a trait is transferred from environmental to genetic control. Genetic assimilation is an important evolutionary transition, but it is poorly understood because the fitness costs and benefits of variation are often unknown. Here we show that the partitioning of damage by a mother bacterium to its two daughters can evolve through genetic assimilation. Bacterial phenotypes are also highly variable. Because gene-regulating elements can have low copy numbers, the variation is attributed to stochastic sampling. Extant Escherichia coli partition asymmetrically and deterministically more damage to the old daughter, the one receiving the mother's old pole. By modeling in silico damage partitioning in a population, we show that deterministic asymmetry is advantageous because it increases fitness variance and hence the efficiency of natural selection. However, we find that symmetrical but stochastic partitioning can be similarly beneficial. To examine why bacteria evolved deterministic asymmetry, we modeled the effect of damage anchored to the mother's old pole. While anchored damage strengthens selection for asymmetry by creating additional fitness variance, it has the opposite effect on symmetry. The difference results because anchored damage reinforces the polarization of partitioning in asymmetric bacteria. In symmetric bacteria, it dilutes the polarization. Thus, stochasticity alone may have protected early bacteria from damage, but deterministic asymmetry has evolved to be equally important in extant bacteria. We estimate that 47% of damage partitioning is deterministic in E. coli. We suggest that the evolution of deterministic asymmetry from stochasticity offers an example of Waddington's genetic assimilation

Asymmetrical Damage Partitioning in Bacteria: A Model for the Evolution of Stochasticity, Determinism, and Genetic Assimilation.

Directory of Open Access Journals (Sweden)

Lin Chao

2016-01-01

Full Text Available Non-genetic phenotypic variation is common in biological organisms. The variation is potentially beneficial if the environment is changing. If the benefit is large, selection can favor the evolution of genetic assimilation, the process by which the expression of a trait is transferred from environmental to genetic control. Genetic assimilation is an important evolutionary transition, but it is poorly understood because the fitness costs and benefits of variation are often unknown. Here we show that the partitioning of damage by a mother bacterium to its two daughters can evolve through genetic assimilation. Bacterial phenotypes are also highly variable. Because gene-regulating elements can have low copy numbers, the variation is attributed to stochastic sampling. Extant Escherichia coli partition asymmetrically and deterministically more damage to the old daughter, the one receiving the mother's old pole. By modeling in silico damage partitioning in a population, we show that deterministic asymmetry is advantageous because it increases fitness variance and hence the efficiency of natural selection. However, we find that symmetrical but stochastic partitioning can be similarly beneficial. To examine why bacteria evolved deterministic asymmetry, we modeled the effect of damage anchored to the mother's old pole. While anchored damage strengthens selection for asymmetry by creating additional fitness variance, it has the opposite effect on symmetry. The difference results because anchored damage reinforces the polarization of partitioning in asymmetric bacteria. In symmetric bacteria, it dilutes the polarization. Thus, stochasticity alone may have protected early bacteria from damage, but deterministic asymmetry has evolved to be equally important in extant bacteria. We estimate that 47% of damage partitioning is deterministic in E. coli. We suggest that the evolution of deterministic asymmetry from stochasticity offers an example of Waddington

Genetic GIScience

DEFF Research Database (Denmark)

Jacquez, Geoffrey; Sabel, Clive E; Shi, Chen

2015-01-01

The exposome, defined as the totality of an individual's exposures over the life course, is a seminal concept in the environmental health sciences. Although inherently geographic, the exposome as yet is unfamiliar to many geographers. This article proposes a place-based synthesis, genetic...... geographic information science (genetic GIScience), that is founded on the exposome, genome+, and behavome. It provides an improved understanding of human health in relation to biology (the genome+), environmental exposures (the exposome), and their social, societal, and behavioral determinants (the behavome......). Genetic GIScience poses three key needs: first, a mathematical foundation for emergent theory; second, process-based models that bridge biological and geographic scales; third, biologically plausible estimates of space?time disease lags. Compartmental models are a possible solution; this article develops...

Genetic fuzzy system modeling and simulation of vascular behaviour

DEFF Research Database (Denmark)

Tang, Jiaowei; Boonen, Harrie C.M.

Background: The purpose of our project is to identify the rule sets and their interaction within the framework of cardiovascular function. By an iterative process of computational simulation and experimental work, we strive to mimic the physiological basis for cardiovascular adaptive changes in c...... the pressure change of different blood vessels. Conclusion: Genetic fuzzy system is one of potential modeling methods in modeling and simulation of vascular behavior.......Background: The purpose of our project is to identify the rule sets and their interaction within the framework of cardiovascular function. By an iterative process of computational simulation and experimental work, we strive to mimic the physiological basis for cardiovascular adaptive changes...... in cardiovascular disease and ultimately improve pharmacotherapy. For this purpose, novel computational approaches incorporating adaptive properties, auto-regulatory control and rule sets will be assessed, properties that are commonly lacking in deterministic models based on differential equations. We hypothesize...

Addition of host genetic variants in a prediction rule for post meningitis hearing loss in childhood: a model updating study.

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Sanders, Marieke S; de Jonge, Rogier C J; Terwee, Caroline B; Heymans, Martijn W; Koomen, Irene; Ouburg, Sander; Spanjaard, Lodewijk; Morré, Servaas A; van Furth, A Marceline

2013-07-23

Sensorineural hearing loss is the most common sequela in survivors of bacterial meningitis (BM). In the past we developed a validated prediction model to identify children at risk for post-meningitis hearing loss. It is known that host genetic variations, besides clinical factors, contribute to severity and outcome of BM. In this study it was determined whether host genetic risk factors improve the predictive abilities of an existing model regarding hearing loss after childhood BM. Four hundred and seventy-one Dutch Caucasian childhood BM were genotyped for 11 single nucleotide polymorphisms (SNPs) in seven different genes involved in pathogen recognition. Genetic data were added to the original clinical prediction model and performance of new models was compared to the original model by likelihood ratio tests and the area under the curve (AUC) of the receiver operating characteristic curves. Addition of TLR9-1237 SNPs and the combination of TLR2 + 2477 and TLR4 + 896 SNPs improved the clinical prediction model, but not significantly (increase of AUC's from 0.856 to 0.861 and from 0.856 to 0.875 (p = 0.570 and 0.335, respectively). Other SNPs analysed were not linked to hearing loss. Although addition of genetic risk factors did not significantly improve the clinical prediction model for post-meningitis hearing loss, AUC's of the pre-existing model remain high after addition of genetic factors. Future studies should evaluate whether more combinations of SNPs in larger cohorts has an additional value to the existing prediction model for post meningitis hearing loss.

Genetic analysis of somatic cell score in Danish dairy cattle using ramdom regression test-day model

DEFF Research Database (Denmark)

Elsaid, Reda; Sabry, Ayman; Lund, Mogens Sandø

2011-01-01

,233 Danish Holstein cows, were extracted from the national milk recording database. Each data set was analyzed with random regression models using AI-REML. Fixed effects in all models were age at first calving, herd test day, days carrying calf, effects of germ plasm importation (e.g. additive breed effects......) and low between the beginning and the end of lactation. The estimated environmental correlations were lower than the genetic correlations, but the trends were similar. Based on test-day records, the accuracy of genetic evaluations for SCC should be improved when the variation in heritabilities...

Genetic Algorithms for Agent-Based Infrastructure Interdependency Modeling and Analysis

Energy Technology Data Exchange (ETDEWEB)

May Permann

2007-03-01

Today’s society relies greatly upon an array of complex national and international infrastructure networks such as transportation, electric power, telecommunication, and financial networks. This paper describes initial research combining agent-based infrastructure modeling software and genetic algorithms (GAs) to help optimize infrastructure protection and restoration decisions. This research proposes to apply GAs to the problem of infrastructure modeling and analysis in order to determine the optimum assets to restore or protect from attack or other disaster. This research is just commencing and therefore the focus of this paper is the integration of a GA optimization method with a simulation through the simulation’s agents.

Evidence for transgenerational metabolic programming in Drosophila

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Jessica L. Buescher

2013-09-01

Worldwide epidemiologic studies have repeatedly demonstrated an association between prenatal nutritional environment, birth weight and susceptibility to adult diseases including obesity, cardiovascular disease and type 2 diabetes. Despite advances in mammalian model systems, the molecular mechanisms underlying this phenomenon are unclear, but might involve programming mechanisms such as epigenetics. Here we describe a new system for evaluating metabolic programming mechanisms using a simple, genetically tractable Drosophila model. We examined the effect of maternal caloric excess on offspring and found that a high-sugar maternal diet alters body composition of larval offspring for at least two generations, augments an obese-like phenotype under suboptimal (high-calorie feeding conditions in adult offspring, and modifies expression of metabolic genes. Our data indicate that nutritional programming mechanisms could be highly conserved and support the use of Drosophila as a model for evaluating the underlying genetic and epigenetic contributions to this phenomenon.

Genetic Learning of Fuzzy Parameters in Predictive and Decision Support Modelling

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Nebot

2012-04-01

Full Text Available In this research a genetic fuzzy system (GFS is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR methodology and the Linguistic Rule FIR (LR-FIR algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn the fuzzification parameters of the FIR and LR-FIR approaches in order to obtain reliable and useful predictive (FIR models and decision support (LR-FIR models. The GFS is evaluated in an e-learning context.

Analysis of a genetically structured variance heterogeneity model using the Box-Cox transformation

DEFF Research Database (Denmark)

Yang, Ye; Christensen, Ole Fredslund; Sorensen, Daniel

2011-01-01

of the marginal distribution of the data. To investigate how the scale of measurement affects inferences, the genetically structured heterogeneous variance model is extended to accommodate the family of Box–Cox transformations. Litter size data in rabbits and pigs that had previously been analysed...... in the untransformed scale were reanalysed in a scale equal to the mode of the marginal posterior distribution of the Box–Cox parameter. In the rabbit data, the statistical evidence for a genetic component at the level of the environmental variance is considerably weaker than that resulting from an analysis...... in the original metric. In the pig data, the statistical evidence is stronger, but the coefficient of correlation between additive genetic effects affecting mean and variance changes sign, compared to the results in the untransformed scale. The study confirms that inferences on variances can be strongly affected...

Native South American genetic structure and prehistory inferred from hierarchical modeling of mtDNA.

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Lewis, Cecil M; Long, Jeffrey C

2008-03-01

Genetic diversity in Native South Americans forms a complex pattern at both the continental and local levels. In comparing the West to the East, there is more variation within groups and smaller genetic distances between groups. From this pattern, researchers have proposed that there is more variation in the West and that a larger, more genetically diverse, founding population entered the West than the East. Here, we question this characterization of South American genetic variation and its interpretation. Our concern arises because others have inferred regional variation from the mean variation within local populations without taking into account the variation among local populations within the same region. This failure produces a biased view of the actual variation in the East. In this study, we analyze the mitochondrial DNA sequence between positions 16040 and 16322 of the Cambridge reference sequence. Our sample represents a total of 886 people from 27 indigenous populations from South (22), Central (3), and North America (2). The basic unit of our analyses is nucleotide identity by descent, which is easily modeled and proportional to nucleotide diversity. We use a forward modeling strategy to fit a series of nested models to identity by descent within and between all pairs of local populations. This method provides estimates of identity by descent at different levels of population hierarchy without assuming homogeneity within populations, regions, or continents. Our main discovery is that Eastern South America harbors more genetic variation than has been recognized. We find no evidence that there is increased identity by descent in the East relative to the total for South America. By contrast, we discovered that populations in the Western region, as a group, harbor more identity by descent than has been previously recognized, despite the fact that average identity by descent within groups is lower. In this light, there is no need to postulate separate founding

Estimating Additive and Non-Additive Genetic Variances and Predicting Genetic Merits Using Genome-Wide Dense Single Nucleotide Polymorphism Markers

DEFF Research Database (Denmark)

Su, Guosheng; Christensen, Ole Fredslund; Ostersen, Tage

2012-01-01

of genomic predictions for daily gain in pigs. In the analysis of daily gain, four linear models were used: 1) a simple additive genetic model (MA), 2) a model including both additive and additive by additive epistatic genetic effects (MAE), 3) a model including both additive and dominance genetic effects...

Real Time Optima Tracking Using Harvesting Models of the Genetic Algorithm

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Baskaran, Subbiah; Noever, D.

1999-01-01

Tracking optima in real time propulsion control, particularly for non-stationary optimization problems is a challenging task. Several approaches have been put forward for such a study including the numerical method called the genetic algorithm. In brief, this approach is built upon Darwinian-style competition between numerical alternatives displayed in the form of binary strings, or by analogy to 'pseudogenes'. Breeding of improved solution is an often cited parallel to natural selection in.evolutionary or soft computing. In this report we present our results of applying a novel model of a genetic algorithm for tracking optima in propulsion engineering and in real time control. We specialize the algorithm to mission profiling and planning optimizations, both to select reduced propulsion needs through trajectory planning and to explore time or fuel conservation strategies.

The five-factor model of personality and borderline personality disorder: a genetic analysis of comorbidity.

