Delay-independent stability of genetic regulatory networks.

Science.gov (United States)

Wu, Fang-Xiang

2011-11-01

Genetic regulatory networks can be described by nonlinear differential equations with time delays. In this paper, we study both locally and globally delay-independent stability of genetic regulatory networks, taking messenger ribonucleic acid alternative splicing into consideration. Based on nonnegative matrix theory, we first develop necessary and sufficient conditions for locally delay-independent stability of genetic regulatory networks with multiple time delays. Compared to the previous results, these conditions are easy to verify. Then we develop sufficient conditions for global delay-independent stability for genetic regulatory networks. Compared to the previous results, this sufficient condition is less conservative. To illustrate theorems developed in this paper, we analyze delay-independent stability of two genetic regulatory networks: a real-life repressilatory network with three genes and three proteins, and a synthetic gene regulatory network with five genes and seven proteins. The simulation results show that the theorems developed in this paper can effectively determine the delay-independent stability of genetic regulatory networks.

Role of Genomic Typing in Taxonomy, Evolutionary Genetics, and Microbial Epidemiology

OpenAIRE

van Belkum, Alex; Struelens, Marc; de Visser, Arjan; Verbrugh, Henri; Tibayrenc, Michel

2001-01-01

Currently, genetic typing of microorganisms is widely used in several major fields of microbiological research. Taxonomy, research aimed at elucidation of evolutionary dynamics or phylogenetic relationships, population genetics of microorganisms, and microbial epidemiology all rely on genetic typing data for discrimination between genotypes. Apart from being an essential component of these fundamental sciences, microbial typing clearly affects several areas of applied microbiogical research. ...

Role of genomic typing in taxonomy, evolutionary genetics, and microbial epidemiology.

OpenAIRE

Belkum, Alex; Struelens, M.; Visser, Arjan; Verbrugh, Henri; Tibayrench, M.

2001-01-01

textabstractCurrently, genetic typing of microorganisms is widely used in several major fields of microbiological research. Taxonomy, research aimed at elucidation of evolutionary dynamics or phylogenetic relationships, population genetics of microorganisms, and microbial epidemiology all rely on genetic typing data for discrimination between genotypes. Apart from being an essential component of these fundamental sciences, microbial typing clearly affects several areas of applied microbiologi...

Development and application of Human Genome Epidemiology

Science.gov (United States)

Xu, Jingwen

2017-12-01

Epidemiology is a science that studies distribution of diseases and health in population and its influencing factors, it also studies how to prevent and cure disease and promote health strategies and measures. Epidemiology has developed rapidly in recent years and it is an intercross subject with various other disciplines to form a series of branch disciplines such as Genetic epidemiology, molecular epidemiology, drug epidemiology and tumor epidemiology. With the implementation and completion of Human Genome Project (HGP), Human Genome Epidemiology (HuGE) has emerged at this historic moment. In this review, the development of Human Genome Epidemiology, research content, the construction and structure of relevant network, research standards, as well as the existing results and problems are briefly outlined.

Human Genome Epidemiology : A scientific foundation for using genetic information to improve health and prevent disease

Directory of Open Access Journals (Sweden)

Stefania Boccia

2005-03-01

Full Text Available

Human health is determined by the interplay of genetic factors and the environment. In this context the recent advances in human genomics are expected to play a central role in medicine and public health by providing genetic information for disease prediction and prevention.

After the completion of the human genome sequencing, a fundamental step will be represented by the translation of these discoveries into meaningful actions to improve health and prevent diseases, and the field of epidemiology plays a central role in this effort. These are some of the issues addressed by Human Genome Epidemiology –A scientific foundation for using genetic information to improve health and prevent disease, a volume edited by Prof. M. Khoury, Prof. J. Little, Prof.W. Burke and published by Oxford university Press 2004.

This book describes the important role that epidemiological methods play in the continuum from gene discovery to the development and application of genetic tests. The Authors calls this continuum human genome epidemiology (HuGE to denote an evolving field of inquiry that uses systematic applications of epidemiological methods to assess the impact of human genetic variation on health and disease.

The book is divided into four sections and it is structured to allow readers to proceed systematically from the fundamentals of genome technology and discovery, to the epidemiological approaches, to gene characterisation, to the evaluation of genetic tests and their use in health services and public health.

Genetic epidemiology of Down syndrome in Iran

OpenAIRE

Manoochehr Shariati

2005-01-01

Down syndrome is the most common autosomal abnormality and occurs in approximately 1 per 700 live births. Down syndrome accounts for about one third of all moderate and sever mental handicaps in school-aged children. To reveal genetic epidemiology of Down syndrome, 545 karyotypes of referred cases to the author were evaluated. The frequencies of three cytogenetic variants of Down syndrome were trisomy 21 (77.5%), mosaicism (18%) and chromosomal translocation (4.5%). Male to female ratio was 1...

Local literature bias in genetic epidemiology: an empirical evaluation of the Chinese literature.

Directory of Open Access Journals (Sweden)

Zhenglun Pan

2005-12-01

Full Text Available BACKGROUND: Postulated epidemiological associations are subject to several biases. We evaluated whether the Chinese literature on human genome epidemiology may offer insights on the operation of selective reporting and language biases. METHODS AND FINDINGS: We targeted 13 gene-disease associations, each already assessed by meta-analyses, including at least 15 non-Chinese studies. We searched the Chinese Journal Full-Text Database for additional Chinese studies on the same topics. We identified 161 Chinese studies on 12 of these gene-disease associations; only 20 were PubMed-indexed (seven English full-text. Many studies (14-35 per topic were available for six topics, covering diseases common in China. With one exception, the first Chinese study appeared with a time lag (2-21 y after the first non-Chinese study on the topic. Chinese studies showed significantly more prominent genetic effects than non-Chinese studies, and 48% were statistically significant per se, despite their smaller sample size (median sample size 146 versus 268, p < 0.001. The largest genetic effects were often seen in PubMed-indexed Chinese studies (65% statistically significant per se. Non-Chinese studies of Asian-descent populations (27% significant per se also tended to show somewhat more prominent genetic effects than studies of non-Asian descent (17% significant per se. CONCLUSION: Our data provide evidence for the interplay of selective reporting and language biases in human genome epidemiology. These biases may not be limited to the Chinese literature and point to the need for a global, transparent, comprehensive outlook in molecular population genetics and epidemiologic studies in general.

Genetic algorithm for neural networks optimization

Science.gov (United States)

Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

2004-11-01

This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

The genesis and evolution of the African Field Epidemiology Network

African Journals Online (AJOL)

The genesis and evolution of the African Field Epidemiology Network. David Mukanga, Mufuta Tshimanga, Frederick Wurapa, David Serwada, George Pariyo, Fred Wabwire-Mangen, Sheba Gitta, Stella Chungong, Murray Trostle, Peter Nsubuga ...

Information transmission in genetic regulatory networks: a review

International Nuclear Information System (INIS)

Tkacik, Gasper; Walczak, Aleksandra M

2011-01-01

Genetic regulatory networks enable cells to respond to changes in internal and external conditions by dynamically coordinating their gene expression profiles. Our ability to make quantitative measurements in these biochemical circuits has deepened our understanding of what kinds of computations genetic regulatory networks can perform, and with what reliability. These advances have motivated researchers to look for connections between the architecture and function of genetic regulatory networks. Transmitting information between a network's inputs and outputs has been proposed as one such possible measure of function, relevant in certain biological contexts. Here we summarize recent developments in the application of information theory to gene regulatory networks. We first review basic concepts in information theory necessary for understanding recent work. We then discuss the functional complexity of gene regulation, which arises from the molecular nature of the regulatory interactions. We end by reviewing some experiments that support the view that genetic networks responsible for early development of multicellular organisms might be maximizing transmitted 'positional information'. (topical review)

[Sex- and gender-sensitive research in epidemiology and medicine: how can this be achieved? Aims and first results of the network "Sex-/Gender-Sensitive Research in Epidemiology, Neurosciences and Genetics/Cancer Research"].

Science.gov (United States)

Jahn, I; Gansefort, D; Kindler-Röhrborn, A; Pfleiderer, B

2014-09-01

It is considered general knowledge among physicians and epidemiologists that biological and social aspects associated with being male or female have a strong influence on health and disease. Integrating these aspects into research is necessary to counteract the problems--including ethical problems--resulting from a different evidence basis for men and women. From January 2011 to June 2014 the Federal Ministry of Education and Research supported the network "Sex-/Gender-Sensitive Research in Epidemiology, Neuroscience and Genetics/Cancer Research" with three subprojects, which aimed to promote gender-sensitive research practices. The concepts and results are presented in this article. The subproject gathered data (literature analyses, questionnaires) and offered programs for young scientists. Experiences and results were collected and generalized, for instance, in the form of definitions of terms. 50 young scientists have taken part in the training program, identifying associations and barriers in sex-/gender-sensitive research. Among others, a working definition for "sex-/gender-sensitive research" was developed, as well as definitions for the terms "sex-specific" (for biological characteristics that are specific to men or women) and "sex-/gender-dependent" or "sex-/gender-associated" (for biological and social factors, for which the extent of occurrence differs between the sexes). The concepts realized by the network are well suited to stimulate further development and discussions. The definition of terms is an important base for a productive and high-yielding interdisciplinary collaboration.

Significance of epidemiological studies for estimating the genetic radiation hazards of man

International Nuclear Information System (INIS)

Stephan, G.

1982-01-01

Following a brief presentation of the fundamentals of epidemiological studies, the problems associated with such studies are discussed. Epidemiological investigations on survivors of the atomic bomb explosions in Hiroshima and Nagasaki and also on the population of Kerala, a state in south west India with a high natural radiation load, are then discussed. Consideration was given to the question whether the Down-Syndrom is a valid indicator for proving a causal relationship between radiation dose and genetic effects. (MG) [de

Epidemiologic research topics in Germany: a keyword network analysis of 2014 DGEpi conference presentations.

Science.gov (United States)

Peter, Raphael Simon; Brehme, Torben; Völzke, Henry; Muche, Rainer; Rothenbacher, Dietrich; Büchele, Gisela

2016-06-01

Knowledge of epidemiologic research topics as well as trends is useful for scientific societies, researchers and funding agencies. In recent years researchers recognized the usefulness of keyword network analysis for visualizing and analyzing scientific research topics. Therefore, we applied keyword network analysis to present an overview of current epidemiologic research topics in Germany. Accepted submissions to the 9th annual congress of the German Society for Epidemiology (DGEpi) in 2014 were used as data source. Submitters had to choose one of 19 subject areas, and were ask to provide a title, structured abstract, names of authors along with their affiliations, and a list of freely selectable keywords. Keywords had been provided for 262 (82 %) submissions, 1030 keywords in total. Overall the most common keywords were: "migration" (18 times), "prevention" (15 times), followed by "children", "cohort study", "physical activity", and "secondary data analysis" (11 times each). Some keywords showed a certain concentration under one specific subject area, e.g. "migration" with 8 of 18 in social epidemiology or "breast cancer" with 4 of 7 in cancer epidemiology. While others like "physical activity" were equally distributed over multiple subject areas (cardiovascular & metabolic diseases, ageing, methods, paediatrics, prevention & health service research). This keyword network analysis demonstrated the high diversity of epidemiologic research topics with a large number of distinct keywords as presented at the annual conference of the DGEpi.

Network-assisted crop systems genetics: network inference and integrative analysis.

Science.gov (United States)

Lee, Tak; Kim, Hyojin; Lee, Insuk

2015-04-01

Although next-generation sequencing (NGS) technology has enabled the decoding of many crop species genomes, most of the underlying genetic components for economically important crop traits remain to be determined. Network approaches have proven useful for the study of the reference plant, Arabidopsis thaliana, and the success of network-based crop genetics will also require the availability of a genome-scale functional networks for crop species. In this review, we discuss how to construct functional networks and elucidate the holistic view of a crop system. The crop gene network then can be used for gene prioritization and the analysis of resequencing-based genome-wide association study (GWAS) data, the amount of which will rapidly grow in the field of crop science in the coming years. Copyright © 2015 Elsevier Ltd. All rights reserved.

Genetic networks and soft computing.

Science.gov (United States)

Mitra, Sushmita; Das, Ranajit; Hayashi, Yoichi

2011-01-01

The analysis of gene regulatory networks provides enormous information on various fundamental cellular processes involving growth, development, hormone secretion, and cellular communication. Their extraction from available gene expression profiles is a challenging problem. Such reverse engineering of genetic networks offers insight into cellular activity toward prediction of adverse effects of new drugs or possible identification of new drug targets. Tasks such as classification, clustering, and feature selection enable efficient mining of knowledge about gene interactions in the form of networks. It is known that biological data is prone to different kinds of noise and ambiguity. Soft computing tools, such as fuzzy sets, evolutionary strategies, and neurocomputing, have been found to be helpful in providing low-cost, acceptable solutions in the presence of various types of uncertainties. In this paper, we survey the role of these soft methodologies and their hybridizations, for the purpose of generating genetic networks.

Establishing a Twin Register : An Invaluable Resource for (Behavior) Genetic, Epidemiological, Biomarker, and 'Omics' Studies

NARCIS (Netherlands)

Odintsova, Veronika V; Willemsen, Gonneke; Dolan, Conor V; Hottenga, Jouke-Jan; Martin, Nicholas G; Slagboom, P Eline; Ordoñana, Juan R; Boomsma, Dorret I

2018-01-01

Twin registers are wonderful research resources for research applications in medical and behavioral genetics, epidemiology, psychology, molecular genetics, and other areas of research. New registers continue to be launched all over the world as researchers from different disciplines recognize the

Introduction to focus issue: quantitative approaches to genetic networks.

Science.gov (United States)

Albert, Réka; Collins, James J; Glass, Leon

2013-06-01

All cells of living organisms contain similar genetic instructions encoded in the organism's DNA. In any particular cell, the control of the expression of each different gene is regulated, in part, by binding of molecular complexes to specific regions of the DNA. The molecular complexes are composed of protein molecules, called transcription factors, combined with various other molecules such as hormones and drugs. Since transcription factors are coded by genes, cellular function is partially determined by genetic networks. Recent research is making large strides to understand both the structure and the function of these networks. Further, the emerging discipline of synthetic biology is engineering novel gene circuits with specific dynamic properties to advance both basic science and potential practical applications. Although there is not yet a universally accepted mathematical framework for studying the properties of genetic networks, the strong analogies between the activation and inhibition of gene expression and electric circuits suggest frameworks based on logical switching circuits. This focus issue provides a selection of papers reflecting current research directions in the quantitative analysis of genetic networks. The work extends from molecular models for the binding of proteins, to realistic detailed models of cellular metabolism. Between these extremes are simplified models in which genetic dynamics are modeled using classical methods of systems engineering, Boolean switching networks, differential equations that are continuous analogues of Boolean switching networks, and differential equations in which control is based on power law functions. The mathematical techniques are applied to study: (i) naturally occurring gene networks in living organisms including: cyanobacteria, Mycoplasma genitalium, fruit flies, immune cells in mammals; (ii) synthetic gene circuits in Escherichia coli and yeast; and (iii) electronic circuits modeling genetic networks

[Cost estimation of an epidemiological surveillance network for animal diseases in Central Africa: a case study of the Chad network].

Science.gov (United States)

Ouagal, M; Berkvens, D; Hendrikx, P; Fecher-Bourgeois, F; Saegerman, C

2012-12-01

In sub-Saharan Africa, most epidemiological surveillance networks for animal diseases were temporarily funded by foreign aid. It should be possible for national public funds to ensure the sustainability of such decision support tools. Taking the epidemiological surveillance network for animal diseases in Chad (REPIMAT) as an example, this study aims to estimate the network's cost by identifying the various costs and expenditures for each level of intervention. The network cost was estimated on the basis of an analysis of the operational organisation of REPIMAT, additional data collected in surveys and interviews with network field workers and a market price listing for Chad. These costs were then compared with those of other epidemiological surveillance networks in West Africa. The study results indicate that REPIMAT costs account for 3% of the State budget allocated to the Ministry of Livestock. In Chad in general, as in other West African countries, fixed costs outweigh variable costs at every level of intervention. The cost of surveillance principally depends on what is needed for surveillance at the local level (monitoring stations) and at the intermediate level (official livestock sectors and regional livestock delegations) and on the cost of the necessary equipment. In African countries, the cost of surveillance per square kilometre depends on livestock density.

A genetic epidemiology approach to cyber-security.

Science.gov (United States)

Gil, Santiago; Kott, Alexander; Barabási, Albert-László

2014-07-16

While much attention has been paid to the vulnerability of computer networks to node and link failure, there is limited systematic understanding of the factors that determine the likelihood that a node (computer) is compromised. We therefore collect threat log data in a university network to study the patterns of threat activity for individual hosts. We relate this information to the properties of each host as observed through network-wide scans, establishing associations between the network services a host is running and the kinds of threats to which it is susceptible. We propose a methodology to associate services to threats inspired by the tools used in genetics to identify statistical associations between mutations and diseases. The proposed approach allows us to determine probabilities of infection directly from observation, offering an automated high-throughput strategy to develop comprehensive metrics for cyber-security.

Genetic epidemiology of hypertension: an update on the African diaspora.

Science.gov (United States)

Daniel, Harold I; Rotimi, Charles N

2003-01-01

Hypertension is a serious global public health problem, affecting approximately 600 million people worldwide. The lifetime risk of developing the condition exceeds 50% in most populations. Despite considerable success in the pharmacological treatment of hypertension in all-human populations, the health-care community still lacks understanding of how and why individuals develop chronically elevated blood pressure. This gap in knowledge, and the high prevalence of hypertension and associated complications in some populations of African descent, have led some to conclude that hypertension is a "different disease" in people of African descent. Despite considerable evidence from epidemiologic studies showing that blood pressure distribution in populations of the African diaspora spans the known spectrum for all human populations, theories in support of unique "defects" among populations of African descent continue to gain wide acceptance. To date, no known environmental factors or genetic variants relevant to the pathophysiology of human hypertension have been found to be unique to Black populations. However, available genetic epidemiologic data demonstrate differential distributions of risk factors that are consistent with current environmental and geographic origins. This review summarizes the available evidence and demonstrates that as the exposure to known risk factors for hypertension (eg, excess consumption of salt and calories, stress, sedentary lifestyle, and degree of urbanization) increases among genetically susceptible individuals, the prevalence of hypertension and associated complications also increases across populations of the African diaspora. This observation is true for all human populations.

Stochastic dynamics of genetic broadcasting networks

Science.gov (United States)

Potoyan, Davit A.; Wolynes, Peter G.

2017-11-01

The complex genetic programs of eukaryotic cells are often regulated by key transcription factors occupying or clearing out of a large number of genomic locations. Orchestrating the residence times of these factors is therefore important for the well organized functioning of a large network. The classic models of genetic switches sidestep this timing issue by assuming the binding of transcription factors to be governed entirely by thermodynamic protein-DNA affinities. Here we show that relying on passive thermodynamics and random release times can lead to a "time-scale crisis" for master genes that broadcast their signals to a large number of binding sites. We demonstrate that this time-scale crisis for clearance in a large broadcasting network can be resolved by actively regulating residence times through molecular stripping. We illustrate these ideas by studying a model of the stochastic dynamics of the genetic network of the central eukaryotic master regulator NFκ B which broadcasts its signals to many downstream genes that regulate immune response, apoptosis, etc.

The French surveillance network of Creutzfeldt-Jakob disease. Epidemiological data in France and worldwide.

Science.gov (United States)

Brandel, J-P; Peckeu, L; Haïk, S

2013-09-01

France, involved for a long time in the epidemiological surveillance of transmissible spongiform encephalopathy (TSE), created a national network of surveillance in 1991, because of the description of the first cases of Creutzfeldt-Jakob disease (CJD) linked to a treatment by growth hormone of human origin and the observation of cases of cats infected with the agent of the bovine spongiform encephalopathy in the United Kingdom (UK). The French surveillance network is integrated into the European network of surveillance since its creation in 1993. As in other countries, sporadic CJD is the most frequent form of TSE in France with an annual mortality rate of 1.44 per million. Genetic forms are most often associated with a mutation at codon 200. Among the cases of iatrogenic CJD, 13 cases of CJD after duramater grafts were observed and 119 related to treatment with growth hormone. France is the country worst affected in Europe and the world by this latter form, before the USA and UK. Since 1996, 27 cases of variant of CJD (vCJD) has been observed, making France the second country in the world most affected after the UK. No cases of transfusion-associated vCJD have been observed. Copyright © 2013. Published by Elsevier SAS.

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.

WiFi networks and malware epidemiology.