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Distel, Marijn A; Trull, Timothy J; Willemsen, Gonneke; Vink, Jacqueline M; Derom, Catherine A; Lynskey, Michael; Martin, Nicholas G; Boomsma, Dorret I

2009-12-15

Recently, the nature of personality disorders and their relationship with normal personality traits has received extensive attention. The five-factor model (FFM) of personality, consisting of the personality traits neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness, is one of the proposed models to conceptualize personality disorders as maladaptive variants of continuously distributed personality traits. The present study examined the phenotypic and genetic association between borderline personality and FFM personality traits. Data were available for 4403 monozygotic twins, 4425 dizygotic twins, and 1661 siblings from 6140 Dutch, Belgian, and Australian families. Broad-sense heritability estimates for neuroticism, agreeableness, conscientiousness, extraversion, openness to experience, and borderline personality were 43%, 36%, 43%, 47%, 54%, and 45%, respectively. Phenotypic correlations between borderline personality and the FFM personality traits ranged from .06 for openness to experience to .68 for neuroticism. Multiple regression analyses showed that a combination of high neuroticism and low agreeableness best predicted borderline personality. Multivariate genetic analyses showed the genetic factors that influence individual differences in neuroticism, agreeableness, conscientiousness, and extraversion account for all genetic liability to borderline personality. Unique environmental effects on borderline personality, however, were not completely shared with those for the FFM traits (33% is unique to borderline personality). Borderline personality shares all genetic variation with neuroticism, agreeableness, conscientiousness, and extraversion. The unique environmental influences specific to borderline personality may cause individuals with a specific pattern of personality traits to cross a threshold and develop borderline personality.

“Glowing Head” Mice: A Genetic Tool Enabling Reliable Preclinical Image-Based Evaluation of Cancers in Immunocompetent Allografts

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Day, Chi-Ping; Carter, John; Ohler, Zoe Weaver; Bonomi, Carrie; El Meskini, Rajaa; Martin, Philip; Graff-Cherry, Cari; Feigenbaum, Lionel; Tüting, Thomas; Van Dyke, Terry; Hollingshead, Melinda; Merlino, Glenn

2014-01-01

Preclinical therapeutic assessment currently relies on the growth response of established human cell lines xenografted into immunocompromised mice, a strategy that is generally not predictive of clinical outcomes. Immunocompetent genetically engineered mouse (GEM)-derived tumor allograft models offer highly tractable preclinical alternatives and facilitate analysis of clinically promising immunomodulatory agents. Imageable reporters are essential for accurately tracking tumor growth and response, particularly for metastases. Unfortunately, reporters such as luciferase and GFP are foreign antigens in immunocompetent mice, potentially hindering tumor growth and confounding therapeutic responses. Here we assessed the value of reporter-tolerized GEMs as allograft recipients by targeting minimal expression of a luciferase-GFP fusion reporter to the anterior pituitary gland (dubbed the “Glowing Head” or GH mouse). The luciferase-GFP reporter expressed in tumor cells induced adverse immune responses in wildtype mouse, but not in GH mouse, as transplantation hosts. The antigenicity of optical reporters resulted in a decrease in both the growth and metastatic potential of the labeled tumor in wildtype mice as compared to the GH mice. Moreover, reporter expression can also alter the tumor response to chemotherapy or targeted therapy in a context-dependent manner. Thus the GH mice and experimental approaches vetted herein provide concept validation and a strategy for effective, reproducible preclinical evaluation of growth and response kinetics for traceable tumors. PMID:25369133

Modeling solar radiation of Mediterranean region in Turkey by using fuzzy genetic approach

International Nuclear Information System (INIS)

Kisi, Ozgur

2014-01-01

The study investigates the ability of FG (fuzzy genetic) approach in modeling solar radiation of seven cities from Mediterranean region of Anatolia, Turkey. Latitude, longitude, altitude and month of the year data from the Adana, K. Maras, Mersin, Antalya, Isparta, Burdur and Antakya cities are used as inputs to the FG model to estimate one month ahead solar radiation. FG model is compared with ANNs (artificial neural networks) and ANFIS (adaptive neruro fuzzzy inference system) models with respect to RMSE (root mean square errors), MAE (mean absolute errors) and determination coefficient (R 2 ) statistics. Comparison results indicate that the FG model performs better than the ANN and ANFIS models. It is found that the FG model can be successfully used for estimating solar radiation by using latitude, longitude, altitude and month of the year information. FG model with RMSE = 6.29 MJ/m 2 , MAE = 4.69 MJ/m 2 and R 2 = 0.905 in the test stage was found to be superior to the optimal ANN model with RMSE = 7.17 MJ/m 2 , MAE = 5.29 MJ/m 2 and R 2 = 0.876 and ANFIS model with RMSE = 6.75 MJ/m 2 , MAE = 5.10 MJ/m 2 and R 2 = 0.892 in estimating solar radiation. - Highlights: • SR (Solar radiation) of seven cities from Mediterranean region of Turkey is predicted. • FG (Fuzzy genetic) models are developed for accurately estimation of SR. • The ability of the FG models used in the study is found to be satisfactory. • FG models are compared with commonly used ANNs (artificial neural networks). • FG models are found to perform better than the ANNs models

On coding genotypes for genetic markers with multiple alleles in genetic association study of quantitative traits

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Wang Tao

2011-09-01

Full Text Available Abstract Background In genetic association study of quantitative traits using F∞ models, how to code the marker genotypes and interpret the model parameters appropriately is important for constructing hypothesis tests and making statistical inferences. Currently, the coding of marker genotypes in building F∞ models has mainly focused on the biallelic case. A thorough work on the coding of marker genotypes and interpretation of model parameters for F∞ models is needed especially for genetic markers with multiple alleles. Results In this study, we will formulate F∞ genetic models under various regression model frameworks and introduce three genotype coding schemes for genetic markers with multiple alleles. Starting from an allele-based modeling strategy, we first describe a regression framework to model the expected genotypic values at given markers. Then, as extension from the biallelic case, we introduce three coding schemes for constructing fully parameterized one-locus F∞ models and discuss the relationships between the model parameters and the expected genotypic values. Next, under a simplified modeling framework for the expected genotypic values, we consider several reduced one-locus F∞ models from the three coding schemes on the estimability and interpretation of their model parameters. Finally, we explore some extensions of the one-locus F∞ models to two loci. Several fully parameterized as well as reduced two-locus F∞ models are addressed. Conclusions The genotype coding schemes provide different ways to construct F∞ models for association testing of multi-allele genetic markers with quantitative traits. Which coding scheme should be applied depends on how convenient it can provide the statistical inferences on the parameters of our research interests. Based on these F∞ models, the standard regression model fitting tools can be used to estimate and test for various genetic effects through statistical contrasts with the

Applying ecological models to communities of genetic elements: the case of neutral theory.

Science.gov (United States)

Linquist, Stefan; Cottenie, Karl; Elliott, Tyler A; Saylor, Brent; Kremer, Stefan C; Gregory, T Ryan

2015-07-01

A promising recent development in molecular biology involves viewing the genome as a mini-ecosystem, where genetic elements are compared to organisms and the surrounding cellular and genomic structures are regarded as the local environment. Here, we critically evaluate the prospects of ecological neutral theory (ENT), a popular model in ecology, as it applies at the genomic level. This assessment requires an overview of the controversy surrounding neutral models in community ecology. In particular, we discuss the limitations of using ENT both as an explanation of community dynamics and as a null hypothesis. We then analyse a case study in which ENT has been applied to genomic data. Our central finding is that genetic elements do not conform to the requirements of ENT once its assumptions and limitations are made explicit. We further compare this genome-level application of ENT to two other, more familiar approaches in genomics that rely on neutral mechanisms: Kimura's molecular neutral theory and Lynch's mutational-hazard model. Interestingly, this comparison reveals that there are two distinct concepts of neutrality associated with these models, which we dub 'fitness neutrality' and 'competitive neutrality'. This distinction helps to clarify the various roles for neutral models in genomics, for example in explaining the evolution of genome size. © 2015 John Wiley & Sons Ltd.

Helicobacter pylori genetic diversification in the Mongolian gerbil model.

Science.gov (United States)

Beckett, Amber C; Loh, John T; Chopra, Abha; Leary, Shay; Lin, Aung Soe; McDonnell, Wyatt J; Dixon, Beverly R E A; Noto, Jennifer M; Israel, Dawn A; Peek, Richard M; Mallal, Simon; Algood, Holly M Scott; Cover, Timothy L

2018-01-01

Helicobacter pylori requires genetic agility to infect new hosts and establish long-term colonization of changing gastric environments. In this study, we analyzed H. pylori genetic adaptation in the Mongolian gerbil model. This model is of particular interest because H. pylori -infected gerbils develop a high level of gastric inflammation and often develop gastric adenocarcinoma or gastric ulceration. We analyzed the whole genome sequences of H. pylori strains cultured from experimentally infected gerbils, in comparison to the genome sequence of the input strain. The mean annualized single nucleotide polymorphism (SNP) rate per site was 1.5e -5 , which is similar to the rates detected previously in H. pylori- infected humans. Many of the mutations occurred within or upstream of genes associated with iron-related functions ( fur , tonB1 , fecA2 , fecA3 , and frpB3 ) or encoding outer membrane proteins ( alpA, oipA, fecA2, fecA3, frpB3 and cagY ). Most of the SNPs within coding regions (86%) were non-synonymous mutations. Several deletion or insertion mutations led to disruption of open reading frames, suggesting that the corresponding gene products are not required or are deleterious during chronic H. pylori colonization of the gerbil stomach. Five variants (three SNPs and two deletions) were detected in isolates from multiple animals, which suggests that these mutations conferred a selective advantage. One of the mutations (FurR88H) detected in isolates from multiple animals was previously shown to confer increased resistance to oxidative stress, and we now show that this SNP also confers a survival advantage when H. pylori is co-cultured with neutrophils. Collectively, these analyses allow the identification of mutations that are positively selected during H. pylori colonization of the gerbil model.

Linear and Poisson models for genetic evaluation of tick resistance in cross-bred Hereford x Nellore cattle.

Science.gov (United States)

Ayres, D R; Pereira, R J; Boligon, A A; Silva, F F; Schenkel, F S; Roso, V M; Albuquerque, L G

2013-12-01

Cattle resistance to ticks is measured by the number of ticks infesting the animal. The model used for the genetic analysis of cattle resistance to ticks frequently requires logarithmic transformation of the observations. The objective of this study was to evaluate the predictive ability and goodness of fit of different models for the analysis of this trait in cross-bred Hereford x Nellore cattle. Three models were tested: a linear model using logarithmic transformation of the observations (MLOG); a linear model without transformation of the observations (MLIN); and a generalized linear Poisson model with residual term (MPOI). All models included the classificatory effects of contemporary group and genetic group and the covariates age of animal at the time of recording and individual heterozygosis, as well as additive genetic effects as random effects. Heritability estimates were 0.08 ± 0.02, 0.10 ± 0.02 and 0.14 ± 0.04 for MLIN, MLOG and MPOI models, respectively. The model fit quality, verified by deviance information criterion (DIC) and residual mean square, indicated fit superiority of MPOI model. The predictive ability of the models was compared by validation test in independent sample. The MPOI model was slightly superior in terms of goodness of fit and predictive ability, whereas the correlations between observed and predicted tick counts were practically the same for all models. A higher rank correlation between breeding values was observed between models MLOG and MPOI. Poisson model can be used for the selection of tick-resistant animals. © 2013 Blackwell Verlag GmbH.

The Potential of Zebrafish as a Model Organism for Improving the Translation of Genetic Anticancer Nanomedicines

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C Gutiérrez-Lovera

2017-11-01

Full Text Available In the last few decades, the field of nanomedicine applied to cancer has revolutionized cancer treatment: several nanoformulations have already reached the market and are routinely being used in the clinical practice. In the case of genetic nanomedicines, i.e., designed to deliver gene therapies to cancer cells for therapeutic purposes, advances have been less impressive. This is because of the many barriers that limit the access of the therapeutic nucleic acids to their target site, and the lack of models that would allow for an improvement in the understanding of how nanocarriers can be tailored to overcome them. Zebrafish has important advantages as a model species for the study of anticancer therapies, and have a lot to offer regarding the rational development of efficient delivery of genetic nanomedicines, and hence increasing the chances of their successful translation. This review aims to provide an overview of the recent advances in the development of genetic anticancer nanomedicines, and of the zebrafish models that stand as promising tools to shed light on their mechanisms of action and overall potential in oncology.

Radio-over-fiber linearization with optimized genetic algorithm CPWL model.