Science.gov (United States)

Hu, Hao; Myers, Steven; Colizza, Vittoria; Vespignani, Alessandro

2009-02-03

In densely populated urban areas WiFi routers form a tightly interconnected proximity network that can be exploited as a substrate for the spreading of malware able to launch massive fraudulent attacks. In this article, we consider several scenarios for the deployment of malware that spreads over the wireless channel of major urban areas in the US. We develop an epidemiological model that takes into consideration prevalent security flaws on these routers. The spread of such a contagion is simulated on real-world data for georeferenced wireless routers. We uncover a major weakness of WiFi networks in that most of the simulated scenarios show tens of thousands of routers infected in as little as 2 weeks, with the majority of the infections occurring in the first 24-48 h. We indicate possible containment and prevention measures and provide computational estimates for the rate of encrypted routers that would stop the spreading of the epidemics by placing the system below the percolation threshold.

WiFi networks and malware epidemiology

Science.gov (United States)

Hu, Hao; Myers, Steven; Colizza, Vittoria; Vespignani, Alessandro

2009-01-01

In densely populated urban areas WiFi routers form a tightly interconnected proximity network that can be exploited as a substrate for the spreading of malware able to launch massive fraudulent attacks. In this article, we consider several scenarios for the deployment of malware that spreads over the wireless channel of major urban areas in the US. We develop an epidemiological model that takes into consideration prevalent security flaws on these routers. The spread of such a contagion is simulated on real-world data for georeferenced wireless routers. We uncover a major weakness of WiFi networks in that most of the simulated scenarios show tens of thousands of routers infected in as little as 2 weeks, with the majority of the infections occurring in the first 24–48 h. We indicate possible containment and prevention measures and provide computational estimates for the rate of encrypted routers that would stop the spreading of the epidemics by placing the system below the percolation threshold. PMID:19171909

Characterizing Race/Ethnicity and Genetic Ancestry for 100,000 Subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) Cohort.

Science.gov (United States)

Banda, Yambazi; Kvale, Mark N; Hoffmann, Thomas J; Hesselson, Stephanie E; Ranatunga, Dilrini; Tang, Hua; Sabatti, Chiara; Croen, Lisa A; Dispensa, Brad P; Henderson, Mary; Iribarren, Carlos; Jorgenson, Eric; Kushi, Lawrence H; Ludwig, Dana; Olberg, Diane; Quesenberry, Charles P; Rowell, Sarah; Sadler, Marianne; Sakoda, Lori C; Sciortino, Stanley; Shen, Ling; Smethurst, David; Somkin, Carol P; Van Den Eeden, Stephen K; Walter, Lawrence; Whitmer, Rachel A; Kwok, Pui-Yan; Schaefer, Catherine; Risch, Neil

2015-08-01

Using genome-wide genotypes, we characterized the genetic structure of 103,006 participants in the Kaiser Permanente Northern California multi-ethnic Genetic Epidemiology Research on Adult Health and Aging Cohort and analyzed the relationship to self-reported race/ethnicity. Participants endorsed any of 23 race/ethnicity/nationality categories, which were collapsed into seven major race/ethnicity groups. By self-report the cohort is 80.8% white and 19.2% minority; 93.8% endorsed a single race/ethnicity group, while 6.2% endorsed two or more. Principal component (PC) and admixture analyses were generally consistent with prior studies. Approximately 17% of subjects had genetic ancestry from more than one continent, and 12% were genetically admixed, considering only nonadjacent geographical origins. Self-reported whites were spread on a continuum along the first two PCs, indicating extensive mixing among European nationalities. Self-identified East Asian nationalities correlated with genetic clustering, consistent with extensive endogamy. Individuals of mixed East Asian-European genetic ancestry were easily identified; we also observed a modest amount of European genetic ancestry in individuals self-identified as Filipinos. Self-reported African Americans and Latinos showed extensive European and African genetic ancestry, and Native American genetic ancestry for the latter. Among 3741 genetically identified parent-child pairs, 93% were concordant for self-reported race/ethnicity; among 2018 genetically identified full-sib pairs, 96% were concordant; the lower rate for parent-child pairs was largely due to intermarriage. The parent-child pairs revealed a trend toward increasing exogamy over time; the presence in the cohort of individuals endorsing multiple race/ethnicity categories creates interesting challenges and future opportunities for genetic epidemiologic studies. Copyright © 2015 by the Genetics Society of America.

A High-Level Petri Net Framework for Genetic Regulatory Networks

Directory of Open Access Journals (Sweden)

Banks Richard

2007-12-01

Full Text Available To understand the function of genetic regulatory networks in the development of cellular systems, we must not only realise the individual network entities, but also the manner by which they interact. Multi-valued networks are a promising qualitative approach for modelling such genetic regulatory networks, however, at present they have limited formal analysis techniques and tools. We present a flexible formal framework for modelling and analysing multi-valued genetic regulatory networks using high-level Petri nets and logic minimization techniques. We demonstrate our approach with a detailed case study in which part of the genetic regulatory network responsible for the carbon starvation stress response in Escherichia coli is modelled and analysed. We then compare and contrast this multivalued model to a corresponding Boolean model and consider their formal relationship.

Characterizing Race/Ethnicity and Genetic Ancestry for 100,000 Subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) Cohort

Science.gov (United States)

Banda, Yambazi; Kvale, Mark N.; Hoffmann, Thomas J.; Hesselson, Stephanie E.; Ranatunga, Dilrini; Tang, Hua; Sabatti, Chiara; Croen, Lisa A.; Dispensa, Brad P.; Henderson, Mary; Iribarren, Carlos; Jorgenson, Eric; Kushi, Lawrence H.; Ludwig, Dana; Olberg, Diane; Quesenberry, Charles P.; Rowell, Sarah; Sadler, Marianne; Sakoda, Lori C.; Sciortino, Stanley; Shen, Ling; Smethurst, David; Somkin, Carol P.; Van Den Eeden, Stephen K.; Walter, Lawrence; Whitmer, Rachel A.; Kwok, Pui-Yan; Schaefer, Catherine; Risch, Neil

2015-01-01

Using genome-wide genotypes, we characterized the genetic structure of 103,006 participants in the Kaiser Permanente Northern California multi-ethnic Genetic Epidemiology Research on Adult Health and Aging Cohort and analyzed the relationship to self-reported race/ethnicity. Participants endorsed any of 23 race/ethnicity/nationality categories, which were collapsed into seven major race/ethnicity groups. By self-report the cohort is 80.8% white and 19.2% minority; 93.8% endorsed a single race/ethnicity group, while 6.2% endorsed two or more. Principal component (PC) and admixture analyses were generally consistent with prior studies. Approximately 17% of subjects had genetic ancestry from more than one continent, and 12% were genetically admixed, considering only nonadjacent geographical origins. Self-reported whites were spread on a continuum along the first two PCs, indicating extensive mixing among European nationalities. Self-identified East Asian nationalities correlated with genetic clustering, consistent with extensive endogamy. Individuals of mixed East Asian–European genetic ancestry were easily identified; we also observed a modest amount of European genetic ancestry in individuals self-identified as Filipinos. Self-reported African Americans and Latinos showed extensive European and African genetic ancestry, and Native American genetic ancestry for the latter. Among 3741 genetically identified parent–child pairs, 93% were concordant for self-reported race/ethnicity; among 2018 genetically identified full-sib pairs, 96% were concordant; the lower rate for parent–child pairs was largely due to intermarriage. The parent–child pairs revealed a trend toward increasing exogamy over time; the presence in the cohort of individuals endorsing multiple race/ethnicity categories creates interesting challenges and future opportunities for genetic epidemiologic studies. PMID:26092716

Performance of gout definitions for genetic epidemiological studies: analysis of UK Biobank.

Science.gov (United States)

Cadzow, Murray; Merriman, Tony R; Dalbeth, Nicola

2017-08-09

Many different combinations of available data have been used to identify gout cases in large genetic studies. The aim of this study was to determine the performance of case definitions of gout using the limited items available in multipurpose cohorts for population-based genetic studies. This research was conducted using the UK Biobank Resource. Data, including genome-wide genotypes, were available for 105,421 European participants aged 40-69 years without kidney disease. Gout definitions and combinations of these definitions were identified from previous epidemiological studies. These definitions were tested for association with 30 urate-associated single-nucleotide polymorphisms (SNPs) by logistic regression, adjusted for age, sex, waist circumference, and ratio of waist circumference to height. Heritability estimates under an additive model were generated using GCTA version 1.26.0 and PLINK version 1.90b3.32 by partitioning the genome. There were 2066 (1.96%) cases defined by self-report of gout, 1652 (1.57%) defined by urate-lowering therapy (ULT) use, 382 (0.36%) defined by hospital diagnosis, 1861 (1.76%) defined by hospital diagnosis or gout-specific medications and 2295 (2.18%) defined by self-report of gout or ULT use. Association with gout at experiment-wide significance (P genetic epidemiological studies of gout.

Molecular and genetic epidemiology of cancer in low- and medium-income countries.

Science.gov (United States)

Malhotra, Jyoti

2014-01-01

Genetic and molecular factors can play an important role in an individual's cancer susceptibility and response to carcinogen exposure. Cancer susceptibility and response to carcinogen exposure can be either through inheritance of high penetrance but rare germline mutations that constitute heritable cancer syndromes, or it can be inherited as common genetic variations or polymorphisms that are associated with low to moderate risk for development of cancer. These polymorphisms can interact with environmental exposures and can influence an individual's cancer risk through multiple pathways, including affecting the rate of metabolism of carcinogens or the immune response to these toxins. Thus, these genetic polymorphisms can account for some of the geographical differences seen in cancer prevalence between different populations. This review explores the role of molecular epidemiology in the field of cancer prevention and control in low- and medium-income countries. Using data from Human Genome Project and HapMap Project, genome-wide association studies have been able to identify multiple susceptibility loci for different cancers. The field of genetic and molecular epidemiology has been further revolutionized by the discovery of newer, faster, and more efficient DNA-sequencing technologies including next-generation sequencing. The new DNA-sequencing technologies can play an important role in planning and implementation of cancer prevention and screening strategies. More research is needed in this area, especially in investigating new biomarkers and measuring gene-environment interactions. Copyright © 2014 Icahn School of Medicine at Mount Sinai. Published by Elsevier Inc. All rights reserved.

A network of genes, genetic disorders, and brain areas.

Directory of Open Access Journals (Sweden)

Satoru Hayasaka

Full Text Available The network-based approach has been used to describe the relationship among genes and various phenotypes, producing a network describing complex biological relationships. Such networks can be constructed by aggregating previously reported associations in the literature from various databases. In this work, we applied the network-based approach to investigate how different brain areas are associated to genetic disorders and genes. In particular, a tripartite network with genes, genetic diseases, and brain areas was constructed based on the associations among them reported in the literature through text mining. In the resulting network, a disproportionately large number of gene-disease and disease-brain associations were attributed to a small subset of genes, diseases, and brain areas. Furthermore, a small number of brain areas were found to be associated with a large number of the same genes and diseases. These core brain regions encompassed the areas identified by the previous genome-wide association studies, and suggest potential areas of focus in the future imaging genetics research. The approach outlined in this work demonstrates the utility of the network-based approach in studying genetic effects on the brain.

A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.

Science.gov (United States)

Koo, Ching Lee; Liew, Mei Jing; Mohamad, Mohd Saberi; Salleh, Abdul Hakim Mohamed

2013-01-01

Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology

Directory of Open Access Journals (Sweden)

Ching Lee Koo

2013-01-01

Full Text Available Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs, support vector machine (SVM, and random forests (RFs in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

Biological Networks Entropies: Examples in Neural Memory Networks, Genetic Regulation Networks and Social Epidemic Networks

Directory of Open Access Journals (Sweden)

Jacques Demongeot

2018-01-01

Full Text Available Networks used in biological applications at different scales (molecule, cell and population are of different types: neuronal, genetic, and social, but they share the same dynamical concepts, in their continuous differential versions (e.g., non-linear Wilson-Cowan system as well as in their discrete Boolean versions (e.g., non-linear Hopfield system; in both cases, the notion of interaction graph G(J associated to its Jacobian matrix J, and also the concepts of frustrated nodes, positive or negative circuits of G(J, kinetic energy, entropy, attractors, structural stability, etc., are relevant and useful for studying the dynamics and the robustness of these systems. We will give some general results available for both continuous and discrete biological networks, and then study some specific applications of three new notions of entropy: (i attractor entropy, (ii isochronal entropy and (iii entropy centrality; in three domains: a neural network involved in the memory evocation, a genetic network responsible of the iron control and a social network accounting for the obesity spread in high school environment.

Optimization of multicast optical networks with genetic algorithm

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Lv, Bo; Mao, Xiangqiao; Zhang, Feng; Qin, Xi; Lu, Dan; Chen, Ming; Chen, Yong; Cao, Jihong; Jian, Shuisheng

2007-11-01

In this letter, aiming to obtain the best multicast performance of optical network in which the video conference information is carried by specified wavelength, we extend the solutions of matrix games with the network coding theory and devise a new method to solve the complex problems of multicast network switching. In addition, an experimental optical network has been testified with best switching strategies by employing the novel numerical solution designed with an effective way of genetic algorithm. The result shows that optimal solutions with genetic algorithm are accordance with the ones with the traditional fictitious play method.

Associations of Nocturnal Blood Pressure With Cognition by Self-Identified Race in Middle-Aged and Older Adults: The GENOA (Genetic Epidemiology Network of Arteriopathy) Study.

Science.gov (United States)

Yano, Yuichiro; Butler, Kenneth R; Hall, Michael E; Schwartz, Gary L; Knopman, David S; Lirette, Seth T; Jones, Daniel W; Wilson, James G; Hall, John E; Correa, Adolfo; Turner, Stephen T; Mosley, Thomas H

2017-10-27

Whether the association of blood pressure (BP) during sleep (nocturnal BP) with cognition differs by race is unknown. Participants in the GENOA (Genetic Epidemiology Network of Arteriopathy) Study underwent ambulatory BP measurements, brain magnetic resonance imaging, and cognitive function testing (the Rey Auditory Verbal Learning Test, the Digit Symbol Substitution Task, and the Trail Making Test Part B) between 2000 and 2007. We examined multivariable linear regression models of the nocturnal BP-cognition association. Among 755 participants (mean age, 63 years; 64% women; 42% self-identified black race; 76% taking antihypertensive medication), mean nocturnal systolic BP (SBP)/diastolic BP was 126/69 mm Hg, daytime SBP/diastolic BP level was 139/82 mm Hg, and mean reduction in SBP from day to night (dipping) was 9%. Among the entire sample, a race interaction was observed in Digit Symbol Substitution Task and Trail Making Test Part B (both P cognition. Nocturnal SBP measurements may be useful in assessing the potential risk for lower cognitive function in middle-aged and older adults, particularly in black individuals. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

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

Shared genetics underlying epidemiological association between endometriosis and ovarian cancer

DEFF Research Database (Denmark)

Lu, Yi; Cuellar-Partida, Gabriel; Painter, Jodie N

2015-01-01

Epidemiological studies have demonstrated associations between endometriosis and certain histotypes of ovarian cancer, including clear cell, low-grade serous and endometrioid carcinomas. We aimed to determine whether the observed associations might be due to shared genetic aetiology. To address...... this, we used two endometriosis datasets genotyped on common arrays with full-genome coverage (3194 cases and 7060 controls) and a large ovarian cancer dataset genotyped on the customized Illumina Infinium iSelect (iCOGS) arrays (10 065 cases and 21 663 controls). Previous work has suggested...... that a large number of genetic variants contribute to endometriosis and ovarian cancer (all histotypes combined) susceptibility. Here, using the iCOGS data, we confirmed polygenic architecture for most histotypes of ovarian cancer. This led us to evaluate if the polygenic effects are shared across diseases. We...

Cannabis Beyond Good and Evil. How genetic and epidemiological factors shape the relationship between cannabis and psychosis

NARCIS (Netherlands)

Schubart, C.D.

2013-01-01

The studies presented in this thesis aimed to identify genetic and non-genetic (epidemiological) factors that shape the association between cannabis use and psychosis. We showed that the age of first use of cannabis is a determinant for the strength of the association between cannabis use and

Epidemiology, major risk factors and genetic predisposition for breast cancer in the Pakistani population.

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Shaukat, Uzma; Ismail, Muhammad; Mehmood, Nasir

2013-01-01

Occurrence of breast cancer is related to genetic as well as cultural, environmental and life-style factors. Variations in diversity of these factors among different ethnic groups and geographical areas emphasize the immense need for studies in all racial-ethnic populations. The incidence of breast cancer in Pakistan is highest in Asians after Jews in Israel and 2.5 times higher than that in neighboring countries like Iran and India, accounting for 34.6% of female cancers. The Pakistani population is deficient in information regarding breast cancer etiology and epidemiology, but efforts done so far had suggested consanguinity as a major risk factor for frequent mutations leading to breast cancer and has also shed light on genetic origins in different ethnic groups within Pakistan. World-wide research efforts on different ethnicities have enhanced our understanding of genetic predisposition to breast cancer but despite these discoveries, 75% of the familial risk of breast cancer remains unexplained, highlighting the fact that the majority of breast cancer susceptibility genes remain unidentified. For this purpose Pakistani population provides a strong genetic pool to elucidate the genetic etiology of breast cancer because of cousin marriages. In this review, we describe the known breast cancer predisposition factors found in the local Pakistani population and the epidemiological research work done to emphasize the importance of exploring factors/variants contributing to breast cance, in order to prevent, cure and decrease its incidence in our country.

Molecular epidemiology and evolutionary genetics of Mycobacterium tuberculosis in Taipei

OpenAIRE

Su Ih-Jen; Lee Shi-Yi; Tsai Wen-Shing; Sun Jun-Ren; Chang Jia-Ru; Lin Chih-Wei; Tseng Fan-Chen; Dou Horng-Yunn; Lu Jang-Jih

2008-01-01

Abstract Background The control of tuberculosis in densely populated cities is complicated by close human-to-human contacts and potential transmission of pathogens from multiple sources. We conducted a molecular epidemiologic analysis of 356 Mycobacterium tuberculosis (MTB) isolates from patients presenting pulmonary tuberculosis in metropolitan Taipei. Classical antibiogram studies and genetic characterization, using mycobacterial interspersed repetitive-unit-variable-number tandem-repeat (M...

New Algorithm and Software (BNOmics) for Inferring and Visualizing Bayesian Networks from Heterogeneous Big Biological and Genetic Data.

Science.gov (United States)

Gogoshin, Grigoriy; Boerwinkle, Eric; Rodin, Andrei S

2017-04-01

Bayesian network (BN) reconstruction is a prototypical systems biology data analysis approach that has been successfully used to reverse engineer and model networks reflecting different layers of biological organization (ranging from genetic to epigenetic to cellular pathway to metabolomic). It is especially relevant in the context of modern (ongoing and prospective) studies that generate heterogeneous high-throughput omics datasets. However, there are both theoretical and practical obstacles to the seamless application of BN modeling to such big data, including computational inefficiency of optimal BN structure search algorithms, ambiguity in data discretization, mixing data types, imputation and validation, and, in general, limited scalability in both reconstruction and visualization of BNs. To overcome these and other obstacles, we present BNOmics, an improved algorithm and software toolkit for inferring and analyzing BNs from omics datasets. BNOmics aims at comprehensive systems biology-type data exploration, including both generating new biological hypothesis and testing and validating the existing ones. Novel aspects of the algorithm center around increasing scalability and applicability to varying data types (with different explicit and implicit distributional assumptions) within the same analysis framework. An output and visualization interface to widely available graph-rendering software is also included. Three diverse applications are detailed. BNOmics was originally developed in the context of genetic epidemiology data and is being continuously optimized to keep pace with the ever-increasing inflow of available large-scale omics datasets. As such, the software scalability and usability on the less than exotic computer hardware are a priority, as well as the applicability of the algorithm and software to the heterogeneous datasets containing many data types-single-nucleotide polymorphisms and other genetic/epigenetic/transcriptome variables, metabolite

Robust and global delay-dependent stability for genetic regulatory networks with parameter uncertainties.

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Tian, Li-Ping; Wang, Jianxin; Wu, Fang-Xiang

2012-09-01

The study of stability is essential for designing or controlling genetic regulatory networks, which can be described by nonlinear differential equations with time delays. Much attention has been paid to the study of delay-independent stability of genetic regulatory networks and as a result, many sufficient conditions have been derived for delay-independent stability. Although it might be more interesting in practice, delay-dependent stability of genetic regulatory networks has been studied insufficiently. Based on the linear matrix inequality (LMI) approach, in this study we will present some delay-dependent stability conditions for genetic regulatory networks. Then we extend these results to genetic regulatory networks with parameter uncertainties. To illustrate the effectiveness of our theoretical results, gene repressilatory networks are analyzed .

Near-Optimal Resource Allocation in Cooperative Cellular Networks Using Genetic Algorithms

OpenAIRE

Luo, Zihan; Armour, Simon; McGeehan, Joe

2015-01-01

This paper shows how a genetic algorithm can be used as a method of obtaining the near-optimal solution of the resource block scheduling problem in a cooperative cellular network. An exhaustive search is initially implementedto guarantee that the optimal result, in terms of maximizing the bandwidth efficiency of the overall network, is found, and then the genetic algorithm with the properly selected termination conditions is used in the same network. The simulation results show that the genet...

Participants' perceptions of research benefits in an African genetic epidemiology study.

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Appiah-Poku, John; Newton, Sam; Kass, Nancy

2011-12-01

  Both the Council for International Organization of Medical Sciences and the Helsinki Declaration emphasize that the potential benefits of research should outweigh potential harms; consequently, some work has been conducted on participants' perception of benefits in therapeutic research. However, there appears to be very little work conducted with participants who have joined non-therapeutic research. This work was done to evaluate participants' perception of benefits in a genetic epidemiological study by examining their perception of the potential benefits of enrollment.   In-depth interviews lasting between 45 and 60 minutes were conducted with a convenient sample of 25 ill patients and 25 healthy accompanying relatives enrolled in a genetic epidemiological study of tuberculosis. Recorded interviews were transcribed and analyzed using content analysis.   Participants perceived that research was beneficial and some of the benefits included the generation of new knowledge, finding the cause of diseases, as well as the control, eradication and prevention of disease. Some thought that research was risky whilst others thought that the benefits outweighed the risks.   Participants perceived research to be beneficial and most of them thought that, though it was risky, the benefits outweighed the risks. It is our view that researchers need to give serious consideration to participant's perception of benefits in designing their consent forms, to see to the fulfillment of achievable goals. © 2011 Blackwell Publishing Ltd.

Cryptic relatedness in epidemiologic collections accessed for genetic association studies: experiences from the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study and the National Health and Nutrition Examination Surveys (NHANES).

Science.gov (United States)

Malinowski, Jennifer; Goodloe, Robert; Brown-Gentry, Kristin; Crawford, Dana C

2015-01-01

Epidemiologic collections have been a major resource for genotype-phenotype studies of complex disease given their large sample size, racial/ethnic diversity, and breadth and depth of phenotypes, traits, and exposures. A major disadvantage of these collections is they often survey households and communities without collecting extensive pedigree data. Failure to account for substantial relatedness can lead to inflated estimates and spurious associations. To examine the extent of cryptic relatedness in an epidemiologic collection, we as the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study accessed the National Health and Nutrition Examination Surveys (NHANES) linked to DNA samples ("Genetic NHANES") from NHANES III and NHANES 1999-2002. NHANES are population-based cross-sectional surveys conducted by the National Center for Health Statistics at the Centers for Disease Control and Prevention. Genome-wide genetic data is not yet available in NHANES, and current data use agreements prohibit the generation of GWAS-level data in NHANES samples due issues in maintaining confidentiality among other ethical concerns. To date, only hundreds of single nucleotide polymorphisms (SNPs) genotyped in a variety of candidate genes are available for analysis in NHANES. We performed identity-by-descent (IBD) estimates in three self-identified subpopulations of Genetic NHANES (non-Hispanic white, non- Hispanic black, and Mexican American) using PLINK software to identify potential familial relationships from presumed unrelated subjects. We then compared the PLINKidentified relationships to those identified by an alternative method implemented in Kinship-based INference for Genome-wide association studies (KING). Overall, both methods identified familial relationships in NHANES III and NHANES 1999-2002 for all three subpopulations, but little concordance was observed between the two methods due in major part to the limited SNP data available in Genetic NHANES

Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

International Nuclear Information System (INIS)

Ortiz R, J. M.; Martinez B, M. R.; Vega C, H. R.; Gallego D, E.; Lorente F, A.; Mendez V, R.; Los Arcos M, J. M.; Guerrero A, J. E.

2011-01-01

With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks (Ann) have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Ann still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning Ann parameters. In recent years the use of hybrid technologies, combining Ann and genetic algorithms, has been utilized to. In this work, several Ann topologies were trained and tested using Ann and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out. (Author)

Contact networks structured by sex underpin sex-specific epidemiology of infection.

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Silk, Matthew J; Weber, Nicola L; Steward, Lucy C; Hodgson, David J; Boots, Mike; Croft, Darren P; Delahay, Richard J; McDonald, Robbie A

2018-02-01

Contact networks are fundamental to the transmission of infection and host sex often affects the acquisition and progression of infection. However, the epidemiological impacts of sex-related variation in animal contact networks have rarely been investigated. We test the hypothesis that sex-biases in infection are related to variation in multilayer contact networks structured by sex in a population of European badgers Meles meles naturally infected with Mycobacterium bovis. Our key results are that male-male and between-sex networks are structured at broader spatial scales than female-female networks and that in male-male and between-sex contact networks, but not female-female networks, there is a significant relationship between infection and contacts with individuals in other groups. These sex differences in social behaviour may underpin male-biased acquisition of infection and may result in males being responsible for more between-group transmission. This highlights the importance of sex-related variation in host behaviour when managing animal diseases. © 2017 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd.

Asymptotic stability of a genetic network under impulsive control

International Nuclear Information System (INIS)

Li Fangfei; Sun Jitao

2010-01-01

The study of the stability of genetic network is an important motif for the understanding of the living organism at both molecular and cellular levels. In this Letter, we provide a theoretical method for analyzing the asymptotic stability of a genetic network under impulsive control. And the sufficient conditions of its asymptotic stability under impulsive control are obtained. Finally, an example is given to illustrate the effectiveness of the obtained method.

Splitting Strategy for Simulating Genetic Regulatory Networks

Directory of Open Access Journals (Sweden)

Xiong You

2014-01-01

Full Text Available The splitting approach is developed for the numerical simulation of genetic regulatory networks with a stable steady-state structure. The numerical results of the simulation of a one-gene network, a two-gene network, and a p53-mdm2 network show that the new splitting methods constructed in this paper are remarkably more effective and more suitable for long-term computation with large steps than the traditional general-purpose Runge-Kutta methods. The new methods have no restriction on the choice of stepsize due to their infinitely large stability regions.

A parallel attractor-finding algorithm based on Boolean satisfiability for genetic regulatory networks.

Directory of Open Access Journals (Sweden)

Wensheng Guo

Full Text Available In biological systems, the dynamic analysis method has gained increasing attention in the past decade. The Boolean network is the most common model of a genetic regulatory network. The interactions of activation and inhibition in the genetic regulatory network are modeled as a set of functions of the Boolean network, while the state transitions in the Boolean network reflect the dynamic property of a genetic regulatory network. A difficult problem for state transition analysis is the finding of attractors. In this paper, we modeled the genetic regulatory network as a Boolean network and proposed a solving algorithm to tackle the attractor finding problem. In the proposed algorithm, we partitioned the Boolean network into several blocks consisting of the strongly connected components according to their gradients, and defined the connection between blocks as decision node. Based on the solutions calculated on the decision nodes and using a satisfiability solving algorithm, we identified the attractors in the state transition graph of each block. The proposed algorithm is benchmarked on a variety of genetic regulatory networks. Compared with existing algorithms, it achieved similar performance on small test cases, and outperformed it on larger and more complex ones, which happens to be the trend of the modern genetic regulatory network. Furthermore, while the existing satisfiability-based algorithms cannot be parallelized due to their inherent algorithm design, the proposed algorithm exhibits a good scalability on parallel computing architectures.

Node-based measures of connectivity in genetic networks.

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Koen, Erin L; Bowman, Jeff; Wilson, Paul J

2016-01-01

At-site environmental conditions can have strong influences on genetic connectivity, and in particular on the immigration and settlement phases of dispersal. However, at-site processes are rarely explored in landscape genetic analyses. Networks can facilitate the study of at-site processes, where network nodes are used to model site-level effects. We used simulated genetic networks to compare and contrast the performance of 7 node-based (as opposed to edge-based) genetic connectivity metrics. We simulated increasing node connectivity by varying migration in two ways: we increased the number of migrants moving between a focal node and a set number of recipient nodes, and we increased the number of recipient nodes receiving a set number of migrants. We found that two metrics in particular, the average edge weight and the average inverse edge weight, varied linearly with simulated connectivity. Conversely, node degree was not a good measure of connectivity. We demonstrated the use of average inverse edge weight to describe the influence of at-site habitat characteristics on genetic connectivity of 653 American martens (Martes americana) in Ontario, Canada. We found that highly connected nodes had high habitat quality for marten (deep snow and high proportions of coniferous and mature forest) and were farther from the range edge. We recommend the use of node-based genetic connectivity metrics, in particular, average edge weight or average inverse edge weight, to model the influences of at-site habitat conditions on the immigration and settlement phases of dispersal. © 2015 John Wiley & Sons Ltd.

Adaptive logical stochastic resonance in time-delayed synthetic genetic networks

Science.gov (United States)

Zhang, Lei; Zheng, Wenbin; Song, Aiguo

2018-04-01

In the paper, the concept of logical stochastic resonance is applied to implement logic operation and latch operation in time-delayed synthetic genetic networks derived from a bacteriophage λ. Clear logic operation and latch operation can be obtained when the network is tuned by modulated periodic force and time-delay. In contrast with the previous synthetic genetic networks based on logical stochastic resonance, the proposed system has two advantages. On one hand, adding modulated periodic force to the background noise can increase the length of the optimal noise plateau of obtaining desired logic response and make the system adapt to varying noise intensity. On the other hand, tuning time-delay can extend the optimal noise plateau to larger range. The result provides possible help for designing new genetic regulatory networks paradigm based on logical stochastic resonance.

Epidemiologic and genetic characteristics of mumps viruses isolated in China from 1995 to 2010.

Science.gov (United States)

Cui, Aili; Zhu, Zhen; Chen, Meng; Zheng, Huanying; Liu, Leng; Wang, Yan; Ma, Yan; Wang, Changyin; Fang, Xueqiang; Li, Ping; Guan, Ronghui; Wang, Shuang; Zhou, Jianhui; Zheng, Lei; Gao, Hui; Ding, Zhengrong; Li, Liqun; Bo, Fang; Sun, Zhaodan; Zhang, Zhenying; Feng, Daxing; He, Jilan; Chen, Hui; Jin, Li; Rota, Paul A; Xu, Wenbo

2014-01-01

The epidemiologic and genetic characteristics of mumps viruses detected in China from 1995 to 2010 were analyzed in this study. Mumps remains endemic in China with a high overall incidence rate. The incidence of mumps in Western China was higher than that in other regions of the country. Each year, most of mumps cases occurred between April and July, but a small peak also occurred in November and December. Mumps cases primarily affected the under 15 year old age group. Virologic data demonstrated that genotype F was the predominant circulating genotype throughout China for at least 15 years and no other genotype was detected between 1995 and 2010. Analysis of sequence data from the small hydrophobic (SH) gene indicated that multiple transmission chains of genotype F were found in various provinces of China, with no apparent chronologic and geographic restriction. This is the first report describing the epidemiology of mumps and genetic characterization of mumps viruses at the national level in China. Copyright © 2013 Elsevier B.V. All rights reserved.

Genetic characterization, molecular epidemiology, and phylogenetic relationships of insect-specific viruses in the taxon Negevirus.

Science.gov (United States)

Nunes, Marcio R T; Contreras-Gutierrez, María Angélica; Guzman, Hilda; Martins, Livia C; Barbirato, Mayla Feitoza; Savit, Chelsea; Balta, Victoria; Uribe, Sandra; Vivero, Rafael; Suaza, Juan David; Oliveira, Hamilton; Nunes Neto, Joaquin P; Carvalho, Valeria L; da Silva, Sandro Patroca; Cardoso, Jedson F; de Oliveira, Rodrigo Santo; da Silva Lemos, Poliana; Wood, Thomas G; Widen, Steven G; Vasconcelos, Pedro F C; Fish, Durland; Vasilakis, Nikos; Tesh, Robert B

2017-04-01

The recently described taxon Negevirus is comprised of a diverse group of insect-specific viruses isolated from mosquitoes and phlebotomine sandflies. In this study, a comprehensive genetic characterization, molecular, epidemiological and evolutionary analyses were conducted on nearly full-length sequences of 91 new negevirus isolates obtained in Brazil, Colombia, Peru, Panama, USA and Nepal. We demonstrated that these arthropod restricted viruses are clustered in two major phylogenetic groups with origins related to three plant virus genera (Cilevirus, Higrevirus and Blunevirus). Molecular analyses demonstrated that specific host correlations are not present with most negeviruses; instead, high genetic variability, wide host-range, and cross-species transmission were noted. The data presented here also revealed the existence of five novel insect-specific viruses falling into two arthropod-restrictive virus taxa, previously proposed as distinct genera, designated Nelorpivirus and Sandewavirus. Our results provide a better understanding of the molecular epidemiology, evolution, taxonomy and stability of this group of insect-restricted viruses. Copyright © 2017 Elsevier Inc. All rights reserved.

Wind power prediction based on genetic neural network

Science.gov (United States)

Zhang, Suhan

2017-04-01

The scale of grid connected wind farms keeps increasing. To ensure the stability of power system operation, make a reasonable scheduling scheme and improve the competitiveness of wind farm in the electricity generation market, it's important to accurately forecast the short-term wind power. To reduce the influence of the nonlinear relationship between the disturbance factor and the wind power, the improved prediction model based on genetic algorithm and neural network method is established. To overcome the shortcomings of long training time of BP neural network and easy to fall into local minimum and improve the accuracy of the neural network, genetic algorithm is adopted to optimize the parameters and topology of neural network. The historical data is used as input to predict short-term wind power. The effectiveness and feasibility of the method is verified by the actual data of a certain wind farm as an example.

Genetic Network Programming with Reconstructed Individuals

Science.gov (United States)

Ye, Fengming; Mabu, Shingo; Wang, Lutao; Eto, Shinji; Hirasawa, Kotaro

A lot of research on evolutionary computation has been done and some significant classical methods such as Genetic Algorithm (GA), Genetic Programming (GP), Evolutionary Programming (EP), and Evolution Strategies (ES) have been studied. Recently, a new approach named Genetic Network Programming (GNP) has been proposed. GNP can evolve itself and find the optimal solution. It is based on the idea of Genetic Algorithm and uses the data structure of directed graphs. Many papers have demonstrated that GNP can deal with complex problems in the dynamic environments very efficiently and effectively. As a result, recently, GNP is getting more and more attentions and is used in many different areas such as data mining, extracting trading rules of stock markets, elevator supervised control systems, etc., and GNP has obtained some outstanding results. Focusing on the GNP's distinguished expression ability of the graph structure, this paper proposes a method named Genetic Network Programming with Reconstructed Individuals (GNP-RI). The aim of GNP-RI is to balance the exploitation and exploration of GNP, that is, to strengthen the exploitation ability by using the exploited information extensively during the evolution process of GNP and finally obtain better performances than that of GNP. In the proposed method, the worse individuals are reconstructed and enhanced by the elite information before undergoing genetic operations (mutation and crossover). The enhancement of worse individuals mimics the maturing phenomenon in nature, where bad individuals can become smarter after receiving a good education. In this paper, GNP-RI is applied to the tile-world problem which is an excellent bench mark for evaluating the proposed architecture. The performance of GNP-RI is compared with that of the conventional GNP. The simulation results show some advantages of GNP-RI demonstrating its superiority over the conventional GNPs.

Complex and unexpected dynamics in simple genetic regulatory networks

Science.gov (United States)

Borg, Yanika; Ullner, Ekkehard; Alagha, Afnan; Alsaedi, Ahmed; Nesbeth, Darren; Zaikin, Alexey

2014-03-01

One aim of synthetic biology is to construct increasingly complex genetic networks from interconnected simpler ones to address challenges in medicine and biotechnology. However, as systems increase in size and complexity, emergent properties lead to unexpected and complex dynamics due to nonlinear and nonequilibrium properties from component interactions. We focus on four different studies of biological systems which exhibit complex and unexpected dynamics. Using simple synthetic genetic networks, small and large populations of phase-coupled quorum sensing repressilators, Goodwin oscillators, and bistable switches, we review how coupled and stochastic components can result in clustering, chaos, noise-induced coherence and speed-dependent decision making. A system of repressilators exhibits oscillations, limit cycles, steady states or chaos depending on the nature and strength of the coupling mechanism. In large repressilator networks, rich dynamics can also be exhibited, such as clustering and chaos. In populations of Goodwin oscillators, noise can induce coherent oscillations. In bistable systems, the speed with which incoming external signals reach steady state can bias the network towards particular attractors. These studies showcase the range of dynamical behavior that simple synthetic genetic networks can exhibit. In addition, they demonstrate the ability of mathematical modeling to analyze nonlinearity and inhomogeneity within these systems.

Recruitment and Participation of Recreational Runners in a Large Epidemiological and Genetic Research Study: Retrospective Data Analysis.

Science.gov (United States)

Manzanero, Silvia; Kozlovskaia, Maria; Vlahovich, Nicole; Hughes, David C

2018-05-23

With the increasing capacity for remote collection of both data and samples for medical research, a thorough assessment is needed to determine the association of population characteristics and recruitment methodologies with response rates. The aim of this research was to assess population representativeness in a two-stage study of health and injury in recreational runners, which consisted of an epidemiological arm and genetic analysis. The cost and success of various classical and internet-based methods were analyzed, and demographic representativeness was assessed for recruitment to the epidemiological survey, reported willingness to participate in the genetic arm of the study, actual participation, sample return, and approval for biobank storage. A total of 4965 valid responses were received, of which 1664 were deemed eligible for genetic analysis. Younger age showed a negative association with initial recruitment rate, expressed willingness to participate in genetic analysis, and actual participation. Additionally, female sex was associated with higher initial recruitment rates, and ethnic origin impacted willingness to participate in the genetic analysis (all P<.001). The sharp decline in retention through the different stages of the study in young respondents suggests the necessity to develop specific recruitment and retention strategies when investigating a young, physically active population. ©Silvia Manzanero, Maria Kozlovskaia, Nicole Vlahovich, David C Hughes. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 23.05.2018.

Genetic Epidemiology of Type 2 Diabetes in Mexican Mestizos

Directory of Open Access Journals (Sweden)

Eiralí Guadalupe García-Chapa

2017-01-01

Full Text Available There are currently about 415 million people with diabetes worldwide, a figure likely to increase to 642 million by 2040. In 2015, Mexico was the second Latin American country and sixth in the world in prevalence of this disorder with nearly 11.5 million of patients. Type 2 diabetes (T2D is the main kind of diabetes and its etiology is complex with environmental and genetic factors involved. Indeed, polymorphisms in several genes have been associated with this disease worldwide. To estimate the genetic epidemiology of T2D in Mexican mestizos a systematic bibliographic search of published articles through PubMed, Scopus, Google Scholar, and Web of Science was conducted. Just case-control studies of candidate genes about T2D in Mexican mestizo inhabitants were included. Nineteen studies that met the inclusion criteria were found. In total, 68 polymorphisms of 41 genes were assessed; 26 of them were associated with T2D risk, which were located in ABCA1, ADRB3, CAPN10, CDC123/CAMK1D, CDKAL1, CDKN2A/2B, CRP, ELMO1, FTO, HHEX, IGF2BP2, IRS1, JAZF1, KCNQ1, LOC387761, LTA, NXPH1, SIRT1, SLC30A8, TCF7L2, and TNF-α genes. Overall, 21 of the 41 analyzed genes were associated with T2D in Mexican mestizos. Such a genetic heterogeneity compares with findings in other ethnic groups.

Bistable responses in bacterial genetic networks: Designs and dynamical consequences

Science.gov (United States)

Tiwari, Abhinav; Ray, J. Christian J.; Narula, Jatin; Igoshin, Oleg A.

2011-01-01

A key property of living cells is their ability to react to stimuli with specific biochemical responses. These responses can be understood through the dynamics of underlying biochemical and genetic networks. Evolutionary design principles have been well studied in networks that display graded responses, with a continuous relationship between input signal and system output. Alternatively, biochemical networks can exhibit bistable responses so that over a range of signals the network possesses two stable steady states. In this review, we discuss several conceptual examples illustrating network designs that can result in a bistable response of the biochemical network. Next, we examine manifestations of these designs in bacterial master-regulatory genetic circuits. In particular, we discuss mechanisms and dynamic consequences of bistability in three circuits: two-component systems, sigma-factor networks, and a multistep phosphorelay. Analyzing these examples allows us to expand our knowledge of evolutionary design principles for networks with bistable responses. PMID:21385588

Prediction of quantitative phenotypes based on genetic networks: a case study in yeast sporulation

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Shen Li

2010-09-01

Full Text Available Abstract Background An exciting application of genetic network is to predict phenotypic consequences for environmental cues or genetic perturbations. However, de novo prediction for quantitative phenotypes based on network topology is always a challenging task. Results Using yeast sporulation as a model system, we have assembled a genetic network from literature and exploited Boolean network to predict sporulation efficiency change upon deleting individual genes. We observe that predictions based on the curated network correlate well with the experimentally measured values. In addition, computational analysis reveals the robustness and hysteresis of the yeast sporulation network and uncovers several patterns of sporulation efficiency change caused by double gene deletion. These discoveries may guide future investigation of underlying mechanisms. We have also shown that a hybridized genetic network reconstructed from both temporal microarray data and literature is able to achieve a satisfactory prediction accuracy of the same quantitative phenotypes. Conclusions This case study illustrates the value of predicting quantitative phenotypes based on genetic network and provides a generic approach.