Science.gov (United States)

Mateo, Carlos; Carro, Pedro L; García-Dúcar, Paloma; De Mingo, Jesús; Salinas, Íñigo

2017-02-20

This article proposes an optimized version of a canonical piece-wise-linear (CPWL) digital predistorter in order to enhance the linearity of a radio-over-fiber (RoF) LTE mobile fronthaul. In this work, we propose a threshold allocation optimization process carried out by a genetic algorithm (GA) in order to optimize the CPWL model (GA-CPWL). Firstly, experiments show how the CPWL model outperforms the classical memory polynomial DPD in an intensity modulation/direct detection (IM/DD) RoF link. Then, the GA-CPWL predistorter is compared with the CPWL model in several scenarios, in order to verify that the proposed DPD offers better performance in different optical transmission conditions. Experimental results reveal that with a proper threshold allocation, the GA-CPWL predistorter offers very promising outcomes.

Localization of canine brachycephaly using an across breed mapping approach.

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Danika Bannasch

2010-03-01

Full Text Available The domestic dog, Canis familiaris, exhibits profound phenotypic diversity and is an ideal model organism for the genetic dissection of simple and complex traits. However, some of the most interesting phenotypes are fixed in particular breeds and are therefore less tractable to genetic analysis using classical segregation-based mapping approaches. We implemented an across breed mapping approach using a moderately dense SNP array, a low number of animals and breeds carefully selected for the phenotypes of interest to identify genetic variants responsible for breed-defining characteristics. Using a modest number of affected (10-30 and control (20-60 samples from multiple breeds, the correct chromosomal assignment was identified in a proof of concept experiment using three previously defined loci; hyperuricosuria, white spotting and chondrodysplasia. Genome-wide association was performed in a similar manner for one of the most striking morphological traits in dogs: brachycephalic head type. Although candidate gene approaches based on comparable phenotypes in mice and humans have been utilized for this trait, the causative gene has remained elusive using this method. Samples from nine affected breeds and thirteen control breeds identified strong genome-wide associations for brachycephalic head type on Cfa 1. Two independent datasets identified the same genomic region. Levels of relative heterozygosity in the associated region indicate that it has been subjected to a selective sweep, consistent with it being a breed defining morphological characteristic. Genotyping additional dogs in the region confirmed the association. To date, the genetic structure of dog breeds has primarily been exploited for genome wide association for segregating traits. These results demonstrate that non-segregating traits under strong selection are equally tractable to genetic analysis using small sample numbers.

A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction

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Danandeh Mehr, Ali; Kahya, Ercan

2017-06-01

Genetic programming (GP) is able to systematically explore alternative model structures of different accuracy and complexity from observed input and output data. The effectiveness of GP in hydrological system identification has been recognized in recent studies. However, selecting a parsimonious (accurate and simple) model from such alternatives still remains a question. This paper proposes a Pareto-optimal moving average multigene genetic programming (MA-MGGP) approach to develop a parsimonious model for single-station streamflow prediction. The three main components of the approach that take us from observed data to a validated model are: (1) data pre-processing, (2) system identification and (3) system simplification. The data pre-processing ingredient uses a simple moving average filter to diminish the lagged prediction effect of stand-alone data-driven models. The multigene ingredient of the model tends to identify the underlying nonlinear system with expressions simpler than classical monolithic GP and, eventually simplification component exploits Pareto front plot to select a parsimonious model through an interactive complexity-efficiency trade-off. The approach was tested using the daily streamflow records from a station on Senoz Stream, Turkey. Comparing to the efficiency results of stand-alone GP, MGGP, and conventional multi linear regression prediction models as benchmarks, the proposed Pareto-optimal MA-MGGP model put forward a parsimonious solution, which has a noteworthy importance of being applied in practice. In addition, the approach allows the user to enter human insight into the problem to examine evolved models and pick the best performing programs out for further analysis.

Survey on queueing models with standbys support

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Kolledath Sreekanth

2018-01-01

Full Text Available This paper is a survey article on queueing models with standbys support. Due to many real life applications of queueing models, it has become an interesting area for researchers and a lot of research work has been exerted so far. It is worthwhile to examine the performance based analysis for queueing modelling system as it provides a valuable insight to the tractability of the system and accelerates its efficiency. The provision of standbys to the queueing modelling of a real system is needed for smooth functioning in the presence of its unavoidable failures. The present survey provides a dig into the research work done, and emphasis the sequential developments on queueing models with standbys support.

Behavioral phenotypes in schizophrenic animal models with multiple combinations of genetic and environmental factors.

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Hida, Hirotake; Mouri, Akihiro; Noda, Yukihiro

2013-01-01

Schizophrenia is a multifactorial psychiatric disorder in which both genetic and environmental factors play a role. Genetic [e.g., Disrupted-in-schizophrenia 1 (DISC1), Neuregulin-1 (NRG1)] and environmental factors (e.g., maternal viral infection, obstetric complications, social stress) may act during the developmental period to increase the incidence of schizophrenia. In animal models, interactions between susceptibility genes and the environment can be controlled in ways not possible in humans; therefore, such models are useful for investigating interactions between or within factors in the pathogenesis and pathophysiology of schizophrenia. We provide an overview of schizophrenic animal models investigating interactions between or within factors. First, we reviewed gene-environment interaction animal models, in which schizophrenic candidate gene mutant mice were subjected to perinatal immune activation or adolescent stress. Next, environment-environment interaction animal models, in which mice were subjected to a combination of perinatal immune activation and adolescent administration of drugs, were described. These animal models showed interaction between or within factors; behavioral changes, which were obscured by each factor, were marked by interaction of factors and vice versa. Appropriate behavioral approaches with such models will be invaluable for translational research on novel compounds, and also for providing insight into the pathogenesis and pathophysiology of schizophrenia.

Genetic Analysis of Somatic Cell Score in Danish Holsteins Using a Liability-Normal Mixture Model

DEFF Research Database (Denmark)

Madsen, P; Shariati, M M; Ødegård, J

2008-01-01

Mixture models are appealing for identifying hidden structures affecting somatic cell score (SCS) data, such as unrecorded cases of subclinical mastitis. Thus, liability-normal mixture (LNM) models were used for genetic analysis of SCS data, with the aim of predicting breeding values for such cas...

Assessment of Genetic Heterogeneity in Structured Plant Populations Using Multivariate Whole-Genome Regression Models.

Science.gov (United States)

Lehermeier, Christina; Schön, Chris-Carolin; de Los Campos, Gustavo

2015-09-01

Plant breeding populations exhibit varying levels of structure and admixture; these features are likely to induce heterogeneity of marker effects across subpopulations. Traditionally, structure has been dealt with as a potential confounder, and various methods exist to "correct" for population stratification. However, these methods induce a mean correction that does not account for heterogeneity of marker effects. The animal breeding literature offers a few recent studies that consider modeling genetic heterogeneity in multibreed data, using multivariate models. However, these methods have received little attention in plant breeding where population structure can have different forms. In this article we address the problem of analyzing data from heterogeneous plant breeding populations, using three approaches: (a) a model that ignores population structure [A-genome-based best linear unbiased prediction (A-GBLUP)], (b) a stratified (i.e., within-group) analysis (W-GBLUP), and (c) a multivariate approach that uses multigroup data and accounts for heterogeneity (MG-GBLUP). The performance of the three models was assessed on three different data sets: a diversity panel of rice (Oryza sativa), a maize (Zea mays L.) half-sib panel, and a wheat (Triticum aestivum L.) data set that originated from plant breeding programs. The estimated genomic correlations between subpopulations varied from null to moderate, depending on the genetic distance between subpopulations and traits. Our assessment of prediction accuracy features cases where ignoring population structure leads to a parsimonious more powerful model as well as others where the multivariate and stratified approaches have higher predictive power. In general, the multivariate approach appeared slightly more robust than either the A- or the W-GBLUP. Copyright © 2015 by the Genetics Society of America.

Genetic structure and bio-climatic modeling support allopatric over parapatric speciation along a latitudinal gradient.

Science.gov (United States)

Rossetto, Maurizio; Allen, Chris B; Thurlby, Katie A G; Weston, Peter H; Milner, Melita L

2012-08-20

Four of the five species of Telopea (Proteaceae) are distributed in a latitudinal replacement pattern on the south-eastern Australian mainland. In similar circumstances, a simple allopatric speciation model that identifies the origins of genetic isolation within temporal geographic separation is considered as the default model. However, secondary contact between differentiated lineages can result in similar distributional patterns to those arising from a process of parapatric speciation (where gene flow between lineages remains uninterrupted during differentiation). Our aim was to use the characteristic distributional patterns in Telopea to test whether it reflected the evolutionary models of allopatric or parapatric speciation. Using a combination of genetic evidence and environmental niche modelling, we focused on three main questions: do currently described geographic borders coincide with genetic and environmental boundaries; are there hybrid zones in areas of secondary contact between closely related species; did species distributions contract during the last glacial maximum resulting in distributional gaps even where overlap and hybridisation currently occur? Total genomic DNA was extracted from 619 individuals sampled from 36 populations representing the four species. Seven nuclear microsatellites (nSSR) and six chloroplast microsatellites (cpSSR) were amplified across all populations. Genetic structure and the signature of admixture in overlap zones was described using the Bayesian clustering methods implemented in STUCTURE and NewHybrids respectively. Relationships between chlorotypes were reconstructed as a median-joining network. Environmental niche models were produced for all species using environmental parameters from both the present day and the last glacial maximum (LGM).The nSSR loci amplified a total of 154 alleles, while data for the cpSSR loci produced a network of six chlorotypes. STRUCTURE revealed an optimum number of five clusters

Developments in statistical analysis in quantitative genetics

DEFF Research Database (Denmark)

Sorensen, Daniel

2009-01-01

of genetic means and variances, models for the analysis of categorical and count data, the statistical genetics of a model postulating that environmental variance is partly under genetic control, and a short discussion of models that incorporate massive genetic marker information. We provide an overview......A remarkable research impetus has taken place in statistical genetics since the last World Conference. This has been stimulated by breakthroughs in molecular genetics, automated data-recording devices and computer-intensive statistical methods. The latter were revolutionized by the bootstrap...... and by Markov chain Monte Carlo (McMC). In this overview a number of specific areas are chosen to illustrate the enormous flexibility that McMC has provided for fitting models and exploring features of data that were previously inaccessible. The selected areas are inferences of the trajectories over time...

Present, past and future of the European rock fern Asplenium fontanum: combining distribution modelling and population genetics to study the effect of climate change on geographic range and genetic diversity.

Science.gov (United States)

Bystriakova, Nadia; Ansell, Stephen W; Russell, Stephen J; Grundmann, Michael; Vogel, Johannes C; Schneider, Harald

2014-02-01

Climate change is expected to alter the geographic range of many plant species dramatically. Predicting this response will be critical to managing the conservation of plant resources and the effects of invasive species. The aim of this study was to predict the response of temperate homosporous ferns to climate change. Genetic diversity and changes in distribution range were inferred for the diploid rock fern Asplenium fontanum along a South-North transect, extending from its putative last glacial maximum (LGM) refugia in southern France towards southern Germany and eastern-central France. This study reconciles observations from distribution models and phylogeographic analyses derived from plastid and nuclear diversity. Genetic diversity distribution and niche modelling propose that genetic diversity accumulates in the LGM climate refugium in southern France with the formation of a diversity gradient reflecting a slow, post-LGM range expansion towards the current distribution range. Evidence supports the fern's preference for outcrossing, contradicting the expectation that homosporous ferns would populate new sites by single-spore colonization. Prediction of climate and distribution range change suggests that a dramatic loss of range and genetic diversity in this fern is possible. The observed migration is best described by the phalanx expansion model. The results suggest that homosporous ferns reproducing preferentially by outcrossing accumulate genetic diversity primarily in LGM climate refugia and may be threatened if these areas disappear due to global climate change.

Using genetic algorithm and TOPSIS for Xinanjiang model calibration with a single procedure

Science.gov (United States)

Cheng, Chun-Tian; Zhao, Ming-Yan; Chau, K. W.; Wu, Xin-Yu

2006-01-01

Genetic Algorithm (GA) is globally oriented in searching and thus useful in optimizing multiobjective problems, especially where the objective functions are ill-defined. Conceptual rainfall-runoff models that aim at predicting streamflow from the knowledge of precipitation over a catchment have become a basic tool for flood forecasting. The parameter calibration of a conceptual model usually involves the multiple criteria for judging the performances of observed data. However, it is often difficult to derive all objective functions for the parameter calibration problem of a conceptual model. Thus, a new method to the multiple criteria parameter calibration problem, which combines GA with TOPSIS (technique for order performance by similarity to ideal solution) for Xinanjiang model, is presented. This study is an immediate further development of authors' previous research (Cheng, C.T., Ou, C.P., Chau, K.W., 2002. Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration. Journal of Hydrology, 268, 72-86), whose obvious disadvantages are to split the whole procedure into two parts and to become difficult to integrally grasp the best behaviors of model during the calibration procedure. The current method integrates the two parts of Xinanjiang rainfall-runoff model calibration together, simplifying the procedures of model calibration and validation and easily demonstrated the intrinsic phenomenon of observed data in integrity. Comparison of results with two-step procedure shows that the current methodology gives similar results to the previous method, is also feasible and robust, but simpler and easier to apply in practice.

Genetics of human hydrocephalus

Science.gov (United States)

Williams, Michael A.; Rigamonti, Daniele

2006-01-01

Human hydrocephalus is a common medical condition that is characterized by abnormalities in the flow or resorption of cerebrospinal fluid (CSF), resulting in ventricular dilatation. Human hydrocephalus can be classified into two clinical forms, congenital and acquired. Hydrocephalus is one of the complex and multifactorial neurological disorders. A growing body of evidence indicates that genetic factors play a major role in the pathogenesis of hydrocephalus. An understanding of the genetic components and mechanism of this complex disorder may offer us significant insights into the molecular etiology of impaired brain development and an accumulation of the cerebrospinal fluid in cerebral compartments during the pathogenesis of hydrocephalus. Genetic studies in animal models have started to open the way for understanding the underlying pathology of hydrocephalus. At least 43 mutants/loci linked to hereditary hydrocephalus have been identified in animal models and humans. Up to date, 9 genes associated with hydrocephalus have been identified in animal models. In contrast, only one such gene has been identified in humans. Most of known hydrocephalus gene products are the important cytokines, growth factors or related molecules in the cellular signal pathways during early brain development. The current molecular genetic evidence from animal models indicate that in the early development stage, impaired and abnormal brain development caused by abnormal cellular signaling and functioning, all these cellular and developmental events would eventually lead to the congenital hydrocephalus. Owing to our very primitive knowledge of the genetics and molecular pathogenesis of human hydrocephalus, it is difficult to evaluate whether data gained from animal models can be extrapolated to humans. Initiation of a large population genetics study in humans will certainly provide invaluable information about the molecular and cellular etiology and the developmental mechanisms of human

Whole lifespan microscopic observation of budding yeast aging through a microfluidic dissection platform

NARCIS (Netherlands)

Lee, Sung Sik; Avalos Vizcarra, Ima; Huberts, Daphne H E W; Lee, Luke P; Heinemann, Matthias

2012-01-01

Important insights into aging have been generated with the genetically tractable and short-lived budding yeast. However, it is still impossible today to continuously track cells by high-resolution microscopic imaging (e.g., fluorescent imaging) throughout their entire lifespan. Instead, the field

Experimental Population Genetics in the Introductory Genetics Laboratory Using "Drosophila" as a Model Organism

Science.gov (United States)

Johnson, Ronald; Kennon, Tillman

2009-01-01

Hypotheses of population genetics are derived and tested by students in the introductory genetics laboratory classroom as they explore the effects of biotic variables (physical traits of fruit flies) and abiotic variables (island size and distance) on fruit fly populations. In addition to this hypothesis-driven experiment, the development of…

Find the weakest link. A comparison between demographic, genetic and demo-genetic metapopulation extinction times

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Robert Alexandre

2011-09-01

Full Text Available Abstract Background While the ultimate causes of most species extinctions are environmental, environmental constraints have various secondary consequences on evolutionary and ecological processes. The roles of demographic, genetic mechanisms and their interactions in limiting the viabilities of species or populations have stirred much debate and remain difficult to evaluate in the absence of demography-genetics conceptual and technical framework. Here, I computed projected times to metapopulation extinction using (1 a model focusing on the effects of species properties, habitat quality, quantity and temporal variability on the time to demographic extinction; (2 a genetic model focusing on the dynamics of the drift and inbreeding loads under the same species and habitat constraints; (3 a demo-genetic model accounting for demographic-genetic processes and feedbacks. Results Results indicate that a given population may have a high demographic, but low genetic viability or vice versa; and whether genetic or demographic aspects will be the most limiting to overall viability depends on the constraints faced by the species (e.g., reduction of habitat quantity or quality. As a consequence, depending on metapopulation or species characteristics, incorporating genetic considerations to demographically-based viability assessments may either moderately or severely reduce the persistence time. On the other hand, purely genetically-based estimates of species viability may either underestimate (by neglecting demo-genetic interactions or overestimate (by neglecting the demographic resilience true viability. Conclusion Unbiased assessments of the viabilities of species may only be obtained by identifying and considering the most limiting processes (i.e., demography or genetics, or, preferentially, by integrating them.

On the Reliability of Nonlinear Modeling using Enhanced Genetic Programming Techniques

Science.gov (United States)

Winkler, S. M.; Affenzeller, M.; Wagner, S.

The use of genetic programming (GP) in nonlinear system identification enables the automated search for mathematical models that are evolved by an evolutionary process using the principles of selection, crossover and mutation. Due to the stochastic element that is intrinsic to any evolutionary process, GP cannot guarantee the generation of similar or even equal models in each GP process execution; still, if there is a physical model underlying to the data that are analyzed, then GP is expected to find these structures and produce somehow similar results. In this paper we define a function for measuring the syntactic similarity of mathematical models represented as structure trees; using this similarity function we compare the results produced by GP techniques for a data set representing measurement data of a BMW Diesel engine.

A Rapid Embryonic Stem Cell-Based Mouse Model for B-cell Lymphomas Driven by Epstein-Barr Virus Protein LMP1.

Science.gov (United States)

Ba, Zhaoqing; Meng, Fei-Long; Gostissa, Monica; Huang, Pei-Yi; Ke, Qiang; Wang, Zhe; Dao, Mai N; Fujiwara, Yuko; Rajewsky, Klaus; Zhang, Baochun; Alt, Frederick W

2015-06-01

The Epstein-Barr virus (EBV) latent membrane protein 1 (LMP1) contributes to oncogenic human B-cell transformation. Mouse B cells conditionally expressing LMP1 are not predisposed to B-cell malignancies, as LMP1-expressing B cells are eliminated by T cells. However, mice with conditional B-cell LMP1 expression and genetic elimination of α/β and γ/δ T cells ("CLT" mice) die early in association with B-cell lymphoproliferation and lymphomagenesis. Generation of CLT mice involves in-breeding multiple independently segregating alleles. Thus, although introduction of additional activating or knockout mutations into the CLT model is desirable for further B-cell expansion and immunosurveillance studies, doing such experiments by germline breeding is time-consuming, expensive, and sometimes unfeasible. To generate a more tractable model, we generated clonal CLT embryonic stem (ES) cells from CLT embryos and injected them into RAG2-deficient blastocysts to generate chimeric mice, which, like germline CLT mice, harbor splenic CLT B cells and lack T cells. CLT chimeric mice generated by this RAG2-deficient blastocyst complementation ("RDBC") approach die rapidly in association with B-cell lymphoproliferation and lymphoma. Because CLT lymphomas routinely express the activation-induced cytidine deaminase (AID) antibody diversifier, we tested potential AID roles by eliminating the AID gene in CLT ES cells and testing them via RDBC. We found that CLT and AID-deficient CLT ES chimeras had indistinguishable phenotypes, showing that AID is not essential for LMP1-induced lymphomagenesis. Beyond expanding accessibility and utility of CLT mice as a cancer immunotherapy model, our studies provide a new approach for facilitating generation of genetically complex mouse cancer models. ©2015 American Association for Cancer Research.

Modeling of genetic algorithms with a finite population

NARCIS (Netherlands)

C.H.M. van Kemenade

1997-01-01

textabstractCross-competition between non-overlapping building blocks can strongly influence the performance of evolutionary algorithms. The choice of the selection scheme can have a strong influence on the performance of a genetic algorithm. This paper describes a number of different genetic

Probability Model of Allele Frequency of Alzheimer’s Disease Genetic Risk Factor

Directory of Open Access Journals (Sweden)

Afshin Fayyaz-Movaghar

2016-06-01

Full Text Available Background and Purpose: The identification of genetics risk factors of human diseases is very important. This study is conducted to model the allele frequencies (AFs of Alzheimer’s disease. Materials and Methods: In this study, several candidate probability distributions are fitted on a data set of Alzheimer’s disease genetic risk factor. Unknown parameters of the considered distributions are estimated, and some criterions of goodness-of-fit are calculated for the sake of comparison. Results: Based on some statistical criterions, the beta distribution gives the best fit on AFs. However, the estimate values of the parameters of beta distribution lead us to the standard uniform distribution. Conclusion: The AFs of Alzheimer’s disease follow the standard uniform distribution.

The equity price channel in a New-Keynesian DSGE model with financial frictions and banking

OpenAIRE

Hylton Hollander; Guangling Liu

2013-01-01

This paper studies the role of the equity price channel in business cycle fluctuations, and highlights the equity price channel as a different aspect to general equilibrium models with financial frictions and, as a result, emphasizes the systemic influence of financial markets on the real economy. We develop a canonical New-Keynesian DSGE model with a tractable role for the equity market in banking, entrepreneur and household economic activities. The model is estimated with Bayesian technique...

Development of eSSR-Markers in Setaria italica and Their Applicability in Studying Genetic Diversity, Cross-Transferability and Comparative Mapping in Millet and Non-Millet Species.

Science.gov (United States)

Kumari, Kajal; Muthamilarasan, Mehanathan; Misra, Gopal; Gupta, Sarika; Subramanian, Alagesan; Parida, Swarup Kumar; Chattopadhyay, Debasis; Prasad, Manoj

2013-01-01

Foxtail millet (Setariaitalica L.) is a tractable experimental model crop for studying functional genomics of millets and bioenergy grasses. But the limited availability of genomic resources, particularly expressed sequence-based genic markers is significantly impeding its genetic improvement. Considering this, we attempted to develop EST-derived-SSR (eSSR) markers and utilize them in germplasm characterization, cross-genera transferability and in silico comparative mapping. From 66,027 foxtail millet EST sequences 24,828 non-redundant ESTs were deduced, representing ~16 Mb, which revealed 534 (~2%) eSSRs in 495 SSR containing ESTs at a frequency of 1/30 kb. A total of 447 pp were successfully designed, of which 327 were mapped physically onto nine chromosomes. About 106 selected primer pairs representing the foxtail millet genome showed high-level of cross-genera amplification at an average of ~88% in eight millets and four non-millet species. Broad range of genetic diversity (0.02-0.65) obtained in constructed phylogenetic tree using 40 eSSR markers demonstrated its utility in germplasm characterizations and phylogenetics. Comparative mapping of physically mapped eSSR markers showed considerable proportion of sequence-based orthology and syntenic relationship between foxtail millet chromosomes and sorghum (~68%), maize (~61%) and rice (~42%) chromosomes. Synteny analysis of eSSRs of foxtail millet, rice, maize and sorghum suggested the nested chromosome fusion frequently observed in grass genomes. Thus, for the first time we had generated large-scale eSSR markers in foxtail millet and demonstrated their utility in germplasm characterization, transferability, phylogenetics and comparative mapping studies in millets and bioenergy grass species.

Cox proportional hazards models have more statistical power than logistic regression models in cross-sectional genetic association studies

NARCIS (Netherlands)

van der Net, Jeroen B.; Janssens, A. Cecile J. W.; Eijkemans, Marinus J. C.; Kastelein, John J. P.; Sijbrands, Eric J. G.; Steyerberg, Ewout W.

2008-01-01

Cross-sectional genetic association studies can be analyzed using Cox proportional hazards models with age as time scale, if age at onset of disease is known for the cases and age at data collection is known for the controls. We assessed to what degree and under what conditions Cox proportional

Cumulative t-link threshold models for the genetic analysis of calving ease scores

Directory of Open Access Journals (Sweden)

Tempelman Robert J

2003-09-01

Full Text Available Abstract In this study, a hierarchical threshold mixed model based on a cumulative t-link specification for the analysis of ordinal data or more, specifically, calving ease scores, was developed. The validation of this model and the Markov chain Monte Carlo (MCMC algorithm was carried out on simulated data from normally and t4 (i.e. a t-distribution with four degrees of freedom distributed populations using the deviance information criterion (DIC and a pseudo Bayes factor (PBF measure to validate recently proposed model choice criteria. The simulation study indicated that although inference on the degrees of freedom parameter is possible, MCMC mixing was problematic. Nevertheless, the DIC and PBF were validated to be satisfactory measures of model fit to data. A sire and maternal grandsire cumulative t-link model was applied to a calving ease dataset from 8847 Italian Piemontese first parity dams. The cumulative t-link model was shown to lead to posterior means of direct and maternal heritabilities (0.40 ± 0.06, 0.11 ± 0.04 and a direct maternal genetic correlation (-0.58 ± 0.15 that were not different from the corresponding posterior means of the heritabilities (0.42 ± 0.07, 0.14 ± 0.04 and the genetic correlation (-0.55 ± 0.14 inferred under the conventional cumulative probit link threshold model. Furthermore, the correlation (> 0.99 between posterior means of sire progeny merit from the two models suggested no meaningful rerankings. Nevertheless, the cumulative t-link model was decisively chosen as the better fitting model for this calving ease data using DIC and PBF.

Residence-time framework for modeling multicomponent reactive transport in stream hyporheic zones

Science.gov (United States)

Painter, S. L.; Coon, E. T.; Brooks, S. C.