Nuclear reactors project optimization based on neural network and genetic algorithm

International Nuclear Information System (INIS)

Pereira, Claudio M.N.A.; Schirru, Roberto; Martinez, Aquilino S.

1997-01-01

This work presents a prototype of a system for nuclear reactor core design optimization based on genetic algorithms and artificial neural networks. A neural network is modeled and trained in order to predict the flux and the neutron multiplication factor values based in the enrichment, network pitch and cladding thickness, with average error less than 2%. The values predicted by the neural network are used by a genetic algorithm in this heuristic search, guided by an objective function that rewards the high flux values and penalizes multiplication factors far from the required value. Associating the quick prediction - that may substitute the reactor physics calculation code - with the global optimization capacity of the genetic algorithm, it was obtained a quick and effective system for nuclear reactor core design optimization. (author). 11 refs., 8 figs., 3 tabs

Comparative review of human and canine osteosarcoma: morphology, epidemiology, prognosis, treatment and genetics.

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Simpson, Siobhan; Dunning, Mark David; de Brot, Simone; Grau-Roma, Llorenç; Mongan, Nigel Patrick; Rutland, Catrin Sian

2017-10-24

Osteosarcoma (OSA) is a rare cancer in people. However OSA incidence rates in dogs are 27 times higher than in people. Prognosis in both species is relatively poor, with 5 year OSA survival rates in people not having improved in decades. For dogs, 1 year survival rates are only around ~ 45%. Improved and novel treatment regimens are urgently required to improve survival in both humans and dogs with OSA. Utilising information from genetic studies could assist in this in both species, with the higher incidence rates in dogs contributing to the dog population being a good model of human disease. This review compares the clinical characteristics, gross morphology and histopathology, aetiology, epidemiology, and genetics of canine and human OSA. Finally, the current position of canine OSA genetic research is discussed and areas for additional work within the canine population are identified.

A global genetic interaction network maps a wiring diagram of cellular function.

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Costanzo, Michael; VanderSluis, Benjamin; Koch, Elizabeth N; Baryshnikova, Anastasia; Pons, Carles; Tan, Guihong; Wang, Wen; Usaj, Matej; Hanchard, Julia; Lee, Susan D; Pelechano, Vicent; Styles, Erin B; Billmann, Maximilian; van Leeuwen, Jolanda; van Dyk, Nydia; Lin, Zhen-Yuan; Kuzmin, Elena; Nelson, Justin; Piotrowski, Jeff S; Srikumar, Tharan; Bahr, Sondra; Chen, Yiqun; Deshpande, Raamesh; Kurat, Christoph F; Li, Sheena C; Li, Zhijian; Usaj, Mojca Mattiazzi; Okada, Hiroki; Pascoe, Natasha; San Luis, Bryan-Joseph; Sharifpoor, Sara; Shuteriqi, Emira; Simpkins, Scott W; Snider, Jamie; Suresh, Harsha Garadi; Tan, Yizhao; Zhu, Hongwei; Malod-Dognin, Noel; Janjic, Vuk; Przulj, Natasa; Troyanskaya, Olga G; Stagljar, Igor; Xia, Tian; Ohya, Yoshikazu; Gingras, Anne-Claude; Raught, Brian; Boutros, Michael; Steinmetz, Lars M; Moore, Claire L; Rosebrock, Adam P; Caudy, Amy A; Myers, Chad L; Andrews, Brenda; Boone, Charles

2016-09-23

We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell. Copyright © 2016, American Association for the Advancement of Science.

Market analyses of livestock trade networks to inform the prevention of joint economic and epidemiological risks.

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Moslonka-Lefebvre, Mathieu; Gilligan, Christopher A; Monod, Hervé; Belloc, Catherine; Ezanno, Pauline; Filipe, João A N; Vergu, Elisabeta

2016-03-01

Conventional epidemiological studies of infections spreading through trade networks, e.g., via livestock movements, generally show that central large-size holdings (hubs) should be preferentially surveyed and controlled in order to reduce epidemic spread. However, epidemiological strategies alone may not be economically optimal when costs of control are factored in together with risks of market disruption from targeting core holdings in a supply chain. Using extensive data on animal movements in supply chains for cattle and swine in France, we introduce a method to identify effective strategies for preventing outbreaks with limited budgets while minimizing the risk of market disruptions. Our method involves the categorization of holdings based on position along the supply chain and degree of market share. Our analyses suggest that trade has a higher risk of propagating epidemics through cattle networks, which are dominated by exchanges involving wholesalers, than for swine. We assess the effectiveness of contrasting interventions from the perspectives of regulators and the market, using percolation analysis. We show that preferentially targeting minor, non-central agents can outperform targeting of hubs when the costs to stakeholders and the risks of market disturbance are considered. Our study highlights the importance of assessing joint economic-epidemiological risks in networks underlying pathogen propagation and trade. © 2016 The Authors.

Market analyses of livestock trade networks to inform the prevention of joint economic and epidemiological risks

Science.gov (United States)

Gilligan, Christopher A.; Belloc, Catherine; Filipe, João A. N.; Vergu, Elisabeta

2016-01-01

Conventional epidemiological studies of infections spreading through trade networks, e.g. via livestock movements, generally show that central large-size holdings (hubs) should be preferentially surveyed and controlled in order to reduce epidemic spread. However, epidemiological strategies alone may not be economically optimal when costs of control are factored in together with risks of market disruption from targeting core holdings in a supply chain. Using extensive data on animal movements in supply chains for cattle and swine in France, we introduce a method to identify effective strategies for preventing outbreaks with limited budgets while minimizing the risk of market disruptions. Our method involves the categorization of holdings based on position along the supply chain and degree of market share. Our analyses suggest that trade has a higher risk of propagating epidemics through cattle networks, which are dominated by exchanges involving wholesalers, than for swine. We assess the effectiveness of contrasting interventions from the perspectives of regulators and the market, using percolation analysis. We show that preferentially targeting minor, non-central agents can outperform targeting of hubs when the costs to stakeholders and the risks of market disturbance are considered. Our study highlights the importance of assessing joint economic–epidemiological risks in networks underlying pathogen propagation and trade. PMID:26984191

Genetic variation shapes protein networks mainly through non-transcriptional mechanisms.

Directory of Open Access Journals (Sweden)

Eric J Foss

2011-09-01

Full Text Available Networks of co-regulated transcripts in genetically diverse populations have been studied extensively, but little is known about the degree to which these networks cause similar co-variation at the protein level. We quantified 354 proteins in a genetically diverse population of yeast segregants, which allowed for the first time construction of a coherent protein co-variation matrix. We identified tightly co-regulated groups of 36 and 93 proteins that were made up predominantly of genes involved in ribosome biogenesis and amino acid metabolism, respectively. Even though the ribosomal genes were tightly co-regulated at both the protein and transcript levels, genetic regulation of proteins was entirely distinct from that of transcripts, and almost no genes in this network showed a significant correlation between protein and transcript levels. This result calls into question the widely held belief that in yeast, as opposed to higher eukaryotes, ribosomal protein levels are regulated primarily by regulating transcript levels. Furthermore, although genetic regulation of the amino acid network was more similar for proteins and transcripts, regression analysis demonstrated that even here, proteins vary predominantly as a result of non-transcriptional variation. We also found that cis regulation, which is common in the transcriptome, is rare at the level of the proteome. We conclude that most inter-individual variation in levels of these particular high abundance proteins in this genetically diverse population is not caused by variation of their underlying transcripts.

Maximizing lifetime of wireless sensor networks using genetic approach

DEFF Research Database (Denmark)

Wagh, Sanjeev; Prasad, Ramjee

2014-01-01

The wireless sensor networks are designed to install the smart network applications or network for emergency solutions, where human interaction is not possible. The nodes in wireless sensor networks have to self organize as per the users requirements through monitoring environments. As the sensor......-objective parameters are considered to solve the problem using genetic algorithm of evolutionary approach.......The wireless sensor networks are designed to install the smart network applications or network for emergency solutions, where human interaction is not possible. The nodes in wireless sensor networks have to self organize as per the users requirements through monitoring environments. As the sensor...

Using genetic markers to orient the edges in quantitative trait networks: the NEO software.

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Aten, Jason E; Fuller, Tova F; Lusis, Aldons J; Horvath, Steve

2008-04-15

Systems genetic studies have been used to identify genetic loci that affect transcript abundances and clinical traits such as body weight. The pairwise correlations between gene expression traits and/or clinical traits can be used to define undirected trait networks. Several authors have argued that genetic markers (e.g expression quantitative trait loci, eQTLs) can serve as causal anchors for orienting the edges of a trait network. The availability of hundreds of thousands of genetic markers poses new challenges: how to relate (anchor) traits to multiple genetic markers, how to score the genetic evidence in favor of an edge orientation, and how to weigh the information from multiple markers. We develop and implement Network Edge Orienting (NEO) methods and software that address the challenges of inferring unconfounded and directed gene networks from microarray-derived gene expression data by integrating mRNA levels with genetic marker data and Structural Equation Model (SEM) comparisons. The NEO software implements several manual and automatic methods for incorporating genetic information to anchor traits. The networks are oriented by considering each edge separately, thus reducing error propagation. To summarize the genetic evidence in favor of a given edge orientation, we propose Local SEM-based Edge Orienting (LEO) scores that compare the fit of several competing causal graphs. SEM fitting indices allow the user to assess local and overall model fit. The NEO software allows the user to carry out a robustness analysis with regard to genetic marker selection. We demonstrate the utility of NEO by recovering known causal relationships in the sterol homeostasis pathway using liver gene expression data from an F2 mouse cross. Further, we use NEO to study the relationship between a disease gene and a biologically important gene co-expression module in liver tissue. The NEO software can be used to orient the edges of gene co-expression networks or quantitative trait

Strategies to work with HLA data in human populations for histocompatibility, clinical transplantation, epidemiology and population genetics: HLA-NET methodological recommendations.

Science.gov (United States)

Sanchez-Mazas, A; Vidan-Jeras, B; Nunes, J M; Fischer, G; Little, A-M; Bekmane, U; Buhler, S; Buus, S; Claas, F H J; Dormoy, A; Dubois, V; Eglite, E; Eliaou, J F; Gonzalez-Galarza, F; Grubic, Z; Ivanova, M; Lie, B; Ligeiro, D; Lokki, M L; da Silva, B Martins; Martorell, J; Mendonça, D; Middleton, D; Voniatis, D Papioannou; Papasteriades, C; Poli, F; Riccio, M E; Vlachou, M Spyropoulou; Sulcebe, G; Tonks, S; Nevessignsky, M Toungouz; Vangenot, C; van Walraven, A-M; Tiercy, J-M

2012-12-01

HLA-NET (a European COST Action) aims at networking researchers working in bone marrow transplantation, epidemiology and population genetics to improve the molecular characterization of the HLA genetic diversity of human populations, with an expected strong impact on both public health and fundamental research. Such improvements involve finding consensual strategies to characterize human populations and samples and report HLA molecular typings and ambiguities; proposing user-friendly access to databases and computer tools and defining minimal requirements related to ethical aspects. The overall outcome is the provision of population genetic characterizations and comparisons in a standard way by all interested laboratories. This article reports the recommendations of four working groups (WG1-4) of the HLA-NET network at the mid-term of its activities. WG1 (Population definitions and sampling strategies for population genetics' analyses) recommends avoiding outdated racial classifications and population names (e.g. 'Caucasian') and using instead geographic and/or cultural (e.g. linguistic) criteria to describe human populations (e.g. 'pan-European'). A standard 'HLA-NET POPULATION DATA QUESTIONNAIRE' has been finalized and is available for the whole HLA community. WG2 (HLA typing standards for population genetics analyses) recommends retaining maximal information when reporting HLA typing results. Rather than using the National Marrow Donor Program coding system, all ambiguities should be provided by listing all allele pairs required to explain each genotype, according to the formats proposed in 'HLA-NET GUIDELINES FOR REPORTING HLA TYPINGS'. The group also suggests taking into account a preliminary list of alleles defined by polymorphisms outside the peptide-binding sites that may affect population genetic statistics because of significant frequencies. WG3 (Bioinformatic strategies for HLA population data storage and analysis) recommends the use of programs capable

A Genetic Epidemiological Mega Analysis of Smoking Initiation in Adolescents.

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Maes, Hermine H; Prom-Wormley, Elizabeth; Eaves, Lindon J; Rhee, Soo Hyun; Hewitt, John K; Young, Susan; Corley, Robin; McGue, Matt; Iacono, William G; Legrand, Lisa; Samek, Diana R; Murrelle, E Lenn; Silberg, Judy L; Miles, Donna R; Schieken, Richard M; Beunen, Gaston P; Thomis, Martine; Rose, Richard J; Dick, Danielle M; Boomsma, Dorret I; Bartels, Meike; Vink, Jacqueline M; Lichtenstein, Paul; White, Victoria; Kaprio, Jaakko; Neale, Michael C

2017-04-01

Previous studies in adolescents were not adequately powered to accurately disentangle genetic and environmental influences on smoking initiation (SI) across adolescence. Mega-analysis of pooled genetically informative data on SI was performed, with structural equation modeling, to test equality of prevalence and correlations across cultural backgrounds, and to estimate the significance and effect size of genetic and environmental effects according to the classical twin study, in adolescent male and female twins from same-sex and opposite-sex twin pairs (N = 19 313 pairs) between ages 10 and 19, with 76 358 longitudinal assessments between 1983 and 2007, from 11 population-based twin samples from the United States, Europe, and Australia. Although prevalences differed between samples, twin correlations did not, suggesting similar etiology of SI across developed countries. The estimate of additive genetic contributions to liability of SI increased from approximately 15% to 45% from ages 13 to 19. Correspondingly, shared environmental factors accounted for a substantial proportion of variance in liability to SI at age 13 (70%) and gradually less by age 19 (40%). Both additive genetic and shared environmental factors significantly contribute to variance in SI throughout adolescence. The present study, the largest genetic epidemiological study on SI to date, found consistent results across 11 studies for the etiology of SI. Environmental factors, especially those shared by siblings in a family, primarily influence SI variance in early adolescence, while an increasing role of genetic factors is seen at later ages, which has important implications for prevention strategies. This is the first study to find evidence of genetic factors in liability to SI at ages as young as 12. It also shows the strongest evidence to date for decay of effects of the shared environment from early adolescence to young adulthood. We found remarkable consistency of twin correlations across

Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis).

Science.gov (United States)

VanderWaal, Kimberly L; Atwill, Edward R; Isbell, Lynne A; McCowan, Brenda

2014-03-01

Although network analysis has drawn considerable attention as a promising tool for disease ecology, empirical research has been hindered by limitations in detecting the occurrence of pathogen transmission (who transmitted to whom) within social networks. Using a novel approach, we utilize the genetics of a diverse microbe, Escherichia coli, to infer where direct or indirect transmission has occurred and use these data to construct transmission networks for a wild giraffe population (Giraffe camelopardalis). Individuals were considered to be a part of the same transmission chain and were interlinked in the transmission network if they shared genetic subtypes of E. coli. By using microbial genetics to quantify who transmits to whom independently from the behavioural data on who is in contact with whom, we were able to directly investigate how the structure of contact networks influences the structure of the transmission network. To distinguish between the effects of social and environmental contact on transmission dynamics, the transmission network was compared with two separate contact networks defined from the behavioural data: a social network based on association patterns, and a spatial network based on patterns of home-range overlap among individuals. We found that links in the transmission network were more likely to occur between individuals that were strongly linked in the social network. Furthermore, individuals that had more numerous connections or that occupied 'bottleneck' positions in the social network tended to occupy similar positions in the transmission network. No similar correlations were observed between the spatial and transmission networks. This indicates that an individual's social network position is predictive of transmission network position, which has implications for identifying individuals that function as super-spreaders or transmission bottlenecks in the population. These results emphasize the importance of association patterns in

Classification of rare missense substitutions, using risk surfaces, with genetic- and molecular-epidemiology applications.

Science.gov (United States)

Tavtigian, Sean V; Byrnes, Graham B; Goldgar, David E; Thomas, Alun

2008-11-01

Many individually rare missense substitutions are encountered during deep resequencing of candidate susceptibility genes and clinical mutation screening of known susceptibility genes. BRCA1 and BRCA2 are among the most resequenced of all genes, and clinical mutation screening of these genes provides an extensive data set for analysis of rare missense substitutions. Align-GVGD is a mathematically simple missense substitution analysis algorithm, based on the Grantham difference, which has already contributed to classification of missense substitutions in BRCA1, BRCA2, and CHEK2. However, the distribution of genetic risk as a function of Align-GVGD's output variables Grantham variation (GV) and Grantham deviation (GD) has not been well characterized. Here, we used data from the Myriad Genetic Laboratories database of nearly 70,000 full-sequence tests plus two risk estimates, one approximating the odds ratio and the other reflecting strength of selection, to display the distribution of risk in the GV-GD plane as a series of surfaces. We abstracted contours from the surfaces and used the contours to define a sequence of missense substitution grades ordered from greatest risk to least risk. The grades were validated internally using a third, personal and family history-based, measure of risk. The Align-GVGD grades defined here are applicable to both the genetic epidemiology problem of classifying rare missense substitutions observed in known susceptibility genes and the molecular epidemiology problem of analyzing rare missense substitutions observed during case-control mutation screening studies of candidate susceptibility genes. (c) 2008 Wiley-Liss, Inc.

Application of genetic neural network in steam generator fault diagnosing

International Nuclear Information System (INIS)

Lin Xiaogong; Jiang Xingwei; Liu Tao; Shi Xiaocheng

2005-01-01

In the paper, a new algorithm which neural network and genetic algorithm are mixed is adopted, aiming at the problems of slow convergence rate and easily falling into part minimums in network studying of traditional BP neural network, and used in the fault diagnosis of steam generator. The result shows that this algorithm can solve the convergence problem in the network trains effectively. (author)

[Exploration and practice of genetics teaching assisted by network technology platform].

Science.gov (United States)

Li, Ya-Xuan; Zhang, Fei-Xiong; Zhao, Xin; Cai, Min-Hua; Yan, Yue-Ming; Hu, Ying-Kao

2010-04-01

More teaching techniques have been brought out gradually along with the development of new technologies. On the basis of those traditional teaching methods, a new platform has been set up by the network technology for teaching process. In genetics teaching, it is possible to use the network platform to guide student studying, promote student's learning interest and study independently by themselves. It has been proved, after exploring and applying for many years, that network teaching is one of the most useful methods and has inimitable advantage comparing to the traditional ones in genetics teaching. The establishment of network teaching platform, the advantage and deficiency and relevant strategies were intro-duced in this paper.

Chapter 2. Fasciola, lymnaeids and human fascioliasis, with a global overview on disease transmission, epidemiology, evolutionary genetics, molecular epidemiology and control.

Science.gov (United States)

Mas-Coma, Santiago; Valero, María Adela; Bargues, María Dolores

2009-01-01

Fascioliasis, caused by liver fluke species of the genus Fasciola, has always been well recognized because of its high veterinary impact but it has been among the most neglected diseases for decades with regard to human infection. However, the increasing importance of human fascioliasis worldwide has re-launched interest in fascioliasis. From the 1990s, many new concepts have been developed regarding human fascioliasis and these have furnished a new baseline for the human disease that is very different to a simple extrapolation from fascioliasis in livestock. Studies have shown that human fascioliasis presents marked heterogeneity, including different epidemiological situations and transmission patterns in different endemic areas. This heterogeneity, added to the present emergence/re-emergence of the disease both in humans and animals in many regions, confirms a worrying global scenario. The huge negative impact of fascioliasis on human communities demands rapid action. When analyzing how better to define control measures for endemic areas differing at such a level, it would be useful to have genetic markers that could distinguish each type of transmission pattern and epidemiological situation. Accordingly, this chapter covers aspects of aetiology, geographical distribution, epidemiology, transmission and control in order to obtain a solid baseline for the interpretation of future results. The origins and geographical spread of F. hepatica and F. gigantica in both the ruminant pre-domestication times and the livestock post-domestication period are analyzed. Paleontological, archaeological and historical records, as well as genetic data on recent dispersal of livestock species, are taken into account to establish an evolutionary framework for the two fasciolids across all continents. Emphasis is given to the distributional overlap of both species and the roles of transportation, transhumance and trade in the different overlap situations. Areas with only one Fasciola

Network statistics of genetically-driven gene co-expression modules in mouse crosses

Directory of Open Access Journals (Sweden)

Marie-Pier eScott-Boyer

2013-12-01

Full Text Available In biology, networks are used in different contexts as ways to represent relationships between entities, such as for instance interactions between genes, proteins or metabolites. Despite progress in the analysis of such networks and their potential to better understand the collective impact of genes on complex traits, one remaining challenge is to establish the biologic validity of gene co-expression networks and to determine what governs their organization. We used WGCNA to construct and analyze seven gene expression datasets from several tissues of mouse recombinant inbred strains (RIS. For six out of the 7 networks, we found that linkage to module QTLs (mQTLs could be established for 29.3% of gene co-expression modules detected in the several mouse RIS. For about 74.6% of such genetically-linked modules, the mQTL was on the same chromosome as the one contributing most genes to the module, with genes originating from that chromosome showing higher connectivity than other genes in the modules. Such modules (that we considered as genetically-driven had network statistic properties (density, centralization and heterogeneity that set them apart from other modules in the network. Altogether, a sizeable portion of gene co-expression modules detected in mouse RIS panels had genetic determinants as their main organizing principle. In addition to providing a biologic interpretation validation for these modules, these genetic determinants imparted on them particular properties that set them apart from other modules in the network, to the point that they can be predicted to a large extent on the basis of their network statistics.