2017-12-01

Process-based models for transport and transformation of nutrients and contaminants in streams require tractable representations of solute exchange between the stream channel and biogeochemically active hyporheic zones. Residence-time based formulations provide an alternative to detailed three-dimensional simulations and have had good success in representing hyporheic exchange of non-reacting solutes. We extend the residence-time formulation for hyporheic transport to accommodate general multicomponent reactive transport. To that end, the integro-differential form of previous residence time models is replaced by an equivalent formulation based on a one-dimensional advection dispersion equation along the channel coupled at each channel location to a one-dimensional transport model in Lagrangian travel-time form. With the channel discretized for numerical solution, the associated Lagrangian model becomes a subgrid model representing an ensemble of streamlines that are diverted into the hyporheic zone before returning to the channel. In contrast to the previous integro-differential forms of the residence-time based models, the hyporheic flowpaths have semi-explicit spatial representation (parameterized by travel time), thus allowing coupling to general biogeochemical models. The approach has been implemented as a stream-corridor subgrid model in the open-source integrated surface/subsurface modeling software ATS. We use bedform-driven flow coupled to a biogeochemical model with explicit microbial biomass dynamics as an example to show that the subgrid representation is able to represent redox zonation in sediments and resulting effects on metal biogeochemical dynamics in a tractable manner that can be scaled to reach scales.

A Multi-Marker Genetic Association Test Based on the Rasch Model Applied to Alzheimer's Disease.

Directory of Open Access Journals (Sweden)

Wenjia Wang

Full Text Available Results from Genome-Wide Association Studies (GWAS have shown that the genetic basis of complex traits often include many genetic variants with small to moderate effects whose identification remains a challenging problem. In this context multi-marker analysis at the gene and pathway level can complement traditional point-wise approaches that treat the genetic markers individually. In this paper we propose a novel statistical approach for multi-marker analysis based on the Rasch model. The method summarizes the categorical genotypes of SNPs by a generalized logistic function into a genetic score that can be used for association analysis. Through different sets of simulations, the false-positive rate and power of the proposed approach are compared to a set of existing methods, and shows good performances. The application of the Rasch model on Alzheimer's Disease (AD ADNI GWAS dataset also allows a coherent interpretation of the results. Our analysis supports the idea that APOE is a major susceptibility gene for AD. In the top genes selected by proposed method, several could be functionally linked to AD. In particular, a pathway analysis of these genes also highlights the metabolism of cholesterol, that is known to play a key role in AD pathogenesis. Interestingly, many of these top genes can be integrated in a hypothetic signalling network.

Software reliability growth models with normal failure time distributions

International Nuclear Information System (INIS)

Okamura, Hiroyuki; Dohi, Tadashi; Osaki, Shunji

2013-01-01

This paper proposes software reliability growth models (SRGM) where the software failure time follows a normal distribution. The proposed model is mathematically tractable and has sufficient ability of fitting to the software failure data. In particular, we consider the parameter estimation algorithm for the SRGM with normal distribution. The developed algorithm is based on an EM (expectation-maximization) algorithm and is quite simple for implementation as software application. Numerical experiment is devoted to investigating the fitting ability of the SRGMs with normal distribution through 16 types of failure time data collected in real software projects

Inferring genetic parameters of lactation in Tropical Milking Criollo cattle with random regression test-day models.

Science.gov (United States)

Santellano-Estrada, E; Becerril-Pérez, C M; de Alba, J; Chang, Y M; Gianola, D; Torres-Hernández, G; Ramírez-Valverde, R

2008-11-01

This study inferred genetic and permanent environmental variation of milk yield in Tropical Milking Criollo cattle and compared 5 random regression test-day models using Wilmink's function and Legendre polynomials. Data consisted of 15,377 test-day records from 467 Tropical Milking Criollo cows that calved between 1974 and 2006 in the tropical lowlands of the Gulf Coast of Mexico and in southern Nicaragua. Estimated heritabilities of test-day milk yields ranged from 0.18 to 0.45, and repeatabilities ranged from 0.35 to 0.68 for the period spanning from 6 to 400 d in milk. Genetic correlation between days in milk 10 and 400 was around 0.50 but greater than 0.90 for most pairs of test days. The model that used first-order Legendre polynomials for additive genetic effects and second-order Legendre polynomials for permanent environmental effects gave the smallest residual variance and was also favored by the Akaike information criterion and likelihood ratio tests.

Optimizing models for production and inventory control using a genetic algorithm

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Dragan S. Pamučar

2012-01-01

Full Text Available In order to make the Economic Production Quantity (EPQ model more applicable to real-world production and inventory control problems, in this paper we expand this model by assuming that some imperfect items of different product types being produced such as reworks are allowed. In addition, we may have more than one product and supplier along with warehouse space and budget limitation. We show that the model of the problem is a constrained non-linear integer program and propose a genetic algorithm to solve it. Moreover, a design of experiments is employed to calibrate the parameters of the algorithm for different problem sizes. In the end, a numerical example is presented to demonstrate the application of the proposed methodology.

Use of genomic models to study genetic control of environmental variance

DEFF Research Database (Denmark)

Yang, Ye; Christensen, Ole Fredslund; Sorensen, Daniel

2011-01-01

. The genomic model commonly found in the literature, with marker effects affecting mean only, is extended to investigate putative effects at the level of the environmental variance. Two classes of models are proposed and their behaviour, studied using simulated data, indicates that they are capable...... of detecting genetic variation at the level of mean and variance. Implementation is via Markov chain Monte Carlo (McMC) algorithms. The models are compared in terms of a measure of global fit, in their ability to detect QTL effects and in terms of their predictive power. The models are subsequently fitted...... to back fat thickness data in pigs. The analysis of back fat thickness shows that the data support genomic models with effects on the mean but not on the variance. The relative sizes of experiment necessary to detect effects on mean and variance is discussed and an extension of the McMC algorithm...

Fatal Prion Disease in a Mouse Model of Genetic E200K Creutzfeldt-Jakob Disease

Science.gov (United States)

Friedman-Levi, Yael; Meiner, Zeev; Canello, Tamar; Frid, Kati; Kovacs, Gabor G.; Budka, Herbert; Avrahami, Dana; Gabizon, Ruth

2011-01-01

Genetic prion diseases are late onset fatal neurodegenerative disorders linked to pathogenic mutations in the prion protein-encoding gene, PRNP. The most prevalent of these is the substitution of Glutamate for Lysine at codon 200 (E200K), causing genetic Creutzfeldt-Jakob disease (gCJD) in several clusters, including Jews of Libyan origin. Investigating the pathogenesis of genetic CJD, as well as developing prophylactic treatments for young asymptomatic carriers of this and other PrP mutations, may well depend upon the availability of appropriate animal models in which long term treatments can be evaluated for efficacy and toxicity. Here we present the first effective mouse model for E200KCJD, which expresses chimeric mouse/human (TgMHu2M) E199KPrP on both a null and a wt PrP background, as is the case for heterozygous patients and carriers. Mice from both lines suffered from distinct neurological symptoms as early as 5–6 month of age and deteriorated to death several months thereafter. Histopathological examination of the brain and spinal cord revealed early gliosis and age-related intraneuronal deposition of disease-associated PrP similarly to human E200K gCJD. Concomitantly we detected aggregated, proteinase K resistant, truncated and oxidized PrP forms on immunoblots. Inoculation of brain extracts from TgMHu2ME199K mice readily induced, the first time for any mutant prion transgenic model, a distinct fatal prion disease in wt mice. We believe that these mice may serve as an ideal platform for the investigation of the pathogenesis of genetic prion disease and thus for the monitoring of anti-prion treatments. PMID:22072968

Fatal prion disease in a mouse model of genetic E200K Creutzfeldt-Jakob disease.

Directory of Open Access Journals (Sweden)

Yael Friedman-Levi

2011-11-01

Full Text Available Genetic prion diseases are late onset fatal neurodegenerative disorders linked to pathogenic mutations in the prion protein-encoding gene, PRNP. The most prevalent of these is the substitution of Glutamate for Lysine at codon 200 (E200K, causing genetic Creutzfeldt-Jakob disease (gCJD in several clusters, including Jews of Libyan origin. Investigating the pathogenesis of genetic CJD, as well as developing prophylactic treatments for young asymptomatic carriers of this and other PrP mutations, may well depend upon the availability of appropriate animal models in which long term treatments can be evaluated for efficacy and toxicity. Here we present the first effective mouse model for E200KCJD, which expresses chimeric mouse/human (TgMHu2M E199KPrP on both a null and a wt PrP background, as is the case for heterozygous patients and carriers. Mice from both lines suffered from distinct neurological symptoms as early as 5-6 month of age and deteriorated to death several months thereafter. Histopathological examination of the brain and spinal cord revealed early gliosis and age-related intraneuronal deposition of disease-associated PrP similarly to human E200K gCJD. Concomitantly we detected aggregated, proteinase K resistant, truncated and oxidized PrP forms on immunoblots. Inoculation of brain extracts from TgMHu2ME199K mice readily induced, the first time for any mutant prion transgenic model, a distinct fatal prion disease in wt mice. We believe that these mice may serve as an ideal platform for the investigation of the pathogenesis of genetic prion disease and thus for the monitoring of anti-prion treatments.

Modeling intelligent adversaries for terrorism risk assessment: some necessary conditions for adversary models.

Science.gov (United States)

Guikema, Seth

2012-07-01

Intelligent adversary modeling has become increasingly important for risk analysis, and a number of different approaches have been proposed for incorporating intelligent adversaries in risk analysis models. However, these approaches are based on a range of often-implicit assumptions about the desirable properties of intelligent adversary models. This "Perspective" paper aims to further risk analysis for situations involving intelligent adversaries by fostering a discussion of the desirable properties for these models. A set of four basic necessary conditions for intelligent adversary models is proposed and discussed. These are: (1) behavioral accuracy to the degree possible, (2) computational tractability to support decision making, (3) explicit consideration of uncertainty, and (4) ability to gain confidence in the model. It is hoped that these suggested necessary conditions foster discussion about the goals and assumptions underlying intelligent adversary modeling in risk analysis. © 2011 Society for Risk Analysis.

MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome.

Science.gov (United States)

Wang, Julia; Al-Ouran, Rami; Hu, Yanhui; Kim, Seon-Young; Wan, Ying-Wooi; Wangler, Michael F; Yamamoto, Shinya; Chao, Hsiao-Tuan; Comjean, Aram; Mohr, Stephanie E; Perrimon, Norbert; Liu, Zhandong; Bellen, Hugo J

2017-06-01

One major challenge encountered with interpreting human genetic variants is the limited understanding of the functional impact of genetic alterations on biological processes. Furthermore, there remains an unmet demand for an efficient survey of the wealth of information on human homologs in model organisms across numerous databases. To efficiently assess the large volume of publically available information, it is important to provide a concise summary of the most relevant information in a rapid user-friendly format. To this end, we created MARRVEL (model organism aggregated resources for rare variant exploration). MARRVEL is a publicly available website that integrates information from six human genetic databases and seven model organism databases. For any given variant or gene, MARRVEL displays information from OMIM, ExAC, ClinVar, Geno2MP, DGV, and DECIPHER. Importantly, it curates model organism-specific databases to concurrently display a concise summary regarding the human gene homologs in budding and fission yeast, worm, fly, fish, mouse, and rat on a single webpage. Experiment-based information on tissue expression, protein subcellular localization, biological process, and molecular function for the human gene and homologs in the seven model organisms are arranged into a concise output. Hence, rather than visiting multiple separate databases for variant and gene analysis, users can obtain important information by searching once through MARRVEL. Altogether, MARRVEL dramatically improves efficiency and accessibility to data collection and facilitates analysis of human genes and variants by cross-disciplinary integration of 18 million records available in public databases to facilitate clinical diagnosis and basic research. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Elaboration of the Reciprocal-Engagement Model of Genetic Counseling Practice: a Qualitative Investigation of Goals and Strategies.

Science.gov (United States)

Redlinger-Grosse, Krista; Veach, Patricia McCarthy; LeRoy, Bonnie S; Zierhut, Heather

2017-12-01

As the genetic counseling field evolves, a comprehensive model of practice is critical. The Reciprocal-Engagement Model (REM) consists of 5 tenets and 17 goals. Lacking in the REM, however, are well-articulated counselor strategies and behaviors. The purpose of the present study was to further elaborate and provide supporting evidence for the REM by identifying and mapping genetic counseling strategies to the REM goals. A secondary, qualitative analysis was conducted on data from two prior studies: 1) focus group results of genetic counseling outcomes (Redlinger-Grosse et al., Journal of Genetic Counseling, 2015); and 2) genetic counselors' examples of successful and unsuccessful genetic counseling sessions (Geiser et al. 2009). Using directed content analysis, 337 unique strategies were extracted from focus group data. A Q-sort of the 337 strategies yielded 15 broader strategy domains that were then mapped to the successful and unsuccessful session examples. Differing prevalence of strategy domains identified in successful sessions versus the prevalence of domains identified as lacking in unsuccessful sessions provide further support for the REM goals. The most prevalent domains for successful sessions were Information Giving and Use Psychosocial Skills and Strategies; and for unsuccessful sessions, Information Giving and Establish Working Alliance. Identified strategies support the REM's reciprocal nature, especially with regard to addressing patients' informational and psychosocial needs. Patients' contributions to success (or lack thereof) of sessions was also noted, supporting a REM tenet that individual characteristics and the counselor-patient relationship are central to processes and outcomes. The elaborated REM could be used as a framework for certain graduate curricular objectives, and REM components could also inform process and outcomes research studies to document and further characterize genetic counselor strategies.

Multilocus genetic models of handedness closely resemble single-locus models in explaining family data and are compatible with genome-wide association studies.

Science.gov (United States)

McManus, I C; Davison, Angus; Armour, John A L

2013-06-01

Right- and left-handedness run in families, show greater concordance in monozygotic than dizygotic twins, and are well described by single-locus Mendelian models. Here we summarize a large genome-wide association study (GWAS) that finds no significant associations with handedness and is consistent with a meta-analysis of GWASs. The GWAS had 99% power to detect a single locus using the conventional criterion of P < 5 × 10(-8) for the single locus models of McManus and Annett. The strong conclusion is that handedness is not controlled by a single genetic locus. A consideration of the genetic architecture of height, primary ciliary dyskinesia, and intelligence suggests that handedness inheritance can be explained by a multilocus variant of the McManus DC model, classical effects on family and twins being barely distinguishable from the single locus model. Based on the ENGAGE meta-analysis of GWASs, we estimate at least 40 loci are involved in determining handedness. © 2013 New York Academy of Sciences.

Genetic structure and bio-climatic modeling support allopatric over parapatric speciation along a latitudinal gradient

Directory of Open Access Journals (Sweden)

Rossetto Maurizio

2012-08-01

Full Text Available Abstract Background Four of the five species of Telopea (Proteaceae are distributed in a latitudinal replacement pattern on the south-eastern Australian mainland. In similar circumstances, a simple allopatric speciation model that identifies the origins of genetic isolation within temporal geographic separation is considered as the default model. However, secondary contact between differentiated lineages can result in similar distributional patterns to those arising from a process of parapatric speciation (where gene flow between lineages remains uninterrupted during differentiation. Our aim was to use the characteristic distributional patterns in Telopea to test whether it reflected the evolutionary models of allopatric or parapatric speciation. Using a combination of genetic evidence and environmental niche modelling, we focused on three main questions: do currently described geographic borders coincide with genetic and environmental boundaries; are there hybrid zones in areas of secondary contact between closely related species; did species distributions contract during the last glacial maximum resulting in distributional gaps even where overlap and hybridisation currently occur? Results Total genomic DNA was extracted from 619 individuals sampled from 36 populations representing the four species. Seven nuclear microsatellites (nSSR and six chloroplast microsatellites (cpSSR were amplified across all populations. Genetic structure and the signature of admixture in overlap zones was described using the Bayesian clustering methods implemented in STUCTURE and NewHybrids respectively. Relationships between chlorotypes were reconstructed as a median-joining network. Environmental niche models were produced for all species using environmental parameters from both the present day and the last glacial maximum (LGM. The nSSR loci amplified a total of 154 alleles, while data for the cpSSR loci produced a network of six chlorotypes. STRUCTURE revealed

Using probability modelling and genetic parentage assignment to test the role of local mate availability in mating system variation.

Science.gov (United States)

Blyton, Michaela D J; Banks, Sam C; Peakall, Rod; Lindenmayer, David B

2012-02-01

The formal testing of mating system theories with empirical data is important for evaluating the relative importance of different processes in shaping mating systems in wild populations. Here, we present a generally applicable probability modelling framework to test the role of local mate availability in determining a population's level of genetic monogamy. We provide a significance test for detecting departures in observed mating patterns from model expectations based on mate availability alone, allowing the presence and direction of behavioural effects to be inferred. The assessment of mate availability can be flexible and in this study it was based on population density, sex ratio and spatial arrangement. This approach provides a useful tool for (1) isolating the effect of mate availability in variable mating systems and (2) in combination with genetic parentage analyses, gaining insights into the nature of mating behaviours in elusive species. To illustrate this modelling approach, we have applied it to investigate the variable mating system of the mountain brushtail possum (Trichosurus cunninghami) and compared the model expectations with the outcomes of genetic parentage analysis over an 18-year study. The observed level of monogamy was higher than predicted under the model. Thus, behavioural traits, such as mate guarding or selective mate choice, may increase the population level of monogamy. We show that combining genetic parentage data with probability modelling can facilitate an improved understanding of the complex interactions between behavioural adaptations and demographic dynamics in driving mating system variation. © 2011 Blackwell Publishing Ltd.

Broad Bandwidth or High Fidelity? Evidence from the Structure of Genetic and Environmental Effects on the Facets of the Five Factor Model

Science.gov (United States)

Briley, Daniel A.; Tucker-Drob, Elliot M.

2017-01-01

The Five Factor Model (FFM) of personality is well-established at the phenotypic level, but much less is known about the coherence of the genetic and environmental influences within each personality domain. Univariate behavioral genetic analyses have consistently found the influence of additive genes and nonshared environment on multiple personality facets, but the extent to which genetic and environmental influences on specific facets reflect more general influences on higher order factors is less clear. We applied a multivariate quantitative-genetic approach to scores on the CPI-Big Five facets for 490 monozygotic and 317 dizygotic twins who took part in the National Merit Twin Study. Our results revealed a complex genetic structure for facets composing all five factors, with both domain-general and facet-specific genetic and environmental influences. Models that required common genetic and environmental influences on each facet to occur by way of effects on a higher order trait did not fit as well as models allowing for common genetic and environmental effects to act directly on the facets for three of the Big Five domains. These results add to the growing body of literature indicating that important variation in personality occurs at the facet level which may be overshadowed by aggregating to the trait level. Research at the facet level, rather than the factor level, is likely to have pragmatic advantages in future research on the genetics of personality. PMID:22695681

A genetic algorithm-based job scheduling model for big data analytics.

Science.gov (United States)

Lu, Qinghua; Li, Shanshan; Zhang, Weishan; Zhang, Lei

Big data analytics (BDA) applications are a new category of software applications that process large amounts of data using scalable parallel processing infrastructure to obtain hidden value. Hadoop is the most mature open-source big data analytics framework, which implements the MapReduce programming model to process big data with MapReduce jobs. Big data analytics jobs are often continuous and not mutually separated. The existing work mainly focuses on executing jobs in sequence, which are often inefficient and consume high energy. In this paper, we propose a genetic algorithm-based job scheduling model for big data analytics applications to improve the efficiency of big data analytics. To implement the job scheduling model, we leverage an estimation module to predict the performance of clusters when executing analytics jobs. We have evaluated the proposed job scheduling model in terms of feasibility and accuracy.

Using genetic algorithms to calibrate a water quality model.

Science.gov (United States)

Liu, Shuming; Butler, David; Brazier, Richard; Heathwaite, Louise; Khu, Soon-Thiam

2007-03-15

With the increasing concern over the impact of diffuse pollution on water bodies, many diffuse pollution models have been developed in the last two decades. A common obstacle in using such models is how to determine the values of the model parameters. This is especially true when a model has a large number of parameters, which makes a full range of calibration expensive in terms of computing time. Compared with conventional optimisation approaches, soft computing techniques often have a faster convergence speed and are more efficient for global optimum searches. This paper presents an attempt to calibrate a diffuse pollution model using a genetic algorithm (GA). Designed to simulate the export of phosphorus from diffuse sources (agricultural land) and point sources (human), the Phosphorus Indicators Tool (PIT) version 1.1, on which this paper is based, consisted of 78 parameters. Previous studies have indicated the difficulty of full range model calibration due to the number of parameters involved. In this paper, a GA was employed to carry out the model calibration in which all parameters were involved. A sensitivity analysis was also performed to investigate the impact of operators in the GA on its effectiveness in optimum searching. The calibration yielded satisfactory results and required reasonable computing time. The application of the PIT model to the Windrush catchment with optimum parameter values was demonstrated. The annual P loss was predicted as 4.4 kg P/ha/yr, which showed a good fitness to the observed value.

Comparing ESC and iPSC—Based Models for Human Genetic Disorders

Directory of Open Access Journals (Sweden)

Tomer Halevy

2014-10-01

Full Text Available Traditionally, human disorders were studied using animal models or somatic cells taken from patients. Such studies enabled the analysis of the molecular mechanisms of numerous disorders, and led to the discovery of new treatments. Yet, these systems are limited or even irrelevant in modeling multiple genetic diseases. The isolation of human embryonic stem cells (ESCs from diseased blastocysts, the derivation of induced pluripotent stem cells (iPSCs from patients’ somatic cells, and the new technologies for genome editing of pluripotent stem cells have opened a new window of opportunities in the field of disease modeling, and enabled studying diseases that couldn’t be modeled in the past. Importantly, despite the high similarity between ESCs and iPSCs, there are several fundamental differences between these cells, which have important implications regarding disease modeling. In this review we compare ESC-based models to iPSC-based models, and highlight the advantages and disadvantages of each system. We further suggest a roadmap for how to choose the optimal strategy to model each specific disorder.

Comparing ESC and iPSC-Based Models for Human Genetic Disorders.

Science.gov (United States)

Halevy, Tomer; Urbach, Achia

2014-10-24

Traditionally, human disorders were studied using animal models or somatic cells taken from patients. Such studies enabled the analysis of the molecular mechanisms of numerous disorders, and led to the discovery of new treatments. Yet, these systems are limited or even irrelevant in modeling multiple genetic diseases. The isolation of human embryonic stem cells (ESCs) from diseased blastocysts, the derivation of induced pluripotent stem cells (iPSCs) from patients' somatic cells, and the new technologies for genome editing of pluripotent stem cells have opened a new window of opportunities in the field of disease modeling, and enabled studying diseases that couldn't be modeled in the past. Importantly, despite the high similarity between ESCs and iPSCs, there are several fundamental differences between these cells, which have important implications regarding disease modeling. In this review we compare ESC-based models to iPSC-based models, and highlight the advantages and disadvantages of each system. We further suggest a roadmap for how to choose the optimal strategy to model each specific disorder.

Using genetic algorithms for calibrating simplified models of nuclear reactor dynamics

International Nuclear Information System (INIS)

Marseguerra, Marzio; Zio, Enrico; Canetta, Raffaele

2004-01-01

In this paper the use of genetic algorithms for the estimation of the effective parameters of a model of nuclear reactor dynamics is investigated. The calibration of the effective parameters is achieved by best fitting the model responses of the quantities of interest (e.g., reactor power, average fuel and coolant temperatures) to the actual evolution profiles, here simulated by the Quandry based reactor kinetics (Quark) code available from the Nuclear Energy Agency. Alternative schemes of single- and multi-objective optimization are investigated. The efficiency of convergence of the algorithm with respect to the different effective parameters to be calibrated is studied with reference to the physical relationships involved

Using genetic algorithms for calibrating simplified models of nuclear reactor dynamics

Energy Technology Data Exchange (ETDEWEB)

Marseguerra, Marzio E-mail: marzio.marseguerra@polimi.it; Zio, Enrico E-mail: enrico.zio@polimi.it; Canetta, Raffaele

2004-07-01

In this paper the use of genetic algorithms for the estimation of the effective parameters of a model of nuclear reactor dynamics is investigated. The calibration of the effective parameters is achieved by best fitting the model responses of the quantities of interest (e.g., reactor power, average fuel and coolant temperatures) to the actual evolution profiles, here simulated by the Quandry based reactor kinetics (Quark) code available from the Nuclear Energy Agency. Alternative schemes of single- and multi-objective optimization are investigated. The efficiency of convergence of the algorithm with respect to the different effective parameters to be calibrated is studied with reference to the physical relationships involved.

Integrating the landscape epidemiology and genetics of RNA viruses: rabies in domestic dogs as a model.

Science.gov (United States)

Brunker, K; Hampson, K; Horton, D L; Biek, R

2012-12-01

Landscape epidemiology and landscape genetics combine advances in molecular techniques, spatial analyses and epidemiological models to generate a more real-world understanding of infectious disease dynamics and provide powerful new tools for the study of RNA viruses. Using dog rabies as a model we have identified how key questions regarding viral spread and persistence can be addressed using a combination of these techniques. In contrast to wildlife rabies, investigations into the landscape epidemiology of domestic dog rabies requires more detailed assessment of the role of humans in disease spread, including the incorporation of anthropogenic landscape features, human movements and socio-cultural factors into spatial models. In particular, identifying and quantifying the influence of anthropogenic features on pathogen spread and measuring the permeability of dispersal barriers are important considerations for planning control strategies, and may differ according to cultural, social and geographical variation across countries or continents. Challenges for dog rabies research include the development of metapopulation models and transmission networks using genetic information to uncover potential source/sink dynamics and identify the main routes of viral dissemination. Information generated from a landscape genetics approach will facilitate spatially strategic control programmes that accommodate for heterogeneities in the landscape and therefore utilise resources in the most cost-effective way. This can include the efficient placement of vaccine barriers, surveillance points and adaptive management for large-scale control programmes.