Protein Kinase C Epsilon and Genetic Networks in Osteosarcoma Metastasis

Energy Technology Data Exchange (ETDEWEB)

Goudarzi, Atta, E-mail: atta.goudarzi@utoronto.ca [Department of Molecular Genetics, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 1A8 (Canada); Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Ave., Toronto, ON M5G 1X5 (Canada); Gokgoz, Nalan; Gill, Mona; Pinnaduwage, Dushanthi [Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Ave., Toronto, ON M5G 1X5 (Canada); Merico, Daniele [The Centre for Applied Genomics, The Hospital for Sick Children, MaRS Centre-East Tower, 101 College Street Rm.14-701, Toronto, ON M5G 1L7 (Canada); Wunder, Jay S. [Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Ave., Toronto, ON M5G 1X5 (Canada); Andrulis, Irene L. [Department of Molecular Genetics, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 1A8 (Canada); Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Ave., Toronto, ON M5G 1X5 (Canada)

2013-04-08

Osteosarcoma (OS) is the most common primary malignant tumor of the bone, and pulmonary metastasis is the most frequent cause of OS mortality. The aim of this study was to discover and characterize genetic networks differentially expressed in metastatic OS. Expression profiling of OS tumors, and subsequent supervised network analysis, was performed to discover genetic networks differentially activated or organized in metastatic OS compared to localized OS. Broad trends among the profiles of metastatic tumors include aberrant activity of intracellular organization and translation networks, as well as disorganization of metabolic networks. The differentially activated PRKCε-RASGRP3-GNB2 network, which interacts with the disorganized DLG2 hub, was also found to be differentially expressed among OS cell lines with differing metastatic capacity in xenograft models. PRKCε transcript was more abundant in some metastatic OS tumors; however the difference was not significant overall. In functional studies, PRKCε was not found to be involved in migration of M132 OS cells, but its protein expression was induced in M112 OS cells following IGF-1 stimulation.

Protein Kinase C Epsilon and Genetic Networks in Osteosarcoma Metastasis

International Nuclear Information System (INIS)

Goudarzi, Atta; Gokgoz, Nalan; Gill, Mona; Pinnaduwage, Dushanthi; Merico, Daniele; Wunder, Jay S.; Andrulis, Irene L.

2013-01-01

Osteosarcoma (OS) is the most common primary malignant tumor of the bone, and pulmonary metastasis is the most frequent cause of OS mortality. The aim of this study was to discover and characterize genetic networks differentially expressed in metastatic OS. Expression profiling of OS tumors, and subsequent supervised network analysis, was performed to discover genetic networks differentially activated or organized in metastatic OS compared to localized OS. Broad trends among the profiles of metastatic tumors include aberrant activity of intracellular organization and translation networks, as well as disorganization of metabolic networks. The differentially activated PRKCε-RASGRP3-GNB2 network, which interacts with the disorganized DLG2 hub, was also found to be differentially expressed among OS cell lines with differing metastatic capacity in xenograft models. PRKCε transcript was more abundant in some metastatic OS tumors; however the difference was not significant overall. In functional studies, PRKCε was not found to be involved in migration of M132 OS cells, but its protein expression was induced in M112 OS cells following IGF-1 stimulation

Familial clustering and genetic risk for dementia in a genetically isolated Dutch population.

NARCIS (Netherlands)

K. Sleegers (Kristel); F. Forey; J. Theuns (Jessie); Y.S. Aulchenko (Yurii); S. Rademakers (Suzanne); M. Cruts (Marc); W.A. van Gool (Willem); P. Heutink (Peter); B.A. Oostra (Ben); J.C. van Swieten (John); C.M. van Duijn (Cornelia); C. van Broeckhoven (Christine)

2004-01-01

textabstractDespite advances in elucidating the genetic epidemiology of Alzheimer's disease and frontotemporal dementia, the aetiology for most patients with dementia remains unclear. We examined the genetic epidemiology of dementia in a recent genetically isolated Dutch population founded around

Inference and Analysis of Population Structure Using Genetic Data and Network Theory.

Science.gov (United States)

Greenbaum, Gili; Templeton, Alan R; Bar-David, Shirli

2016-04-01

Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is most often done by applying model-based approaches, aided by visualization using distance-based approaches such as multidimensional scaling. While existing distance-based approaches suffer from a lack of statistical rigor, model-based approaches entail assumptions of prior conditions such as that the subpopulations are at Hardy-Weinberg equilibria. Here we present a distance-based approach for inference about population structure using genetic data by defining population structure using network theory terminology and methods. A network is constructed from a pairwise genetic-similarity matrix of all sampled individuals. The community partition, a partition of a network to dense subgraphs, is equated with population structure, a partition of the population to genetically related groups. Community-detection algorithms are used to partition the network into communities, interpreted as a partition of the population to subpopulations. The statistical significance of the structure can be estimated by using permutation tests to evaluate the significance of the partition's modularity, a network theory measure indicating the quality of community partitions. To further characterize population structure, a new measure of the strength of association (SA) for an individual to its assigned community is presented. The strength of association distribution (SAD) of the communities is analyzed to provide additional population structure characteristics, such as the relative amount of gene flow experienced by the different subpopulations and identification of hybrid individuals. Human genetic data and simulations are used to demonstrate the applicability of the analyses. The approach presented here provides a novel, computationally efficient model-free method for inference about population structure that does not entail assumption of

Improved Cost-Base Design of Water Distribution Networks using Genetic Algorithm

Science.gov (United States)

Moradzadeh Azar, Foad; Abghari, Hirad; Taghi Alami, Mohammad; Weijs, Steven

2010-05-01

Population growth and progressive extension of urbanization in different places of Iran cause an increasing demand for primary needs. The water, this vital liquid is the most important natural need for human life. Providing this natural need is requires the design and construction of water distribution networks, that incur enormous costs on the country's budget. Any reduction in these costs enable more people from society to access extreme profit least cost. Therefore, investment of Municipal councils need to maximize benefits or minimize expenditures. To achieve this purpose, the engineering design depends on the cost optimization techniques. This paper, presents optimization models based on genetic algorithm(GA) to find out the minimum design cost Mahabad City's (North West, Iran) water distribution network. By designing two models and comparing the resulting costs, the abilities of GA were determined. the GA based model could find optimum pipe diameters to reduce the design costs of network. Results show that the water distribution network design using Genetic Algorithm could lead to reduction of at least 7% in project costs in comparison to the classic model. Keywords: Genetic Algorithm, Optimum Design of Water Distribution Network, Mahabad City, Iran.

Familial clustering and genetic risk for dementia in a genetically isolated Dutch population

NARCIS (Netherlands)

Sleegers, K.; Roks, G.; Theuns, J.; Aulchenko, Y. S.; Rademakers, R.; Cruts, M.; van Gool, W. A.; van Broeckhoven, C.; Heutink, P.; Oostra, B. A.; van Swieten, J. C.; van Duijn, C. M.

2004-01-01

Despite advances in elucidating the genetic epidemiology of Alzheimer's disease and frontotemporal dementia, the aetiology for most patients with dementia remains unclear. We examined the genetic epidemiology of dementia in a recent genetically isolated Dutch population founded around 1750. The

GKIN: a tool for drawing genetic networks

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Jonathan Arnold

2012-03-01

Full Text Available We present GKIN, a simulator and a comprehensive graphical interface where one can draw the model specification of reactions between hypothesized molecular participants in a gene regulatory and biochemical reaction network (or genetic network for short. The solver is written in C++ in a nearly platform independentmanner to simulate large ensembles of models, which can run on PCs, Macintoshes, and UNIX machines, and its graphical user interface is written in Java which can run as a standalone or WebStart application. The drawing capability for rendering a network significantly enhances the ease of use of other reaction network simulators, such as KINSOLVER (Aleman-Meza et al., 2009 and enforces a correct semantic specification of the network. In a usability study with novice users, drawing the network with GKIN was preferred and faster in comparison with entry with a dialog-box guided interface in COPASI (Hoops, et al., 2006 with no difference in error rates between GKIN and COPASI in specifying the network. GKIN is freely available at http://faculty.cs.wit.edu/~ldeligia/PROJECTS/GKIN/.

Epidemiology of Brucellosis and Genetic Diversity of Brucella abortus in Kazakhstan.

Science.gov (United States)

Shevtsova, Elena; Shevtsov, Alexandr; Mukanov, Kasim; Filipenko, Maxim; Kamalova, Dinara; Sytnik, Igor; Syzdykov, Marat; Kuznetsov, Andrey; Akhmetova, Assel; Zharova, Mira; Karibaev, Talgat; Tarlykov, Pavel; Ramanculov, Erlan

2016-01-01

Brucellosis is a major zoonotic infection in Kazakhstan. However, there is limited data on its incidence in humans and animals, and the genetic diversity of prevalent strains is virtually unstudied. Additionally, there is no detailed overview of Kazakhstan brucellosis control and eradication programs. Here, we analyzed brucellosis epidemiological data, and assessed the effectiveness of eradication strategies employed over the past 70 years to counteract this infection. We also conducted multiple loci variable-number tandem repeat analysis (MLVA) of Brucella abortus strains found in Kazakhstan. We analyzed official data on the incidence of animal brucellosis in Kazakhstan. The records span more than 70 years of anti-brucellosis campaigns, and contain a brief description of the applied control strategies, their effectiveness, and their impact on the incidence in humans. The MLVA-16 method was used to type 94 strains of B. abortus and serial passages of B. abortus 82, a strain used in vaccines. MLVA-8 and MLVA-11 analyses clustered strains into a total of four and seven genotypes, respectively; it is the first time that four of these genotypes have been described. MLVA-16 analysis divided strains into 28 distinct genotypes having genetic similarity coefficient that varies from 60 to100% and a Hunter & Gaston diversity index of 0.871. MST analysis reconstruction revealed clustering into "Kazakhstani-Chinese (Central Asian)", "European" and "American" lines. Detection of multiple genotypes in a single outbreak confirms that poorly controlled trade of livestock plays a crucial role in the spread of infection. Notably, the MLVA-16 profile of the B. abortus 82 strain was unique and did not change during 33 serial passages. MLVA genotyping may thus be useful for epidemiological monitoring of brucellosis, and for tracking the source(s) of infection. We suggest that countrywide application of MLVA genotyping would improve the control of brucellosis in Kazakhstan.

Implementation and Characterization of Dynamic Genetic Networks in Vitro

OpenAIRE

Niederholtmeyer, Henrike Marie

2015-01-01

Transcription and translation (TX-TL) can be performed in vitro, outside of cells, allowing the assembly and analysis of genetic networks. This approach to engineering biological networks in a less complex and more controllable environment could one day allow rapid prototyping of network designs before implementing them in living cells. Furthermore, the in vitro approach provides insight into how natural biological systems are built and is instructive to define the rules for engineering biolo...

Maximization Network Throughput Based on Improved Genetic Algorithm and Network Coding for Optical Multicast Networks

Science.gov (United States)

Wei, Chengying; Xiong, Cuilian; Liu, Huanlin

2017-12-01

Maximal multicast stream algorithm based on network coding (NC) can improve the network's throughput for wavelength-division multiplexing (WDM) networks, which however is far less than the network's maximal throughput in terms of theory. And the existing multicast stream algorithms do not give the information distribution pattern and routing in the meantime. In the paper, an improved genetic algorithm is brought forward to maximize the optical multicast throughput by NC and to determine the multicast stream distribution by hybrid chromosomes construction for multicast with single source and multiple destinations. The proposed hybrid chromosomes are constructed by the binary chromosomes and integer chromosomes, while the binary chromosomes represent optical multicast routing and the integer chromosomes indicate the multicast stream distribution. A fitness function is designed to guarantee that each destination can receive the maximum number of decoding multicast streams. The simulation results showed that the proposed method is far superior over the typical maximal multicast stream algorithms based on NC in terms of network throughput in WDM networks.

eCOMPAGT – efficient Combination and Management of Phenotypes and Genotypes for Genetic Epidemiology

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Specht Günther

2009-05-01

Full Text Available Abstract Background High-throughput genotyping and phenotyping projects of large epidemiological study populations require sophisticated laboratory information management systems. Most epidemiological studies include subject-related personal information, which needs to be handled with care by following data privacy protection guidelines. In addition, genotyping core facilities handling cooperative projects require a straightforward solution to monitor the status and financial resources of the different projects. Description We developed a database system for an efficient combination and management of phenotypes and genotypes (eCOMPAGT deriving from genetic epidemiological studies. eCOMPAGT securely stores and manages genotype and phenotype data and enables different user modes with different rights. Special attention was drawn on the import of data deriving from TaqMan and SNPlex genotyping assays. However, the database solution is adjustable to other genotyping systems by programming additional interfaces. Further important features are the scalability of the database and an export interface to statistical software. Conclusion eCOMPAGT can store, administer and connect phenotype data with all kinds of genotype data and is available as a downloadable version at http://dbis-informatik.uibk.ac.at/ecompagt.

The systematics and population genetics of Opisthorchis viverrini sensu lato: implications in parasite epidemiology and bile duct cancer.

Science.gov (United States)

Sithithaworn, Paiboon; Andrews, Ross H; Petney, Trevor N; Saijuntha, Weerachai; Laoprom, Nonglak

2012-03-01

Together with host and environmental factors, the systematics and population genetic variation of Opisthorchis viverrini may contribute to recorded local and regional differences in epidemiology and host morbidity in opisthorchiasis and cholangiocarcinoma (CCA). In this review, we address recent findings that O. viverrini comprises a species complex with varying degrees of population genetic variation which are associated with specific river wetland systems within Thailand as well as the Lao PDR. Having an accurate understanding of systematics is a prerequisite for a meaningful assessment of the population structure of each species within the O. viverrini complex in nature, as well as a better understanding of the magnitude of genetic variation that occurs within different species of hosts in its life cycle. Whether specific genotypes are related to habitat type(s) and/or specific intermediate host species are discussed based on current available data. Most importantly, we focus on whether there is a correlation between incidence of CCA and genotype(s) of O. viverrini. This will provide a solid basis for further comprehensive investigations of the role of genetic variation within each species of O. viverrini sensu lato in human epidemiology and genotype related morbidity as well as co-evolution of parasites with primary and secondary intermediate species of host. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology

Science.gov (United States)

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental

A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

Science.gov (United States)

Kobayashi, Takahisa; Simon, Donald L.

2001-01-01

In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

Design of Supply Chain Networks with Supply Disruptions using Genetic Algorithm

OpenAIRE

Taha, Raghda; Abdallah, Khaled; Sadek, Yomma; El-Kharbotly, Amin; Afia, Nahid

2014-01-01

The design of supply chain networks subject to disruptions is tackled. A genetic algorithm with the objective of minimizing the design cost and regret cost is developed to achieve a reliable supply chain network. The improvement of supply chain network reliability is measured against the supply chain cost.

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.

Science.gov (United States)

Chang, P; Grinband, J; Weinberg, B D; Bardis, M; Khy, M; Cadena, G; Su, M-Y; Cha, S; Filippi, C G; Bota, D; Baldi, P; Poisson, L M; Jain, R; Chow, D

2018-05-10

The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation. MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 ( IDH1 ) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase ( MGMT ) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification. Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features. Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training. © 2018 by American Journal of Neuroradiology.

Proceedings of the African Field Epidemiology Network (AFENET) Scientific Conference 17-22 November 2013 Addis Ababa, Ethiopia: plenaries and oral presentations

OpenAIRE

Gitta, Sheba Nakacubo; Mwesiga, Allan; Kamadjeu, Raoul; Stanley, Claire; Borchert, Jeff; Mensah, George; Bejtullahu, Armand; Kamadjeu, Raoul; Kebede, Amha; Berhane, Yemane; Jocker, Mathew Lado; Ebontane, Ndode Corlins; Feyissa, Daba; Yeshitila, Kidanie; Mehari, Goitom

2015-01-01

Biennially, trainees and graduates of Field Epidemiology and Laboratory Training Programs (FELTPs) are presented with a platform to share investigations and projects undertaken during their two-year training in Applied Epidemiology. The African Field Epidemiology Network (AFENET) Scientific Conference, is a perfect opportunity for public health professionals from various sectors and organizations to come together to discuss issues that impact on public health in Africa. This year's conference...

Genetic diversity and epidemiology of infectious hematopoietic necrosis virus in Alaska

Science.gov (United States)

Emmenegger, E.G; Meyers, T.R.; Burton, T.O.; Kurath, G.

2000-01-01

Forty-two infectious hematopoietic necrosis virus (IHNV) isolates from Alaska were analyzed using the ribonuclease protection assay (RPA) and nucleotide sequencing. RPA analyses, utilizing 4 probes, N5, N3 (N gene), GF (G gene), and NV (NV gene), determined that the haplotypes of all 3 genes demonstrated a consistent spatial pattern. Virus isolates belonging to the most common haplotype groups were distributed throughout Alaska, whereas isolates in small haplotype groups were obtained from only 1 site (hatchery, lake, etc.). The temporal pattern of the GF haplotypes suggested a 'genetic acclimation' of the G gene, possibly due to positive selection on the glycoprotein. A pairwise comparison of the sequence data determined that the maximum nucleotide diversity of the isolates was 2.75% (10 mismatches) for the NV gene, and 1.99% (6 mismatches) for a 301 base pair region of the G gene, indicating that the genetic diversity of IHNV within Alaska is notably lower than in the more southern portions of the IHNV North American range. Phylogenetic analysis of representative Alaskan sequences and sequences of 12 previously characterized IHNV strains from Washington, Oregon, Idaho, California (USA) and British Columbia (Canada) distinguished the isolates into clusters that correlated with geographic origin and indicated that the Alaskan and British Columbia isolates may have a common viral ancestral lineage. Comparisons of multiple isolates from the same site provided epidemiological insights into viral transmission patterns and indicated that viral evolution, viral introduction, and genetic stasis were the mechanisms involved with IHN virus population dynamics in Alaska. The examples of genetic stasis and the overall low sequence heterogeneity of the Alaskan isolates suggested that they are evolutionarily constrained. This study establishes a baseline of genetic fingerprint patterns and sequence groups representing the genetic diversity of Alaskan IHNV isolates. This

Genetic interaction network of the Saccharomyces cerevisiae type 1 phosphatase Glc7

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Neszt Michael

2008-07-01

Full Text Available Abstract Background Protein kinases and phosphatases regulate protein phosphorylation, a critical means of modulating protein function, stability and localization. The identification of functional networks for protein phosphatases has been slow due to their redundant nature and the lack of large-scale analyses. We hypothesized that a genome-scale analysis of genetic interactions using the Synthetic Genetic Array could reveal protein phosphatase functional networks. We apply this approach to the conserved type 1 protein phosphatase Glc7, which regulates numerous cellular processes in budding yeast. Results We created a novel glc7 catalytic mutant (glc7-E101Q. Phenotypic analysis indicates that this novel allele exhibits slow growth and defects in glucose metabolism but normal cell cycle progression and chromosome segregation. This suggests that glc7-E101Q is a hypomorphic glc7 mutant. Synthetic Genetic Array analysis of glc7-E101Q revealed a broad network of 245 synthetic sick/lethal interactions reflecting that many processes are required when Glc7 function is compromised such as histone modification, chromosome segregation and cytokinesis, nutrient sensing and DNA damage. In addition, mitochondrial activity and inheritance and lipid metabolism were identified as new processes involved in buffering Glc7 function. An interaction network among 95 genes genetically interacting with GLC7 was constructed by integration of genetic and physical interaction data. The obtained network has a modular architecture, and the interconnection among the modules reflects the cooperation of the processes buffering Glc7 function. Conclusion We found 245 genes required for the normal growth of the glc7-E101Q mutant. Functional grouping of these genes and analysis of their physical and genetic interaction patterns bring new information on Glc7-regulated processes.

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.

Unravelling the Molecular Epidemiology and Genetic Diversity among Burkholderia pseudomallei Isolates from South India Using Multi-Locus Sequence Typing.

Science.gov (United States)

Tellapragada, Chaitanya; Kamthan, Aayushi; Shaw, Tushar; Ke, Vandana; Kumar, Subodh; Bhat, Vinod; Mukhopadhyay, Chiranjay

2016-01-01

There is a slow but steady rise in the case detection rates of melioidosis from various parts of the Indian sub-continent in the past two decades. However, the epidemiology of the disease in India and the surrounding South Asian countries remains far from well elucidated. Multi-locus sequence typing (MLST) is a useful epidemiological tool to study the genetic relatedness of bacterial isolates both with-in and across the countries. With this background, we studied the molecular epidemiology of 32 Burkholderia pseudomallei isolates (31 clinical and 1 soil isolate) obtained during 2006-2015 from various parts of south India using multi-locus sequencing typing and analysis. Of the 32 isolates included in the analysis, 30 (93.7%) had novel allelic profiles that were not reported previously. Sequence type (ST) 1368 (n = 15, 46.8%) with allelic profile (1, 4, 6, 4, 1, 1, 3) was the most common genotype observed. We did not observe a genotypic association of STs with geographical location, type of infection and year of isolation in the present study. Measure of genetic differentiation (FST) between Indian and the rest of world isolates was 0.14413. Occurrence of the same ST across three adjacent states of south India suggest the dispersion of B.pseudomallei across the south western coastal part of India with limited geographical clustering. However, majority of the STs reported from the present study remained as "outliers" on the eBURST "Population snapshot", suggesting the genetic diversity of Indian isolates from the Australasian and Southeast Asian isolates.

Unravelling the Molecular Epidemiology and Genetic Diversity among Burkholderia pseudomallei Isolates from South India Using Multi-Locus Sequence Typing.

Directory of Open Access Journals (Sweden)

Chaitanya Tellapragada

Full Text Available There is a slow but steady rise in the case detection rates of melioidosis from various parts of the Indian sub-continent in the past two decades. However, the epidemiology of the disease in India and the surrounding South Asian countries remains far from well elucidated. Multi-locus sequence typing (MLST is a useful epidemiological tool to study the genetic relatedness of bacterial isolates both with-in and across the countries. With this background, we studied the molecular epidemiology of 32 Burkholderia pseudomallei isolates (31 clinical and 1 soil isolate obtained during 2006-2015 from various parts of south India using multi-locus sequencing typing and analysis. Of the 32 isolates included in the analysis, 30 (93.7% had novel allelic profiles that were not reported previously. Sequence type (ST 1368 (n = 15, 46.8% with allelic profile (1, 4, 6, 4, 1, 1, 3 was the most common genotype observed. We did not observe a genotypic association of STs with geographical location, type of infection and year of isolation in the present study. Measure of genetic differentiation (FST between Indian and the rest of world isolates was 0.14413. Occurrence of the same ST across three adjacent states of south India suggest the dispersion of B.pseudomallei across the south western coastal part of India with limited geographical clustering. However, majority of the STs reported from the present study remained as "outliers" on the eBURST "Population snapshot", suggesting the genetic diversity of Indian isolates from the Australasian and Southeast Asian isolates.

Epidemiology and genetics of intracranial aneurysms

International Nuclear Information System (INIS)

Caranci, F.; Briganti, F.; Cirillo, L.; Leonardi, M.; Muto, M.

2013-01-01

Intracranial aneurysms are acquired lesions (5–10% of the population), a fraction of which rupture leading to subarachnoid hemorrhage with devastating consequences. Until now, the exact etiology of intracranial aneurysms formation remains unclear. The low incidence of subarachnoid hemorrhage in comparison with the prevalence of unruptured IAs suggests that the vast majority of intracranial aneurysms do not rupture and that identifying those at highest risk is important in defining the optimal management. The most important factors predicting rupture are aneurysm size and site. In addition to ambiental factors (smoking, excessive alcohol consumption and hypertension), epidemiological studies have demonstrated a familiar influence contributing to the pathogenesis of intracranial aneurysms, with increased frequency in first- and second-degree relatives of people with subarachnoid hemorrhage. In comparison to sporadic aneurysms, familial aneurysms tend to be larger, more often located at the middle cerebral artery, and more likely to be multiple. Other than familiar occurrence, there are several heritable conditions associated with intracranial aneurysm formation, including autosomal dominant polycystic kidney disease, neurofibromatosis type I, Marfan syndrome, multiple endocrine neoplasia type I, pseudoxanthoma elasticum, hereditary hemorrhagic telangiectasia, and Ehlers-Danlos syndrome type II and IV. The familial occurrence and the association with heritable conditions indicate that genetic factors may play a role in the development of intracranial aneurysms. Genome-wide linkage studies in families and sib pairs with intracranial aneurysms have identified several loci on chromosomes showing suggestive evidence of linkage, particularly on chromosomes 1p34.3–p36.13, 7q11, 19q13.3, and Xp22. For the loci on 1p34.3–p36.13 and 7q11, a moderate positive association with positional candidate genes has been demonstrated (perlecan gene, elastin gene, collagen type 1 A2

Epidemiology and genetics of intracranial aneurysms

Energy Technology Data Exchange (ETDEWEB)

Caranci, F., E-mail: ferdinandocaranci@libero.it [Unit of Neuroradiology, Department of Diagnostic Radiology and Radiotherapy, Federico II University, Naples (Italy); Briganti, F., E-mail: frabriga@unina.it [Unit of Neuroradiology, Department of Diagnostic Radiology and Radiotherapy, Federico II University, Naples (Italy); Cirillo, L.; Leonardi, M. [Neuroradiology service, Bellaria Hospital, Bologna (Italy); Muto, M., E-mail: mutomar@tiscali.it [Neuroradiology Service Cardarelli Hospital Naples (Italy)

2013-10-01

Intracranial aneurysms are acquired lesions (5–10% of the population), a fraction of which rupture leading to subarachnoid hemorrhage with devastating consequences. Until now, the exact etiology of intracranial aneurysms formation remains unclear. The low incidence of subarachnoid hemorrhage in comparison with the prevalence of unruptured IAs suggests that the vast majority of intracranial aneurysms do not rupture and that identifying those at highest risk is important in defining the optimal management. The most important factors predicting rupture are aneurysm size and site. In addition to ambiental factors (smoking, excessive alcohol consumption and hypertension), epidemiological studies have demonstrated a familiar influence contributing to the pathogenesis of intracranial aneurysms, with increased frequency in first- and second-degree relatives of people with subarachnoid hemorrhage. In comparison to sporadic aneurysms, familial aneurysms tend to be larger, more often located at the middle cerebral artery, and more likely to be multiple. Other than familiar occurrence, there are several heritable conditions associated with intracranial aneurysm formation, including autosomal dominant polycystic kidney disease, neurofibromatosis type I, Marfan syndrome, multiple endocrine neoplasia type I, pseudoxanthoma elasticum, hereditary hemorrhagic telangiectasia, and Ehlers-Danlos syndrome type II and IV. The familial occurrence and the association with heritable conditions indicate that genetic factors may play a role in the development of intracranial aneurysms. Genome-wide linkage studies in families and sib pairs with intracranial aneurysms have identified several loci on chromosomes showing suggestive evidence of linkage, particularly on chromosomes 1p34.3–p36.13, 7q11, 19q13.3, and Xp22. For the loci on 1p34.3–p36.13 and 7q11, a moderate positive association with positional candidate genes has been demonstrated (perlecan gene, elastin gene, collagen type 1 A2

MAC Protocol for Ad Hoc Networks Using a Genetic Algorithm

Science.gov (United States)

Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L.

2014-01-01

The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. PMID:25140339

MAC Protocol for Ad Hoc Networks Using a Genetic Algorithm

Directory of Open Access Journals (Sweden)

Omar Elizarraras

2014-01-01

Full Text Available The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15% compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput.

A genetic algorithm for solving supply chain network design model

Science.gov (United States)

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.

Scale-dependent genetic structure of the Idaho giant salamander (Dicamptodon aterrimus) in stream networks

Science.gov (United States)

Lindy B. Mullen; H. Arthur Woods; Michael K. Schwartz; Adam J. Sepulveda; Winsor H. Lowe

2010-01-01

The network architecture of streams and rivers constrains evolutionary, demographic and ecological processes of freshwater organisms. This consistent architecture also makes stream networks useful for testing general models of population genetic structure and the scaling of gene flow. We examined genetic structure and gene flow in the facultatively paedomorphic Idaho...

Genetic epidemiology, hematological and clinical features of hemoglobinopathies in Iran.

Science.gov (United States)

Rahimi, Zohreh

2013-01-01

There is large variation in the molecular genetics and clinical features of hemoglobinopathies in Iran. Studying structural variants of hemoglobin demonstrated that the β-chain variants of hemoglobin S and D-Punjab are more prevalent in the Fars (southwestern Iran) and Kermanshah (western Iran) provinces, respectively. Also, α-chain variants of Hb Q-Iran and Hb Setif are prevalent in western Iran. The molecular basis and clinical severity of thalassemias are extremely heterogenous among Iranians due to the presence of multiethnic groups in the country. β-Thalassemia is more prevalent in northern and southern Iran. Among 52 different β-thalassemia mutations that have been identified among Iranian populations, IVSII-1 G:A is the most frequent mutation in most parts of the country. The presence of IVS I-5 G:C mutation with high frequency in southeastern Iran might reflect gene flow from neighboring countries. A wide spectrum of α-thalassemia alleles has been detected among Iranians with -α(3.7 kb) as the most prevalent α-thalassemia mutation. The prevention program of thalassemia birth in Iran has reduced the birth rate of homozygous β-thalassemia since the implementation of the program in 1997. In this review genetic epidemiology, clinical and hematological aspects of hemoglobinopathies, and the prevention programs of β-thalassemia in Iran will be discussed.

Genetic Epidemiology, Hematological and Clinical Features of Hemoglobinopathies in Iran

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Zohreh Rahimi

2013-01-01

Full Text Available There is large variation in the molecular genetics and clinical features of hemoglobinopathies in Iran. Studying structural variants of hemoglobin demonstrated that the β-chain variants of hemoglobin S and D-Punjab are more prevalent in the Fars (southwestern Iran and Kermanshah (western Iran provinces, respectively. Also, α-chain variants of Hb Q-Iran and Hb Setif are prevalent in western Iran. The molecular basis and clinical severity of thalassemias are extremely heterogenous among Iranians due to the presence of multiethnic groups in the country. β-Thalassemia is more prevalent in northern and southern Iran. Among 52 different β-thalassemia mutations that have been identified among Iranian populations, IVSII-1 G:A is the most frequent mutation in most parts of the country. The presence of IVS I-5 G:C mutation with high frequency in southeastern Iran might reflect gene flow from neighboring countries. A wide spectrum of α-thalassemia alleles has been detected among Iranians with as the most prevalent α-thalassemia mutation. The prevention program of thalassemia birth in Iran has reduced the birth rate of homozygous β-thalassemia since the implementation of the program in 1997. In this review genetic epidemiology, clinical and hematological aspects of hemoglobinopathies, and the prevention programs of β-thalassemia in Iran will be discussed.

Genetic relationships and epidemiological links between wild type 1 poliovirus isolates in Pakistan and Afghanistan.

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Angez, Mehar; Shaukat, Shahzad; Alam, Muhammad M; Sharif, Salmaan; Khurshid, Adnan; Zaidi, Syed Sohail Zahoor

2012-02-22

Efforts have been made to eliminate wild poliovirus transmission since 1988 when the World Health Organization began its global eradication campaign. Since then, the incidence of polio has decreased significantly. However, serotype 1 and serotype 3 still circulate endemically in Pakistan and Afghanistan. Both countries constitute a single epidemiologic block representing one of the three remaining major global reservoirs of poliovirus transmission. In this study we used genetic sequence data to investigate transmission links among viruses from diverse locations during 2005-2007. In order to find the origins and routes of wild type 1 poliovirus circulation, polioviruses were isolated from faecal samples of Acute Flaccid Paralysis (AFP) patients. We used viral cultures, two intratypic differentiation methods PCR, ELISA to characterize as vaccine or wild type 1 and nucleic acid sequencing of entire VP1 region of poliovirus genome to determine the genetic relatedness. One hundred eleven wild type 1 poliovirus isolates were subjected to nucleotide sequencing for genetic variation study. Considering the 15% divergence of the sequences from Sabin 1, Phylogenetic analysis by MEGA software revealed that active inter and intra country transmission of many genetically distinct strains of wild poliovirus type 1 belonged to genotype SOAS which is indigenous in this region. By grouping wild type 1 polioviruses according to nucleotide sequence homology, three distinct clusters A, B and C were obtained with multiple chains of transmission together with some silent circulations represented by orphan lineages. Our results emphasize that there was a persistent transmission of wild type 1 polioviruses in Pakistan and Afghanistan during 2005-2007. The epidemiologic information provided by the sequence data can contribute to the formulation of better strategies for poliomyelitis control to those critical areas, associated with high risk population groups which include migrants

Genetic relationships and epidemiological links between wild type 1 poliovirus isolates in Pakistan and Afghanistan

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Angez Mehar

2012-02-01

Full Text Available Abstract Background/Aim Efforts have been made to eliminate wild poliovirus transmission since 1988 when the World Health Organization began its global eradication campaign. Since then, the incidence of polio has decreased significantly. However, serotype 1 and serotype 3 still circulate endemically in Pakistan and Afghanistan. Both countries constitute a single epidemiologic block representing one of the three remaining major global reservoirs of poliovirus transmission. In this study we used genetic sequence data to investigate transmission links among viruses from diverse locations during 2005-2007. Methods In order to find the origins and routes of wild type 1 poliovirus circulation, polioviruses were isolated from faecal samples of Acute Flaccid Paralysis (AFP patients. We used viral cultures, two intratypic differentiation methods PCR, ELISA to characterize as vaccine or wild type 1 and nucleic acid sequencing of entire VP1 region of poliovirus genome to determine the genetic relatedness. Results One hundred eleven wild type 1 poliovirus isolates were subjected to nucleotide sequencing for genetic variation study. Considering the 15% divergence of the sequences from Sabin 1, Phylogenetic analysis by MEGA software revealed that active inter and intra country transmission of many genetically distinct strains of wild poliovirus type 1 belonged to genotype SOAS which is indigenous in this region. By grouping wild type 1 polioviruses according to nucleotide sequence homology, three distinct clusters A, B and C were obtained with multiple chains of transmission together with some silent circulations represented by orphan lineages. Conclusion Our results emphasize that there was a persistent transmission of wild type1 polioviruses in Pakistan and Afghanistan during 2005-2007. The epidemiologic information provided by the sequence data can contribute to the formulation of better strategies for poliomyelitis control to those critical areas

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm

International Nuclear Information System (INIS)

Zu Yun-Xiao; Zhou Jie

2012-01-01

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algorithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate. (geophysics, astronomy, and astrophysics)

Causal models in epidemiology: past inheritance and genetic future

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Kriebel David

2006-07-01

Full Text Available Abstract The eruption of genetic research presents a tremendous opportunity to epidemiologists to improve our ability to identify causes of ill health. Epidemiologists have enthusiastically embraced the new tools of genomics and proteomics to investigate gene-environment interactions. We argue that neither the full import nor limitations of such studies can be appreciated without clarifying underlying theoretical models of interaction, etiologic fraction, and the fundamental concept of causality. We therefore explore different models of causality in the epidemiology of disease arising out of genes, environments, and the interplay between environments and genes. We begin from Rothman's "pie" model of necessary and sufficient causes, and then discuss newer approaches, which provide additional insights into multifactorial causal processes. These include directed acyclic graphs and structural equation models. Caution is urged in the application of two essential and closely related concepts found in many studies: interaction (effect modification and the etiologic or attributable fraction. We review these concepts and present four important limitations. 1. Interaction is a fundamental characteristic of any causal process involving a series of probabilistic steps, and not a second-order phenomenon identified after first accounting for "main effects". 2. Standard methods of assessing interaction do not adequately consider the life course, and the temporal dynamics through which an individual's sufficient cause is completed. Different individuals may be at different stages of development along the path to disease, but this is not usually measurable. Thus, for example, acquired susceptibility in children can be an important source of variation. 3. A distinction must be made between individual-based and population-level models. Most epidemiologic discussions of causality fail to make this distinction. 4. At the population level, there is additional

Neural networks for predicting breeding values and genetic gains

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Gabi Nunes Silva

2014-12-01

Full Text Available Analysis using Artificial Neural Networks has been described as an approach in the decision-making process that, although incipient, has been reported as presenting high potential for use in animal and plant breeding. In this study, we introduce the procedure of using the expanded data set for training the network. Wealso proposed using statistical parameters to estimate the breeding value of genotypes in simulated scenarios, in addition to the mean phenotypic value in a feed-forward back propagation multilayer perceptron network. After evaluating artificial neural network configurations, our results showed its superiority to estimates based on linear models, as well as its applicability in the genetic value prediction process. The results further indicated the good generalization performance of the neural network model in several additional validation experiments.

Character Recognition Using Genetically Trained Neural Networks

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Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

1998-10-01

Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the

Ethics, big data and computing in epidemiology and public health.

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Salerno, Jennifer; Knoppers, Bartha M; Lee, Lisa M; Hlaing, WayWay M; Goodman, Kenneth W

2017-05-01

This article reflects on the activities of the Ethics Committee of the American College of Epidemiology (ACE). Members of the Ethics Committee identified an opportunity to elaborate on knowledge gained since the inception of the original Ethics Guidelines published by the ACE Ethics and Standards of Practice Committee in 2000. The ACE Ethics Committee presented a symposium session at the 2016 Epidemiology Congress of the Americas in Miami on the evolving complexities of ethics and epidemiology as it pertains to "big data." This article presents a summary and further discussion of that symposium session. Three topic areas were presented: the policy implications of big data and computing, the fallacy of "secondary" data sources, and the duty of citizens to contribute to big data. A balanced perspective is needed that provides safeguards for individuals but also furthers research to improve population health. Our in-depth review offers next steps for teaching of ethics and epidemiology, as well as for epidemiological research, public health practice, and health policy. To address contemporary topics in the area of ethics and epidemiology, the Ethics Committee hosted a symposium session on the timely topic of big data. Technological advancements in clinical medicine and genetic epidemiology research coupled with rapid advancements in data networks, storage, and computation at a lower cost are resulting in the growth of huge data repositories. Big data increases concerns about data integrity; informed consent; protection of individual privacy, confidentiality, and harm; data reidentification; and the reporting of faulty inferences. Copyright © 2017 Elsevier Inc. All rights reserved.

Genetic epidemiology of motor neuron disease-associated variants in the Scottish population.

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Black, Holly A; Leighton, Danielle J; Cleary, Elaine M; Rose, Elaine; Stephenson, Laura; Colville, Shuna; Ross, David; Warner, Jon; Porteous, Mary; Gorrie, George H; Swingler, Robert; Goldstein, David; Harms, Matthew B; Connick, Peter; Pal, Suvankar; Aitman, Timothy J; Chandran, Siddharthan

2017-03-01

Genetic understanding of motor neuron disease (MND) has evolved greatly in the past 10 years, including the recent identification of association between MND and variants in TBK1 and NEK1. Our aim was to determine the frequency of pathogenic variants in known MND genes and to assess whether variants in TBK1 and NEK1 contribute to the burden of MND in the Scottish population. SOD1, TARDBP, OPTN, TBK1, and NEK1 were sequenced in 441 cases and 400 controls. In addition to 44 cases known to carry a C9orf72 hexanucleotide repeat expansion, we identified 31 cases and 2 controls that carried a loss-of-function or pathogenic variant. Loss-of-function variants were found in TBK1 in 3 cases and no controls and, separately, in NEK1 in 3 cases and no controls. This study provides an accurate description of the genetic epidemiology of MND in Scotland and provides support for the contribution of both TBK1 and NEK1 to MND susceptibility in the Scottish population. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

HIV-1 subtype F1 epidemiological networks among Italian heterosexual males are associated with introduction events from South America.

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Lai, Alessia; Simonetti, Francesco R; Zehender, Gianguglielmo; De Luca, Andrea; Micheli, Valeria; Meraviglia, Paola; Corsi, Paola; Bagnarelli, Patrizia; Almi, Paolo; Zoncada, Alessia; Paolucci, Stefania; Gonnelli, Angela; Colao, Grazia; Tacconi, Danilo; Franzetti, Marco; Ciccozzi, Massimo; Zazzi, Maurizio; Balotta, Claudia

2012-01-01

About 40% of the Italian HIV-1 epidemic due to non-B variants is sustained by F1 clade, which circulates at high prevalence in South America and Eastern Europe. Aim of this study was to define clade F1 origin, population dynamics and epidemiological networks through phylogenetic approaches. We analyzed pol sequences of 343 patients carrying F1 subtype stored in the ARCA database from 1998 to 2009. Citizenship of patients was as follows: 72.6% Italians, 9.3% South Americans and 7.3% Rumanians. Heterosexuals, Homo-bisexuals, Intravenous Drug Users accounted for 58.1%, 24.0% and 8.8% of patients, respectively. Phylogenetic analysis indicated that 70% of sequences clustered in 27 transmission networks. Two distinct groups were identified; the first clade, encompassing 56 sequences, included all Rumanian patients. The second group involved the remaining clusters and included 10 South American Homo-bisexuals in 9 distinct clusters. Heterosexual modality of infection was significantly associated with the probability to be detected in transmission networks. Heterosexuals were prevalent either among Italians (67.2%) or Rumanians (50%); by contrast, Homo-bisexuals accounted for 71.4% of South Americans. Among patients with resistant strains the proportion of clustering sequences was 57.1%, involving 14 clusters (51.8%). Resistance in clusters tended to be higher in South Americans (28.6%) compared to Italian (17.7%) and Rumanian patients (14.3%). A striking proportion of epidemiological networks could be identified in heterosexuals carrying F1 subtype residing in Italy. Italian Heterosexual males predominated within epidemiological clusters while foreign patients were mainly Heterosexual Rumanians, both males and females, and South American Homo-bisexuals. Tree topology suggested that F1 variant from South America gave rise to the Italian F1 epidemic through multiple introduction events. The contact tracing also revealed an unexpected burden of resistance in epidemiological

Design and Implementation of the International Genetics and Translational Research in Transplantation Network.

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2015-11-01

Genetic association studies of transplantation outcomes have been hampered by small samples and highly complex multifactorial phenotypes, hindering investigations of the genetic architecture of a range of comorbidities which significantly impact graft and recipient life expectancy. We describe here the rationale and design of the International Genetics & Translational Research in Transplantation Network. The network comprises 22 studies to date, including 16494 transplant recipients and 11669 donors, of whom more than 5000 are of non-European ancestry, all of whom have existing genomewide genotype data sets. We describe the rich genetic and phenotypic information available in this consortium comprising heart, kidney, liver, and lung transplant cohorts. We demonstrate significant power in International Genetics & Translational Research in Transplantation Network to detect main effect association signals across regions such as the MHC region as well as genomewide for transplant outcomes that span all solid organs, such as graft survival, acute rejection, new onset of diabetes after transplantation, and for delayed graft function in kidney only. This consortium is designed and statistically powered to deliver pioneering insights into the genetic architecture of transplant-related outcomes across a range of different solid-organ transplant studies. The study design allows a spectrum of analyses to be performed including recipient-only analyses, donor-recipient HLA mismatches with focus on loss-of-function variants and nonsynonymous single nucleotide polymorphisms.

Studying the Genetics of Complex Disease With Ancestry-Specific Human Phenotype Networks: The Case of Type 2 Diabetes in East Asian Populations.

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Qiu, Jingya; Moore, Jason H; Darabos, Christian

2016-05-01

Genome-wide association studies (GWAS) have led to the discovery of over 200 single nucleotide polymorphisms (SNPs) associated with type 2 diabetes mellitus (T2DM). Additionally, East Asians develop T2DM at a higher rate, younger age, and lower body mass index than their European ancestry counterparts. The reason behind this occurrence remains elusive. With comprehensive searches through the National Human Genome Research Institute (NHGRI) GWAS catalog literature, we compiled a database of 2,800 ancestry-specific SNPs associated with T2DM and 70 other related traits. Manual data extraction was necessary because the GWAS catalog reports statistics such as odds ratio and P-value, but does not consistently include ancestry information. Currently, many statistics are derived by combining initial and replication samples from study populations of mixed ancestry. Analysis of all-inclusive data can be misleading, as not all SNPs are transferable across diverse populations. We used ancestry data to construct ancestry-specific human phenotype networks (HPN) centered on T2DM. Quantitative and visual analysis of network models reveal the genetic disparities between ancestry groups. Of the 27 phenotypes in the East Asian HPN, six phenotypes were unique to the network, revealing the underlying ancestry-specific nature of some SNPs associated with T2DM. We studied the relationship between T2DM and five phenotypes unique to the East Asian HPN to generate new interaction hypotheses in a clinical context. The genetic differences found in our ancestry-specific HPNs suggest different pathways are involved in the pathogenesis of T2DM among different populations. Our study underlines the importance of ancestry in the development of T2DM and its implications in pharmocogenetics and personalized medicine. © 2016 The Authors. *Genetic Epidemiology Published by Wiley Periodicals, Inc.

Proceedings of the African Field Epidemiology Network (AFENET) Scientific Conference 17-22 November 2013 Addis Ababa, Ethiopia: plenaries and oral presentations.

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Gitta, Sheba Nakacubo; Mwesiga, Allan; Kamadjeu, Raoul

2015-01-01

Biennially, trainees and graduates of Field Epidemiology and Laboratory Training Programs (FELTPs) are presented with a platform to share investigations and projects undertaken during their two-year training in Applied Epidemiology. The African Field Epidemiology Network (AFENET) Scientific Conference, is a perfect opportunity for public health professionals from various sectors and organizations to come together to discuss issues that impact on public health in Africa. This year's conference was organized by the Ethiopian Health and Nutrition Research Institute in collaboration with the Ethiopia Ministry of Health, Ethiopian Public Health Association (EPHA), Ethiopia Field Epidemiology Training Program (EFETP), Addis Ababa University (AAU), Training Programs in Epidemiology and Public Health Interventions Network (TEPHINET) and AFENET. Participants at this year's conference numbered 400 from over 20 countries including; Angola, Burkina Faso, Cameroon, Central African Republic, Democratic Republic of the Congo, Ethiopia, Ghana, Indonesia, Kenya, Mozambique, Namibia, Nigeria, Rwanda, South Africa, Sudan, Tanzania, Uganda, Yemen and Zimbabwe. The topics covered in the 144 oral presentations included: global health security, emergency response, public health informatics, vaccine preventable diseases, immunization, outbreak investigation, Millennium Development Goals, Non-Communicable Diseases, and public health surveillance. The theme for the 5th AFENET Scientific Conference was; "Addressing Public Health Priorities in Africa through FELTPs." Previous AFENET Scientific conferences have been held in: Accra, Ghana (2005), Kampala, Uganda (2007), Mombasa, Kenya (2009) and Dar es Salaam, Tanzania (2011).

Molecular Epidemiology and Genetic Variation of Pathogenic Vibrio parahaemolyticus in Peru

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Gavilan, Ronnie G.; Zamudio, Maria L.; Martinez-Urtaza, Jaime

2013-01-01

Vibrio parahaemolyticus is a foodborne pathogen that has become a public health concern at the global scale. The epidemiological significance of V. parahaemolyticus infections in Latin America received little attention until the winter of 1997 when cases related to the pandemic clone were detected in the region, changing the epidemic dynamics of this pathogen in Peru. With the aim to assess the impact of the arrival of the pandemic clone on local populations of pathogenic V. parahaemolyticus in Peru, we investigated the population genetics and genomic variation in a complete collection of non-pandemic strains recovered from clinical sources in Peru during the pre- and post-emergence periods of the pandemic clone. A total of 56 clinical strains isolated in Peru during the period 1994 to 2007, 13 strains from Chile and 20 strains from Asia were characterized by Multilocus Sequence Typing (MLST) and checked for the presence of Variable Genomic Regions (VGRs). The emergence of O3:K6 cases in Peru implied a drastic disruption of the seasonal dynamics of infections and a shift in the serotype dominance of pathogenic V. parahaemolyticus. After the arrival of the pandemic clone, a great diversity of serovars not previously reported was detected in the country, which supports the introduction of additional populations cohabitating with the pandemic group. Moreover, the presence of genomic regions characteristic of the pandemic clone in other non-pandemic strains may represent early evidence of genetic transfer from the introduced population to the local communities. Finally, the results of this study stress the importance of population admixture, horizontal genetic transfer and homologous recombination as major events shaping the structure and diversity of pathogenic V. parahaemolyticus. PMID:23696906

Genetic analysis of the heparan modification network in Caenorhabditis elegans.

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Townley, Robert A; Bülow, Hannes E

2011-05-13

Heparan sulfates (HS) are highly modified sugar polymers in multicellular organisms that function in cell adhesion and cellular responses to protein signaling. Functionally distinct, cell type-dependent HS modification patterns arise as the result of a conserved network of enzymes that catalyze deacetylations, sulfations, and epimerizations in specific positions of the sugar residues. To understand the genetic interactions of the enzymes during the HS modification process, we have measured the composition of HS purified from mutant strains of Caenorhabditis elegans. From these measurements we have developed a genetic network model of HS modification. We find the interactions to be highly recursive positive feed-forward and negative feedback loops. Our genetic analyses show that the HS C-5 epimerase hse-5, the HS 2-O-sulfotransferase hst-2, or the HS 6-O-sulfotransferase hst-6 inhibit N-sulfation. In contrast, hse-5 stimulates both 2-O- and 6-O-sulfation and, hst-2 and hst-6 inhibit 6-O- and 2-O-sulfation, respectively. The effects of hst-2 and hst-6 on N-sulfation, 6-O-sulfation, and 2-O-sulfation appear largely dependent on hse-5 function. This core of regulatory interactions is further modulated by 6-O-endosulfatase activity (sul-1). 47% of all 6-O-sulfates get removed from HS and this editing process is dependent on hst-2, thereby providing additional negative feedback between 2-O- and 6-O-sulfation. These findings suggest that the modification patterns are highly sensitive to the relative composition of the HS modification enzymes. Our comprehensive genetic analysis forms the basis of understanding the HS modification network in metazoans.

Genetic Analysis of the Heparan Modification Network in Caenorhabditis elegans*

Science.gov (United States)

Townley, Robert A.; Bülow, Hannes E.

2011-01-01

Heparan sulfates (HS) are highly modified sugar polymers in multicellular organisms that function in cell adhesion and cellular responses to protein signaling. Functionally distinct, cell type-dependent HS modification patterns arise as the result of a conserved network of enzymes that catalyze deacetylations, sulfations, and epimerizations in specific positions of the sugar residues. To understand the genetic interactions of the enzymes during the HS modification process, we have measured the composition of HS purified from mutant strains of Caenorhabditis elegans. From these measurements we have developed a genetic network model of HS modification. We find the interactions to be highly recursive positive feed-forward and negative feedback loops. Our genetic analyses show that the HS C-5 epimerase hse-5, the HS 2-O-sulfotransferase hst-2, or the HS 6-O-sulfotransferase hst-6 inhibit N-sulfation. In contrast, hse-5 stimulates both 2-O- and 6-O-sulfation and, hst-2 and hst-6 inhibit 6-O- and 2-O-sulfation, respectively. The effects of hst-2 and hst-6 on N-sulfation, 6-O-sulfation, and 2-O-sulfation appear largely dependent on hse-5 function. This core of regulatory interactions is further modulated by 6-O-endosulfatase activity (sul-1). 47% of all 6-O-sulfates get removed from HS and this editing process is dependent on hst-2, thereby providing additional negative feedback between 2-O- and 6-O-sulfation. These findings suggest that the modification patterns are highly sensitive to the relative composition of the HS modification enzymes. Our comprehensive genetic analysis forms the basis of understanding the HS modification network in metazoans. PMID:21454666

GENETIC ALGORITHM BASED CONCEPT DESIGN TO OPTIMIZE NETWORK LOAD BALANCE

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Ashish Jain

2012-07-01

Full Text Available Multiconstraints optimal network load balancing is an NP-hard problem and it is an important part of traffic engineering. In this research we balance the network load using classical method (brute force approach and dynamic programming is used but result shows the limitation of this method but at a certain level we recognized that the optimization of balanced network load with increased number of nodes and demands is intractable using the classical method because the solution set increases exponentially. In such case the optimization techniques like evolutionary techniques can employ for optimizing network load balance. In this paper we analyzed proposed classical algorithm and evolutionary based genetic approach is devise as well as proposed in this paper for optimizing the balance network load.

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

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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

[About twins: Epidemiological, genetic, and obstetrical aspects, specific risks, and outcome].

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Tauzin, M; Felix, A; Michot, C; Dedieu, C; Aoust, L; Fortas, F; Guillier, C; Ngo, J; Wachter, P-Y; Petermann, L; Kermorvant-Duchemin, E

2017-12-01

The incidence of twin pregnancies has increased steadily for the last 40 years due to assisted reproductive technology and increased maternal childbearing age. Multiple pregnancies, especially monochorionic twin pregnancies, carry a high risk for the mother and the fetuses and require close follow-up. Twins are exposed to a higher risk of perinatal anoxia, in utero fetal demise, preterm birth, congenital malformations, fetal growth restriction, and vascular complications. Compared to singletons, twins are at higher risk of perinatal mortality and impaired neurodevelopmental outcome, justifying a thorough follow-up by pediatricians, including assessment and management of familial and psychosocial impact. This paper discusses the epidemiological, obstetrical, and genetic issues raised by twin pregnancies and reviews the data on the perinatal and neurological long-term outcomes of twins, as well as the psychosocial impact of multiple births on twins and their families. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Genetic Evaluation of Children with Global Developmental Delay—Current Status of Network Systems in Taiwan

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Yong-Lin Foo

2015-08-01

Full Text Available This review article aims to introduce the screening and referral network of genetic evaluation for children with developmental delay in Taiwan. For these children, integrated systems provide services from the medical, educational, and social welfare sectors. All cities and counties in Taiwan have established a network for screening, detection, referral, evaluation, and intervention services. Increased awareness improves early detection and intervention. There remains a gap between supply and demand, especially with regard to financial resources and professional manpower. Genetic etiology has a major role in prenatal causes of developmental delay. A summary of reports on some related genetic disorders in the Taiwanese population is included in this review. Genetic diagnosis allows counseling with regard to recurrence risk and prevention. Networking with neonatal screening, laboratory diagnosis, genetic counseling, and orphan drugs logistics systems can provide effective treatment for patients. In Taiwan, several laboratories provide genetic tests for clinical diagnosis. Accessibility to advanced expensive tests such as gene chips or whole exome sequencing is limited because of funding problems; however, the service system in Taiwan can still operate in a relatively cost-effective manner. This experience in Taiwan may serve as a reference for other countries.

[Genetic subtype and epidemiological feature of HIV-1 circulating strains among recently infected patients in Fujian province].

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Deng, Yongyue; Zhang, Chunyang; Yan, Yansheng; Yan, Pingping; Wu, Shouli

2014-06-01

In order to evaluate the distribution of genetic subtypes and epidemiological feature of HIV-1 circulating strains in Fujian province. Blood samples and epidemiological data were collected from 104 newly infected patients who were distinguished by BED-CEIA methodology, during 2011-2012. Viral sequences(n = 81) of HIV-1 gag, env, and pol segments were amplified by nested PCR. Subtypes B and four Circulating Recombinant Forms, (CRF01_AE, CRF07_BC, CRF08_BC and CRF55_01B) were found in the samples, CRF01_AE(45.68%)and CRF07_BC(35.80%) were the two main HIV-1 strains in Fujian province. Compared with previous data, the proportion of CRF07_BC rose significantly while it gradually decreased in CRF01_AE. Heterosexual contact was still the principal transmission route in Fujian province, but the number of infection among men-who-have-sex-with- men grew rapidly. Results from this study suggested that different subtypes of HIV-1 strain existed in Fujian province. The distribution of subtypes and the mode of transmission were changing with the progress of epidemic. Dynamic monitoring of the molecular epidemiology trends of HIV-1 infection should be enhanced.

An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks.

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Yoon, Yourim; Kim, Yong-Hyuk

2013-10-01

Sensor networks have a lot of applications such as battlefield surveillance, environmental monitoring, and industrial diagnostics. Coverage is one of the most important performance metrics for sensor networks since it reflects how well a sensor field is monitored. In this paper, we introduce the maximum coverage deployment problem in wireless sensor networks and analyze the properties of the problem and its solution space. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and therefore, we need a more intelligent way for sensor deployment. We found that the phenotype space of the problem is a quotient space of the genotype space in a mathematical view. Based on this property, we propose an efficient genetic algorithm using a novel normalization method. A Monte Carlo method is adopted to design an efficient evaluation function, and its computation time is decreased without loss of solution quality using a method that starts from a small number of random samples and gradually increases the number for subsequent generations. The proposed genetic algorithms could be further improved by combining with a well-designed local search. The performance of the proposed genetic algorithm is shown by a comparative experimental study. When compared with random deployment and existing methods, our genetic algorithm was not only about twice faster, but also showed significant performance improvement in quality.

Applications of a formal approach to decipher discrete genetic networks.

Science.gov (United States)

Corblin, Fabien; Fanchon, Eric; Trilling, Laurent

2010-07-20

A growing demand for tools to assist the building and analysis of biological networks exists in systems biology. We argue that the use of a formal approach is relevant and applicable to address questions raised by biologists about such networks. The behaviour of these systems being complex, it is essential to exploit efficiently every bit of experimental information. In our approach, both the evolution rules and the partial knowledge about the structure and the behaviour of the network are formalized using a common constraint-based language. In this article our formal and declarative approach is applied to three biological applications. The software environment that we developed allows to specifically address each application through a new class of biologically relevant queries. We show that we can describe easily and in a formal manner the partial knowledge about a genetic network. Moreover we show that this environment, based on a constraint algorithmic approach, offers a wide variety of functionalities, going beyond simple simulations, such as proof of consistency, model revision, prediction of properties, search for minimal models relatively to specified criteria. The formal approach proposed here deeply changes the way to proceed in the exploration of genetic and biochemical networks, first by avoiding the usual trial-and-error procedure, and second by placing the emphasis on sets of solutions, rather than a single solution arbitrarily chosen among many others. Last, the constraint approach promotes an integration of model and experimental data in a single framework.

Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm

Directory of Open Access Journals (Sweden)

Jianyong Liu

2015-01-01

Full Text Available The method that the real-coded quantum-inspired genetic algorithm (RQGA used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.

Genetic epidemiology of Scheuermann's disease

DEFF Research Database (Denmark)

Damborg, Frank; Engell, Vilhelm; Nielsen, Jan

2011-01-01

The genetic/environmental etiology of Scheuermann's disease is unclear. We estimated the heritability of the disease using an etiological model adjusted for sex and time of diagnosis, and examined whether the prevalence of Scheuermann's disease was constant over time.......The genetic/environmental etiology of Scheuermann's disease is unclear. We estimated the heritability of the disease using an etiological model adjusted for sex and time of diagnosis, and examined whether the prevalence of Scheuermann's disease was constant over time....

District Heating Network Design and Configuration Optimization with Genetic Algorithm

DEFF Research Database (Denmark)

Li, Hongwei; Svendsen, Svend

2013-01-01

In this paper, the configuration of a district heating network which connects from the heating plant to the end users is optimized. Each end user in the network represents a building block. The connections between the heat generation plant and the end users are represented with mixed integer...... and the pipe friction and heat loss formulations are non-linear. In order to find the optimal district heating network configuration, genetic algorithm which handles the mixed integer nonlinear programming problem is chosen. The network configuration is represented with binary and integer encoding...... and it is optimized in terms of the net present cost. The optimization results indicates that the optimal DH network configuration is determined by multiple factors such as the consumer heating load, the distance between the heating plant to the consumer, the design criteria regarding the pressure and temperature...

Genetic Evaluation of Children with Global Developmental Delay--Current Status of Network Systems in Taiwan.

Science.gov (United States)

Foo, Yong-Lin; Chow, Julie Chi; Lai, Ming-Chi; Tsai, Wen-Hui; Tung, Li-Chen; Kuo, Mei-Chin; Lin, Shio-Jean

2015-08-01

This review article aims to introduce the screening and referral network of genetic evaluation for children with developmental delay in Taiwan. For these children, integrated systems provide services from the medical, educational, and social welfare sectors. All cities and counties in Taiwan have established a network for screening, detection, referral, evaluation, and intervention services. Increased awareness improves early detection and intervention. There remains a gap between supply and demand, especially with regard to financial resources and professional manpower. Genetic etiology has a major role in prenatal causes of developmental delay. A summary of reports on some related genetic disorders in the Taiwanese population is included in this review. Genetic diagnosis allows counseling with regard to recurrence risk and prevention. Networking with neonatal screening, laboratory diagnosis, genetic counseling, and orphan drugs logistics systems can provide effective treatment for patients. In Taiwan, several laboratories provide genetic tests for clinical diagnosis. Accessibility to advanced expensive tests such as gene chips or whole exome sequencing is limited because of funding problems; however, the service system in Taiwan can still operate in a relatively cost-effective manner. This experience in Taiwan may serve as a reference for other countries. Copyright © 2014. Published by Elsevier B.V.

[The international network and Italian modernization. Ruggero Ceppellini, genetics, and HLA].

Science.gov (United States)

Capocci, Mauro

2014-01-01

The paper reconstructs the scientific career of Ruggero Ceppellini, focusing especially on his role in the discovery of the genetic system underlying the Human Leucocyte Antigen. From his earliest investigations in blood group genetics, Ceppellini quickly became an internationally acknowledged authority in the field of immunogenetics--the study of genetics by means of immunological tools--and participated to the endeavor that ultimately yelded a new meaning for the word: thanks to the pioneering research in the HLA field, immunogenetics became the study of the genetic control of immune system. The paper will also place Ceppellini's scientific work against the backdrop of the modernization of Italian genetics after WWII, resulting from the efforts of a handful of scientists to connect to international networks and adopting new methodologies in life sciences.

A candidate multimodal functional genetic network for thermal adaptation

Directory of Open Access Journals (Sweden)

Katharina C. Wollenberg Valero

2014-09-01

Full Text Available Vertebrate ectotherms such as reptiles provide ideal organisms for the study of adaptation to environmental thermal change. Comparative genomic and exomic studies can recover markers that diverge between warm and cold adapted lineages, but the genes that are functionally related to thermal adaptation may be difficult to identify. We here used a bioinformatics genome-mining approach to predict and identify functions for suitable candidate markers for thermal adaptation in the chicken. We first established a framework of candidate functions for such markers, and then compiled the literature on genes known to adapt to the thermal environment in different lineages of vertebrates. We then identified them in the genomes of human, chicken, and the lizard Anolis carolinensis, and established a functional genetic interaction network in the chicken. Surprisingly, markers initially identified from diverse lineages of vertebrates such as human and fish were all in close functional relationship with each other and more associated than expected by chance. This indicates that the general genetic functional network for thermoregulation and/or thermal adaptation to the environment might be regulated via similar evolutionarily conserved pathways in different vertebrate lineages. We were able to identify seven functions that were statistically overrepresented in this network, corresponding to four of our originally predicted functions plus three unpredicted functions. We describe this network as multimodal: central regulator genes with the function of relaying thermal signal (1, affect genes with different cellular functions, namely (2 lipoprotein metabolism, (3 membrane channels, (4 stress response, (5 response to oxidative stress, (6 muscle contraction and relaxation, and (7 vasodilation, vasoconstriction and regulation of blood pressure. This network constitutes a novel resource for the study of thermal adaptation in the closely related nonavian reptiles and

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.

Between "design" and "bricolage": genetic networks, levels of selection, and adaptive evolution.

Science.gov (United States)

Wilkins, Adam S

2007-05-15

The extent to which "developmental constraints" in complex organisms restrict evolutionary directions remains contentious. Yet, other forms of internal constraint, which have received less attention, may also exist. It will be argued here that a set of partial constraints below the level of phenotypes, those involving genes and molecules, influences and channels the set of possible evolutionary trajectories. At the top-most organizational level there are the genetic network modules, whose operations directly underlie complex morphological traits. The properties of these network modules, however, have themselves been set by the evolutionary history of the component genes and their interactions. Characterization of the components, structures, and operational dynamics of specific genetic networks should lead to a better understanding not only of the morphological traits they underlie but of the biases that influence the directions of evolutionary change. Furthermore, such knowledge may permit assessment of the relative degrees of probability of short evolutionary trajectories, those on the microevolutionary scale. In effect, a "network perspective" may help transform evolutionary biology into a scientific enterprise with greater predictive capability than it has hitherto possessed.

XMRF: an R package to fit Markov Networks to high-throughput genetics data.

Science.gov (United States)

Wan, Ying-Wooi; Allen, Genevera I; Baker, Yulia; Yang, Eunho; Ravikumar, Pradeep; Anderson, Matthew; Liu, Zhandong

2016-08-26

Technological advances in medicine have led to a rapid proliferation of high-throughput "omics" data. Tools to mine this data and discover disrupted disease networks are needed as they hold the key to understanding complicated interactions between genes, mutations and aberrations, and epi-genetic markers. We developed an R software package, XMRF, that can be used to fit Markov Networks to various types of high-throughput genomics data. Encoding the models and estimation techniques of the recently proposed exponential family Markov Random Fields (Yang et al., 2012), our software can be used to learn genetic networks from RNA-sequencing data (counts via Poisson graphical models), mutation and copy number variation data (categorical via Ising models), and methylation data (continuous via Gaussian graphical models). XMRF is the only tool that allows network structure learning using the native distribution of the data instead of the standard Gaussian. Moreover, the parallelization feature of the implemented algorithms computes the large-scale biological networks efficiently. XMRF is available from CRAN and Github ( https://github.com/zhandong/XMRF ).

Fertility and pregnancy: an epidemiologic perspective

National Research Council Canada - National Science Library

Wilcox, Allen J

2010-01-01

.... Weaving together history, biology, obstetrics, pediatrics, demography, infectious diseases, molecular genetics, and evolutionary biology, Allen Wilcox brings a fresh coherence to the epidemiologic...

Genomic Resources for Cancer Epidemiology

Science.gov (United States)

This page provides links to research resources, complied by the Epidemiology and Genomics Research Program, that may be of interest to genetic epidemiologists conducting cancer research, but is not exhaustive.

Genetic algorithm and neural network hybrid approach for job-shop scheduling

OpenAIRE

Zhao, Kai; Yang, Shengxiang; Wang, Dingwei

1998-01-01

Copyright @ 1998 ACTA Press This paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CSANN) hybrid approach for job-shop scheduling problems. In the hybrid approach, GA is used to iterate for searching optimal solutions, CSANN is used to obtain feasible solutions during the iteration of genetic algorithm. Simulations have shown the valid performance of the proposed hybrid approach for job-shop scheduling with respect to the quality of solutions and ...

Towards a predictive theory for genetic regulatory networks

Science.gov (United States)

Tkacik, Gasper

When cells respond to changes in the environment by regulating the expression levels of their genes, we often draw parallels between these biological processes and engineered information processing systems. One can go beyond this qualitative analogy, however, by analyzing information transmission in biochemical ``hardware'' using Shannon's information theory. Here, gene regulation is viewed as a transmission channel operating under restrictive constraints set by the resource costs and intracellular noise. We present a series of results demonstrating that a theory of information transmission in genetic regulatory circuits feasibly yields non-trivial, testable predictions. These predictions concern strategies by which individual gene regulatory elements, e.g., promoters or enhancers, read out their signals; as well as strategies by which small networks of genes, independently or in spatially coupled settings, respond to their inputs. These predictions can be quantitatively compared to the known regulatory networks and their function, and can elucidate how reproducible biological processes, such as embryonic development, can be orchestrated by networks built out of noisy components. Preliminary successes in the gap gene network of the fruit fly Drosophila indicate that a full ab initio theoretical prediction of a regulatory network is possible, a feat that has not yet been achieved for any real regulatory network. We end by describing open challenges on the path towards such a prediction.

Epidemiology of subtypes of depression

DEFF Research Database (Denmark)

Kessing, L V

2007-01-01

depression, dysthymia, and subsyndromal states; the association between stressful life events and depression appears to diminish with the number of depressive episodes. Finally, recent genetic findings are congruent with a model indicating that the majority of depressions develop in the interplay between...... genes and stressful experiences, whereas 'reactive' depressions and 'endogenous' depressions apparently exist at a lower prevalence. CONCLUSION: Further longitudinal, analytical, and genetic epidemiologic studies are needed to reveal which conditions are mild and transient, and which may be precursors......OBJECTIVE: There is a general clinical impression that depression differs qualitatively from non-depressive conditions, and that it can be identified as a categorical entity. In contrast, epidemiological studies support the view that depression is dynamic in nature and develops on a continuous...

A parallel adaptive quantum genetic algorithm for the controllability of arbitrary networks.

Science.gov (United States)

Li, Yuhong; Gong, Guanghong; Li, Ni

2018-01-01

In this paper, we propose a novel algorithm-parallel adaptive quantum genetic algorithm-which can rapidly determine the minimum control nodes of arbitrary networks with both control nodes and state nodes. The corresponding network can be fully controlled with the obtained control scheme. We transformed the network controllability issue into a combinational optimization problem based on the Popov-Belevitch-Hautus rank condition. A set of canonical networks and a list of real-world networks were experimented. Comparison results demonstrated that the algorithm was more ideal to optimize the controllability of networks, especially those larger-size networks. We demonstrated subsequently that there were links between the optimal control nodes and some network statistical characteristics. The proposed algorithm provides an effective approach to improve the controllability optimization of large networks or even extra-large networks with hundreds of thousands nodes.

A parallel adaptive quantum genetic algorithm for the controllability of arbitrary networks

Science.gov (United States)

Li, Yuhong

2018-01-01

In this paper, we propose a novel algorithm—parallel adaptive quantum genetic algorithm—which can rapidly determine the minimum control nodes of arbitrary networks with both control nodes and state nodes. The corresponding network can be fully controlled with the obtained control scheme. We transformed the network controllability issue into a combinational optimization problem based on the Popov-Belevitch-Hautus rank condition. A set of canonical networks and a list of real-world networks were experimented. Comparison results demonstrated that the algorithm was more ideal to optimize the controllability of networks, especially those larger-size networks. We demonstrated subsequently that there were links between the optimal control nodes and some network statistical characteristics. The proposed algorithm provides an effective approach to improve the controllability optimization of large networks or even extra-large networks with hundreds of thousands nodes. PMID:29554140

District Heating Network Design and Configuration Optimization with Genetic Algorithm

DEFF Research Database (Denmark)

Li, Hongwei; Svendsen, Svend

2011-01-01

In this paper, the configuration of a district heating (DH) network which connects from the heating plant to the end users was optimized with emphasizing the network thermal performance. Each end user in the network represents a building block. The locations of the building blocks are fixed while...... the heating plant location is allowed to vary. The connection between the heat generation plant and the end users can be represented with mixed integer and the pipe friction and heat loss formulations are non-linear. In order to find the optimal DH distribution pipeline configuration, the genetic algorithm...... by multi factors as the consumer heating load, the distance between the heating plant to the consumer, the design criteria regarding pressure and temperature limitation, as well as the corresponding network heat loss....

Endogenous network states predict gain or loss of functions for genetic mutations in hepatocellular carcinoma.

Science.gov (United States)

Wang, Gaowei; Su, Hang; Yu, Helin; Yuan, Ruoshi; Zhu, Xiaomei; Ao, Ping

2016-02-01

Cancers have been typically characterized by genetic mutations. Patterns of such mutations have traditionally been analysed by posteriori statistical association approaches. One may ponder the possibility of a priori determination of any mutation regularity. Here by exploring biological processes implied in a mechanistic theory recently developed (the endogenous molecular-cellular network theory), we found that the features of genetic mutations in cancers may be predicted without any prior knowledge of mutation propensities. With hepatocellular carcinoma (HCC) as an example, we found that the normal hepatocyte and cancerous hepatocyte can be represented by robust stable states of one single endogenous network. These stable states, specified by distinct patterns of expressions or activities of proteins in the network, provide means to directly identify a set of most probable genetic mutations and their effects in HCC. As the key proteins and main interactions in the network are conserved through cell types in an organism, similar mutational features may also be found in other cancers. This analysis yielded straightforward and testable predictions on accumulated and preferred mutation spectra in normal tissue. The validation of predicted cancer state mutation patterns demonstrates the usefulness and potential of a causal dynamical framework to understand and predict genetic mutations in cancer. © 2016 The Author(s).

Genetic algorithm-based neural network for accidents diagnosis of research reactors on FPGA

International Nuclear Information System (INIS)

Ghuname, A.A.A.

2012-01-01

The Nuclear Research Reactors plants are expected to be operated with high levels of reliability, availability and safety. In order to achieve and maintain system stability and assure satisfactory and safe operation, there is increasing demand for automated systems to detect and diagnose such failures. Artificial Neural Networks (ANNs) are one of the most popular solutions because of their parallel structure, high speed, and their ability to give easy solution to complicated problems. The genetic algorithms (GAs) which are search algorithms (optimization techniques), in recent years, have been used to find the optimum construction of a neural network for definite application, as one of the advantages of its usage. Nowadays, Field Programmable Gate Arrays (FPGAs) are being an important implementation method of neural networks due to their high performance and they can easily be made parallel. The VHDL, which stands for VHSIC (Very High Speed Integrated Circuits) Hardware Description Language, have been used to describe the design behaviorally in addition to schematic and other description languages. The description of designs in synthesizable language such as VHDL make them reusable and be implemented in upgradeable systems like the Nuclear Research Reactors plants. In this thesis, the work was carried out through three main parts.In the first part, the Nuclear Research Reactors accident's pattern recognition is tackled within the artificial neural network approach. Such patterns are introduced initially without noise. And, to increase the reliability of such neural network, the noise ratio up to 50% was added for training in order to ensure the recognition of these patterns if it introduced with noise.The second part is concerned with the construction of Artificial Neural Networks (ANNs) using Genetic algorithms (GAs) for the nuclear accidents diagnosis. MATLAB ANNs toolbox and GAs toolbox are employed to optimize an ANN for this purpose. The results obtained show

Influenza epidemiology and influenza vaccine effectiveness during the 2014–2015 season: annual report from the Global Influenza Hospital Surveillance Network

Directory of Open Access Journals (Sweden)

Joan Puig-Barberà

2016-08-01

Full Text Available Abstract The Global Influenza Hospital Surveillance Network (GIHSN has established a prospective, active surveillance, hospital-based epidemiological study to collect epidemiological and virological data for the Northern and Southern Hemispheres over several consecutive seasons. It focuses exclusively on severe cases of influenza requiring hospitalization. A standard protocol is shared between sites allowing comparison and pooling of results. During the 2014–2015 influenza season, the GIHSN included seven coordinating sites from six countries (St. Petersburg and Moscow, Russian Federation; Prague, Czech Republic; Istanbul, Turkey; Beijing, China; Valencia, Spain; and Rio de Janeiro, Brazil. Here, we present the detailed epidemiological and influenza vaccine effectiveness findings for the Northern Hemisphere 2014–2015 influenza season.

Genome-wide association studies dissect the genetic networks underlying agronomical traits in soybean.

Science.gov (United States)

Fang, Chao; Ma, Yanming; Wu, Shiwen; Liu, Zhi; Wang, Zheng; Yang, Rui; Hu, Guanghui; Zhou, Zhengkui; Yu, Hong; Zhang, Min; Pan, Yi; Zhou, Guoan; Ren, Haixiang; Du, Weiguang; Yan, Hongrui; Wang, Yanping; Han, Dezhi; Shen, Yanting; Liu, Shulin; Liu, Tengfei; Zhang, Jixiang; Qin, Hao; Yuan, Jia; Yuan, Xiaohui; Kong, Fanjiang; Liu, Baohui; Li, Jiayang; Zhang, Zhiwu; Wang, Guodong; Zhu, Baoge; Tian, Zhixi

2017-08-24

Soybean (Glycine max [L.] Merr.) is one of the most important oil and protein crops. Ever-increasing soybean consumption necessitates the improvement of varieties for more efficient production. However, both correlations among different traits and genetic interactions among genes that affect a single trait pose a challenge to soybean breeding. To understand the genetic networks underlying phenotypic correlations, we collected 809 soybean accessions worldwide and phenotyped them for two years at three locations for 84 agronomic traits. Genome-wide association studies identified 245 significant genetic loci, among which 95 genetically interacted with other loci. We determined that 14 oil synthesis-related genes are responsible for fatty acid accumulation in soybean and function in line with an additive model. Network analyses demonstrated that 51 traits could be linked through the linkage disequilibrium of 115 associated loci and these links reflect phenotypic correlations. We revealed that 23 loci, including the known Dt1, E2, E1, Ln, Dt2, Fan, and Fap loci, as well as 16 undefined associated loci, have pleiotropic effects on different traits. This study provides insights into the genetic correlation among complex traits and will facilitate future soybean functional studies and breeding through molecular design.

Spatial evolutionary epidemiology of spreading epidemics.

Science.gov (United States)

Lion, S; Gandon, S

2016-10-26

Most spatial models of host-parasite interactions either neglect the possibility of pathogen evolution or consider that this process is slow enough for epidemiological dynamics to reach an equilibrium on a fast timescale. Here, we propose a novel approach to jointly model the epidemiological and evolutionary dynamics of spatially structured host and pathogen populations. Starting from a multi-strain epidemiological model, we use a combination of spatial moment equations and quantitative genetics to analyse the dynamics of mean transmission and virulence in the population. A key insight of our approach is that, even in the absence of long-term evolutionary consequences, spatial structure can affect the short-term evolution of pathogens because of the build-up of spatial differentiation in mean virulence. We show that spatial differentiation is driven by a balance between epidemiological and genetic effects, and this quantity is related to the effect of kin competition discussed in previous studies of parasite evolution in spatially structured host populations. Our analysis can be used to understand and predict the transient evolutionary dynamics of pathogens and the emergence of spatial patterns of phenotypic variation. © 2016 The Author(s).

One for all and all for One: Improving replication of genetic studies through network diffusion.

Directory of Open Access Journals (Sweden)

Daniel Lancour

2018-04-01

Full Text Available Improving accuracy in genetic studies would greatly accelerate understanding the genetic basis of complex diseases. One approach to achieve such an improvement for risk variants identified by the genome wide association study (GWAS approach is to incorporate previously known biology when screening variants across the genome. We developed a simple approach for improving the prioritization of candidate disease genes that incorporates a network diffusion of scores from known disease genes using a protein network and a novel integration with GWAS risk scores, and tested this approach on a large Alzheimer disease (AD GWAS dataset. Using a statistical bootstrap approach, we cross-validated the method and for the first time showed that a network approach improves the expected replication rates in GWAS studies. Several novel AD genes were predicted including CR2, SHARPIN, and PTPN2. Our re-prioritized results are enriched for established known AD-associated biological pathways including inflammation, immune response, and metabolism, whereas standard non-prioritized results were not. Our findings support a strategy of considering network information when investigating genetic risk factors.

Biomarkers in Prostate Cancer Epidemiology

Directory of Open Access Journals (Sweden)

Mudit Verma

2011-09-01

Full Text Available Understanding the etiology of a disease such as prostate cancer may help in identifying populations at high risk, timely intervention of the disease, and proper treatment. Biomarkers, along with exposure history and clinical data, are useful tools to achieve these goals. Individual risk and population incidence of prostate cancer result from the intervention of genetic susceptibility and exposure. Biochemical, epigenetic, genetic, and imaging biomarkers are used to identify people at high risk for developing prostate cancer. In cancer epidemiology, epigenetic biomarkers offer advantages over other types of biomarkers because they are expressed against a person’s genetic background and environmental exposure, and because abnormal events occur early in cancer development, which includes several epigenetic alterations in cancer cells. This article describes different biomarkers that have potential use in studying the epidemiology of prostate cancer. We also discuss the characteristics of an ideal biomarker for prostate cancer, and technologies utilized for biomarker assays. Among epigenetic biomarkers, most reports indicate GSTP1 hypermethylation as the diagnostic marker for prostate cancer; however, NKX2-5, CLSTN1, SPOCK2, SLC16A12, DPYS, and NSE1 also have been reported to be regulated by methylation mechanisms in prostate cancer. Current challenges in utilization of biomarkers in prostate cancer diagnosis and epidemiologic studies and potential solutions also are discussed.

Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease.

Science.gov (United States)

Johnson, Michael R; Shkura, Kirill; Langley, Sarah R; Delahaye-Duriez, Andree; Srivastava, Prashant; Hill, W David; Rackham, Owen J L; Davies, Gail; Harris, Sarah E; Moreno-Moral, Aida; Rotival, Maxime; Speed, Doug; Petrovski, Slavé; Katz, Anaïs; Hayward, Caroline; Porteous, David J; Smith, Blair H; Padmanabhan, Sandosh; Hocking, Lynne J; Starr, John M; Liewald, David C; Visconti, Alessia; Falchi, Mario; Bottolo, Leonardo; Rossetti, Tiziana; Danis, Bénédicte; Mazzuferi, Manuela; Foerch, Patrik; Grote, Alexander; Helmstaedter, Christoph; Becker, Albert J; Kaminski, Rafal M; Deary, Ian J; Petretto, Enrico

2016-02-01

Genetic determinants of cognition are poorly characterized, and their relationship to genes that confer risk for neurodevelopmental disease is unclear. Here we performed a systems-level analysis of genome-wide gene expression data to infer gene-regulatory networks conserved across species and brain regions. Two of these networks, M1 and M3, showed replicable enrichment for common genetic variants underlying healthy human cognitive abilities, including memory. Using exome sequence data from 6,871 trios, we found that M3 genes were also enriched for mutations ascertained from patients with neurodevelopmental disease generally, and intellectual disability and epileptic encephalopathy in particular. M3 consists of 150 genes whose expression is tightly developmentally regulated, but which are collectively poorly annotated for known functional pathways. These results illustrate how systems-level analyses can reveal previously unappreciated relationships between neurodevelopmental disease-associated genes in the developed human brain, and provide empirical support for a convergent gene-regulatory network influencing cognition and neurodevelopmental disease.

Optimization of composite panels using