Parameter identification of ZnO surge arrester models based on genetic algorithms

Energy Technology Data Exchange (ETDEWEB)

Bayadi, Abdelhafid [Laboratoire d' Automatique de Setif, Departement d' Electrotechnique, Faculte des Sciences de l' Ingenieur, Universite Ferhat ABBAS de Setif, Route de Bejaia Setif 19000 (Algeria)

2008-07-15

The correct and adequate modelling of ZnO surge arresters characteristics is very important for insulation coordination studies and systems reliability. In this context many researchers addressed considerable efforts to the development of surge arresters models to reproduce the dynamic characteristics observed in their behaviour when subjected to fast front impulse currents. The difficulties with these models reside essentially in the calculation and the adjustment of their parameters. This paper proposes a new technique based on genetic algorithm to obtain the best possible series of parameter values of ZnO surge arresters models. The validity of the predicted parameters is then checked by comparing the predicted results with the experimental results available in the literature. Using the ATP-EMTP package, an application of the arrester model on network system studies is presented and discussed. (author)

Theory and Practice in Quantitative Genetics

DEFF Research Database (Denmark)

Posthuma, Daniëlle; Beem, A Leo; de Geus, Eco J C

2003-01-01

With the rapid advances in molecular biology, the near completion of the human genome, the development of appropriate statistical genetic methods and the availability of the necessary computing power, the identification of quantitative trait loci has now become a realistic prospect for quantitative...... geneticists. We briefly describe the theoretical biometrical foundations underlying quantitative genetics. These theoretical underpinnings are translated into mathematical equations that allow the assessment of the contribution of observed (using DNA samples) and unobserved (using known genetic relationships......) genetic variation to population variance in quantitative traits. Several statistical models for quantitative genetic analyses are described, such as models for the classical twin design, multivariate and longitudinal genetic analyses, extended twin analyses, and linkage and association analyses. For each...

Development of a tiered and binned genetic counseling model for informed consent in the era of multiplex testing for cancer susceptibility.

Science.gov (United States)

Bradbury, Angela R; Patrick-Miller, Linda; Long, Jessica; Powers, Jacquelyn; Stopfer, Jill; Forman, Andrea; Rybak, Christina; Mattie, Kristin; Brandt, Amanda; Chambers, Rachelle; Chung, Wendy K; Churpek, Jane; Daly, Mary B; Digiovanni, Laura; Farengo-Clark, Dana; Fetzer, Dominique; Ganschow, Pamela; Grana, Generosa; Gulden, Cassandra; Hall, Michael; Kohler, Lynne; Maxwell, Kara; Merrill, Shana; Montgomery, Susan; Mueller, Rebecca; Nielsen, Sarah; Olopade, Olufunmilayo; Rainey, Kimberly; Seelaus, Christina; Nathanson, Katherine L; Domchek, Susan M

2015-06-01

Multiplex genetic testing, including both moderate- and high-penetrance genes for cancer susceptibility, is associated with greater uncertainty than traditional testing, presenting challenges to informed consent and genetic counseling. We sought to develop a new model for informed consent and genetic counseling for four ongoing studies. Drawing from professional guidelines, literature, conceptual frameworks, and clinical experience, a multidisciplinary group developed a tiered-binned genetic counseling approach proposed to facilitate informed consent and improve outcomes of cancer susceptibility multiplex testing. In this model, tier 1 "indispensable" information is presented to all patients. More specific tier 2 information is provided to support variable informational needs among diverse patient populations. Clinically relevant information is "binned" into groups to minimize information overload, support informed decision making, and facilitate adaptive responses to testing. Seven essential elements of informed consent are provided to address the unique limitations, risks, and uncertainties of multiplex testing. A tiered-binned model for informed consent and genetic counseling has the potential to address the challenges of multiplex testing for cancer susceptibility and to support informed decision making and adaptive responses to testing. Future prospective studies including patient-reported outcomes are needed to inform how to best incorporate multiplex testing for cancer susceptibility into clinical practice.Genet Med 17 6, 485-492.

MOESHA: A genetic algorithm for automatic calibration and estimation of parameter uncertainty and sensitivity of hydrologic models

Science.gov (United States)

Characterization of uncertainty and sensitivity of model parameters is an essential and often overlooked facet of hydrological modeling. This paper introduces an algorithm called MOESHA that combines input parameter sensitivity analyses with a genetic algorithm calibration routin...

A general framework for the evaluation of genetic association studies using multiple marginal models

DEFF Research Database (Denmark)

Kitsche, Andreas; Ritz, Christian; Hothorn, Ludwig A.

2016-01-01

OBJECTIVE: In this study, we present a simultaneous inference procedure as a unified analysis framework for genetic association studies. METHODS: The method is based on the formulation of multiple marginal models that reflect different modes of inheritance. The basic advantage of this methodology...

Olfaction in three genetic and two MPTP-induced Parkinson's disease mouse models.

Directory of Open Access Journals (Sweden)

Stefan Kurtenbach

Full Text Available Various genetic or toxin-induced mouse models are frequently used for investigation of early PD pathology. Although olfactory impairment is known to precede motor symptoms by years, it is not known whether it is caused by impairments in the brain, the olfactory epithelium, or both. In this study, we investigated the olfactory function in three genetic Parkinson's disease (PD mouse models and mice treated with MPTP intraperitoneally and intranasally. To investigate olfactory function, we performed electro-olfactogram recordings (EOGs and an olfactory behavior test (cookie-finding test. We show that neither a parkin knockout mouse strain, nor intraperitoneal MPTP treated animals display any olfactory impairment in EOG recordings and the applied behavior test. We also found no difference in the responses of the olfactory epithelium to odorants in a mouse strain over-expressing doubly mutated α-synuclein, while this mouse strain was not suitable to test olfaction in a cookie-finding test as it displays a mobility impairment. A transgenic mouse expressing mutated α-synuclein in dopaminergic neurons performed equal to control animals in the cookie-finding test. Further we show that intranasal MPTP application can cause functional damage of the olfactory epithelium.

A Consistent Pricing Model for Index Options and Volatility Derivatives

DEFF Research Database (Denmark)

Kokholm, Thomas

to be priced consistently, while allowing for jumps in volatility and returns. An affine specification using Lévy processes as building blocks leads to analytically tractable pricing formulas for volatility derivatives, such as VIX options, as well as efficient numerical methods for pricing of European options...... on the underlying asset. The model has the convenient feature of decoupling the vanilla skews from spot/volatility correlations and allowing for different conditional correlations in large and small spot/volatility moves. We show that our model can simultaneously fit prices of European options on S&P 500 across...

A Consistent Pricing Model for Index Options and Volatility Derivatives

DEFF Research Database (Denmark)

Cont, Rama; Kokholm, Thomas

2013-01-01

to be priced consistently, while allowing for jumps in volatility and returns. An affine specification using Lévy processes as building blocks leads to analytically tractable pricing formulas for volatility derivatives, such as VIX options, as well as efficient numerical methods for pricing of European options...... on the underlying asset. The model has the convenient feature of decoupling the vanilla skews from spot/volatility correlations and allowing for different conditional correlations in large and small spot/volatility moves. We show that our model can simultaneously fit prices of European options on S&P 500 across...

A model based on soil structural aspects describing the fate of genetically modified bacteria in soil

NARCIS (Netherlands)

Hoeven, van der N.; Elsas, van J.D.; Heijnen, C.E.

1996-01-01

A computer simulation model was developed which describes growth and competition of bacteria in the soil environment. In the model, soil was assumed to contain millions of pores of a few different size classes. An introduced bacterial strain, e.g. a genetically modified micro-organism (GEMMO), was

Strategies for MCMC computation in quantitative genetics

DEFF Research Database (Denmark)

Waagepetersen, Rasmus; Ibánez, N.; Sorensen, Daniel

2006-01-01

Given observations of a trait and a pedigree for a group of animals, the basic model in quantitative genetics is a linear mixed model with genetic random effects. The correlation matrix of the genetic random effects is determined by the pedigree and is typically very highdimensional but with a sp...

A Continuous Correlated Beta Process Model for Genetic Ancestry in Admixed Populations.

Science.gov (United States)

Gompert, Zachariah

2016-01-01

Admixture and recombination create populations and genomes with genetic ancestry from multiple source populations. Analyses of genetic ancestry in admixed populations are relevant for trait and disease mapping, studies of speciation, and conservation efforts. Consequently, many methods have been developed to infer genome-average ancestry and to deconvolute ancestry into continuous local ancestry blocks or tracts within individuals. Current methods for local ancestry inference perform well when admixture occurred recently or hybridization is ongoing, or when admixture occurred in the distant past such that local ancestry blocks have fixed in the admixed population. However, methods to infer local ancestry frequencies in isolated admixed populations still segregating for ancestry do not exist. In the current paper, I develop and test a continuous correlated beta process model to fill this analytical gap. The method explicitly models autocorrelations in ancestry frequencies at the population-level and uses discriminant analysis of SNP windows to take advantage of ancestry blocks within individuals. Analyses of simulated data sets show that the method is generally accurate such that ancestry frequency estimates exhibited low root-mean-square error and were highly correlated with the true values, particularly when large (±10 or ±20) SNP windows were used. Along these lines, the proposed method outperformed post hoc inference of ancestry frequencies from a traditional hidden Markov model (i.e., the linkage model in structure), particularly when admixture occurred more distantly in the past with little on-going gene flow or was followed by natural selection. The reliability and utility of the method was further assessed by analyzing genetic ancestry in an admixed human population (Uyghur) and three populations from a hybrid zone between Mus domesticus and M. musculus. Considerable variation in ancestry frequencies was detected within and among chromosomes in the Uyghur

Genetic screening and testing in an episode-based payment model: preserving patient autonomy.

Science.gov (United States)

Sutherland, Sharon; Farrell, Ruth M; Lockwood, Charles

2014-11-01

The State of Ohio is implementing an episode-based payment model for perinatal care. All costs of care will be tabulated for each live birth and assigned to the delivering provider, creating a three-tiered model for reimbursement for care. Providers will be reimbursed as usual for care that is average in cost and quality, while instituting rewards or penalties for those outside the expected range in either domain. There are few exclusions, and all methods of genetic screening and diagnostic testing are included in the episode cost calculation as proposed. Prenatal ultrasonography, genetic screening, and diagnostic testing are critical components of the delivery of high-quality, evidence-based prenatal care. These tests provide pregnant women with key information about the pregnancy, which, in turn, allows them to work closely with their health care provider to determine optimal prenatal care. The concepts of informed consent and decision-making, cornerstones of the ethical practice of medicine, are founded on the principles of autonomy and respect for persons. These principles recognize that patients' rights to make choices and take actions are based on their personal beliefs and values. Given the personal nature of such decisions, it is critical that patients have unbarred access to prenatal genetic tests if they elect to use them as part of their prenatal care. The proposed restructuring of reimbursement creates a clear conflict between patient autonomy and physician financial incentives.

Modelling the effect of structural QSAR parameters on skin penetration using genetic programming

International Nuclear Information System (INIS)

Chung, K K; Do, D Q

2010-01-01

In order to model relationships between chemical structures and biological effects in quantitative structure–activity relationship (QSAR) data, an alternative technique of artificial intelligence computing—genetic programming (GP)—was investigated and compared to the traditional method—statistical. GP, with the primary advantage of generating mathematical equations, was employed to model QSAR data and to define the most important molecular descriptions in QSAR data. The models predicted by GP agreed with the statistical results, and the most predictive models of GP were significantly improved when compared to the statistical models using ANOVA. Recently, artificial intelligence techniques have been applied widely to analyse QSAR data. With the capability of generating mathematical equations, GP can be considered as an effective and efficient method for modelling QSAR data

A General Nonlinear Fluid Model for Reacting Plasma-Neutral Mixtures

Energy Technology Data Exchange (ETDEWEB)

Meier, E T; Shumlak, U

2012-04-06

A generalized, computationally tractable fluid model for capturing the effects of neutral particles in plasmas is derived. The model derivation begins with Boltzmann equations for singly charged ions, electrons, and a single neutral species. Electron-impact ionization, radiative recombination, and resonant charge exchange reactions are included. Moments of the reaction collision terms are detailed. Moments of the Boltzmann equations for electron, ion, and neutral species are combined to yield a two-component plasma-neutral fluid model. Separate density, momentum, and energy equations, each including reaction transfer terms, are produced for the plasma and neutral equations. The required closures for the plasma-neutral model are discussed.

Fitting and interpreting continuous-time latent Markov models for panel data.

Science.gov (United States)

Lange, Jane M; Minin, Vladimir N

2013-11-20

Multistate models characterize disease processes within an individual. Clinical studies often observe the disease status of individuals at discrete time points, making exact times of transitions between disease states unknown. Such panel data pose considerable modeling challenges. Assuming the disease process progresses accordingly, a standard continuous-time Markov chain (CTMC) yields tractable likelihoods, but the assumption of exponential sojourn time distributions is typically unrealistic. More flexible semi-Markov models permit generic sojourn distributions yet yield intractable likelihoods for panel data in the presence of reversible transitions. One attractive alternative is to assume that the disease process is characterized by an underlying latent CTMC, with multiple latent states mapping to each disease state. These models retain analytic tractability due to the CTMC framework but allow for flexible, duration-dependent disease state sojourn distributions. We have developed a robust and efficient expectation-maximization algorithm in this context. Our complete data state space consists of the observed data and the underlying latent trajectory, yielding computationally efficient expectation and maximization steps. Our algorithm outperforms alternative methods measured in terms of time to convergence and robustness. We also examine the frequentist performance of latent CTMC point and interval estimates of disease process functionals based on simulated data. The performance of estimates depends on time, functional, and data-generating scenario. Finally, we illustrate the interpretive power of latent CTMC models for describing disease processes on a dataset of lung transplant patients. We hope our work will encourage wider use of these models in the biomedical setting. Copyright © 2013 John Wiley & Sons, Ltd.

Mathematical programming models for solving in equal-sized facilities layout problems. A genetic search method

International Nuclear Information System (INIS)

Tavakkoli-Moghaddam, R.

1999-01-01

This paper present unequal-sized facilities layout solutions generated by a genetic search program. named Layout Design using a Genetic Algorithm) 9. The generalized quadratic assignment problem requiring pre-determined distance and material flow matrices as the input data and the continuous plane model employing a dynamic distance measure and a material flow matrix are discussed. Computational results on test problems are reported as compared with layout solutions generated by the branch - and bound algorithm a hybrid method merging simulated annealing and local search techniques, and an optimization process of an enveloped block

Near infrared spectrometric technique for testing fruit quality: optimisation of regression models using genetic algorithms

Science.gov (United States)

Isingizwe Nturambirwe, J. Frédéric; Perold, Willem J.; Opara, Umezuruike L.

2016-02-01

Near infrared (NIR) spectroscopy has gained extensive use in quality evaluation. It is arguably one of the most advanced spectroscopic tools in non-destructive quality testing of food stuff, from measurement to data analysis and interpretation. NIR spectral data are interpreted through means often involving multivariate statistical analysis, sometimes associated with optimisation techniques for model improvement. The objective of this research was to explore the extent to which genetic algorithms (GA) can be used to enhance model development, for predicting fruit quality. Apple fruits were used, and NIR spectra in the range from 12000 to 4000 cm-1 were acquired on both bruised and healthy tissues, with different degrees of mechanical damage. GAs were used in combination with partial least squares regression methods to develop bruise severity prediction models, and compared to PLS models developed using the full NIR spectrum. A classification model was developed, which clearly separated bruised from unbruised apple tissue. GAs helped improve prediction models by over 10%, in comparison with full spectrum-based models, as evaluated in terms of error of prediction (Root Mean Square Error of Cross-validation). PLS models to predict internal quality, such as sugar content and acidity were developed and compared to the versions optimized by genetic algorithm. Overall, the results highlighted the potential use of GA method to improve speed and accuracy of fruit quality prediction.

lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals.

Science.gov (United States)

Ziyatdinov, Andrey; Vázquez-Santiago, Miquel; Brunel, Helena; Martinez-Perez, Angel; Aschard, Hugues; Soria, Jose Manuel

2018-02-27

Quantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software. To address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project. Our software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl .

An Intelligent Model for Pairs Trading Using Genetic Algorithms.

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Huang, Chien-Feng; Hsu, Chi-Jen; Chen, Chi-Chung; Chang, Bao Rong; Li, Chen-An

2015-01-01

Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice.

Effect of Keishibukuryogan on Genetic and Dietary Obesity Models

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Fengying Gao

2015-01-01

Full Text Available Obesity has been recognized as one of the most important risk factors for a variety of chronic diseases, such as diabetes, hypertension/cardiovascular diseases, steatosis/hepatitis, and cancer. Keishibukuryogan (KBG, Gui Zhi Fu Ling Wan in Chinese is a traditional Chinese/Japanese (Kampo medicine that has been known to improve blood circulation and is also known for its anti-inflammatory or scavenging effect. In this study, we evaluated the effect of KBG in two distinct rodent models of obesity driven by either a genetic (SHR/NDmcr-cp rat model or dietary (high-fat diet-induced mouse obesity model mechanism. Although there was no significant effect on the body composition in either the SHR rat or the DIO mouse models, KBG treatment significantly decreased the serum level of leptin and liver TG level in the DIO mouse, but not in the SHR rat model. Furthermore, a lower fat deposition in liver and a smaller size of adipocytes in white adipose tissue were observed in the DIO mice treated with KBG. Importantly, we further found downregulation of genes involved in lipid metabolism in the KBG-treated liver, along with decreased liver TG and cholesterol level. Our present data experimentally support in fact that KBG can be an attractive Kampo medicine to improve obese status through a regulation of systemic leptin level and/or lipid metabolism.

General Methods for Evolutionary Quantitative Genetic Inference from Generalized Mixed Models.

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de Villemereuil, Pierre; Schielzeth, Holger; Nakagawa, Shinichi; Morrissey, Michael

2016-11-01

Methods for inference and interpretation of evolutionary quantitative genetic parameters, and for prediction of the response to selection, are best developed for traits with normal distributions. Many traits of evolutionary interest, including many life history and behavioral traits, have inherently nonnormal distributions. The generalized linear mixed model (GLMM) framework has become a widely used tool for estimating quantitative genetic parameters for nonnormal traits. However, whereas GLMMs provide inference on a statistically convenient latent scale, it is often desirable to express quantitative genetic parameters on the scale upon which traits are measured. The parameters of fitted GLMMs, despite being on a latent scale, fully determine all quantities of potential interest on the scale on which traits are expressed. We provide expressions for deriving each of such quantities, including population means, phenotypic (co)variances, variance components including additive genetic (co)variances, and parameters such as heritability. We demonstrate that fixed effects have a strong impact on those parameters and show how to deal with this by averaging or integrating over fixed effects. The expressions require integration of quantities determined by the link function, over distributions of latent values. In general cases, the required integrals must be solved numerically, but efficient methods are available and we provide an implementation in an R package, QGglmm. We show that known formulas for quantities such as heritability of traits with binomial and Poisson distributions are special cases of our expressions. Additionally, we show how fitted GLMM can be incorporated into existing methods for predicting evolutionary trajectories. We demonstrate the accuracy of the resulting method for evolutionary prediction by simulation and apply our approach to data from a wild pedigreed vertebrate population. Copyright © 2016 de Villemereuil et al.

GSEVM v.2: MCMC software to analyse genetically structured environmental variance models

DEFF Research Database (Denmark)

Ibáñez-Escriche, N; Garcia, M; Sorensen, D

2010-01-01

This note provides a description of software that allows to fit Bayesian genetically structured variance models using Markov chain Monte Carlo (MCMC). The gsevm v.2 program was written in Fortran 90. The DOS and Unix executable programs, the user's guide, and some example files are freely available...... for research purposes at http://www.bdporc.irta.es/estudis.jsp. The main feature of the program is to compute Monte Carlo estimates of marginal posterior distributions of parameters of interest. The program is quite flexible, allowing the user to fit a variety of linear models at the level of the mean...

Drag reduction of a car model by linear genetic programming control

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Li, Ruiying; Noack, Bernd R.; Cordier, Laurent; Borée, Jacques; Harambat, Fabien

2017-08-01

We investigate open- and closed-loop active control for aerodynamic drag reduction of a car model. Turbulent flow around a blunt-edged Ahmed body is examined at ReH≈ 3× 105 based on body height. The actuation is performed with pulsed jets at all trailing edges (multiple inputs) combined with a Coanda deflection surface. The flow is monitored with 16 pressure sensors distributed at the rear side (multiple outputs). We apply a recently developed model-free control strategy building on genetic programming in Dracopoulos and Kent (Neural Comput Appl 6:214-228, 1997) and Gautier et al. (J Fluid Mech 770:424-441, 2015). The optimized control laws comprise periodic forcing, multi-frequency forcing and sensor-based feedback including also time-history information feedback and combinations thereof. Key enabler is linear genetic programming (LGP) as powerful regression technique for optimizing the multiple-input multiple-output control laws. The proposed LGP control can select the best open- or closed-loop control in an unsupervised manner. Approximately 33% base pressure recovery associated with 22% drag reduction is achieved in all considered classes of control laws. Intriguingly, the feedback actuation emulates periodic high-frequency forcing. In addition, the control identified automatically the only sensor which listens to high-frequency flow components with good signal to noise ratio. Our control strategy is, in principle, applicable to all multiple actuators and sensors experiments.

Genetic determinants of cardiometabolic risk: a proposed model for phenotype association and interaction.

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Blackett, Piers R; Sanghera, Dharambir K

2013-01-01

This review provides a translational and unifying summary of metabolic syndrome genetics and highlights evidence that genetic studies are starting to unravel and untangle origins of the complex and challenging cluster of disease phenotypes. The associated genes effectively express in the brain, liver, kidney, arterial endothelium, adipocytes, myocytes, and β cells. Progression of syndrome traits has been associated with ectopic lipid accumulation in the arterial wall, visceral adipocytes, myocytes, and liver. Thus, it follows that the genetics of dyslipidemia, obesity, and nonalcoholic fatty liver disease are central in triggering progression of the syndrome to overt expression of disease traits and have become a key focus of interest for early detection and for designing prevention and treatments. To support the "birds' eye view" approach, we provide a road-map depicting commonality and interrelationships between the traits and their genetic and environmental determinants based on known risk factors, metabolic pathways, pharmacologic targets, treatment responses, gene networks, pleiotropy, and association with circadian rhythm. Although only a small portion of the known heritability is accounted for and there is insufficient support for clinical application of gene-based prediction models, there is direction and encouraging progress in a rapidly moving field that is beginning to show clinical relevance. Copyright © 2013 National Lipid Association. Published by Elsevier Inc. All rights reserved.

Substitution models for overlapping technologies - an application to fast reactor deployment

International Nuclear Information System (INIS)

Lehtinen, R.; Silvennoinen, P.; Vira, J.

1982-01-01

In this paper market penetration models are discussed in the context of interacting technologies. An increased confidence credit is proposed for a technology that can draw on other overlapping technologies. The model is also reduced to a numerically tractable form. As an application, scenarios of fast reactor deployment are derived under different assumptions on the uranium and fast reactor investment costs and by varying model parameters for the penetration of fusion and solar technologies. The market share of fast reactors in electricity generation is expected to lie between zero and 40 per cent in 2050 depending on the market parameters. (orig.) [de

Network clustering analysis using mixture exponential-family random graph models and its application in genetic interaction data.

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Wang, Yishu; Zhao, Hongyu; Deng, Minghua; Fang, Huaying; Yang, Dejie

2017-08-24

Epistatic miniarrary profile (EMAP) studies have enabled the mapping of large-scale genetic interaction networks and generated large amounts of data in model organisms. It provides an incredible set of molecular tools and advanced technologies that should be efficiently understanding the relationship between the genotypes and phenotypes of individuals. However, the network information gained from EMAP cannot be fully exploited using the traditional statistical network models. Because the genetic network is always heterogeneous, for example, the network structure features for one subset of nodes are different from those of the left nodes. Exponentialfamily random graph models (ERGMs) are a family of statistical models, which provide a principled and flexible way to describe the structural features (e.g. the density, centrality and assortativity) of an observed network. However, the single ERGM is not enough to capture this heterogeneity of networks. In this paper, we consider a mixture ERGM (MixtureEGRM) networks, which model a network with several communities, where each community is described by a single EGRM.

Nrl-Cre transgenic mouse mediates loxP recombination in developing rod photoreceptors.

Science.gov (United States)

Brightman, Diana S; Razafsky, David; Potter, Chloe; Hodzic, Didier; Chen, Shiming

2016-03-01

The developing mouse retina is a tractable model for studying neurogenesis and differentiation. Although transgenic Cre mouse lines exist to mediate conditional genetic manipulations in developing mouse retinas, none of them act specifically in early developing rods. For conditional genetic manipulations of developing retinas, a Nrl-Cre mouse line in which the Nrl promoter drives expression of Cre in rod precursors was created. The results showed that Nrl-Cre expression was specific to the retina where it drives rod-specific recombination with a temporal pattern similar to endogenous Nrl expression during retinal development. This Nrl-Cre transgene does not negatively impact retinal structure and function. Taken together, the data suggested that the Nrl-Cre mouse line was a valuable tool to drive Cre-mediated recombination specifically in developing rods. © 2016 Wiley Periodicals, Inc.

Protein engineering of the chemokine CCL20 prevents psoriasiform dermatitis in an IL-23-dependent murine model

DEFF Research Database (Denmark)

Getschman, A E; Imai, Y; Larsen, O

2017-01-01

signaling. When given in an IL-23-dependent mouse model for psoriasis, CCL20 S64C prevented psoriatic inflammation and the up-regulation of IL-17A and IL-22. Our results validate CCR6 as a tractable therapeutic target for psoriasis and demonstrate the value of CCL20 S64C as a lead compound....

A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction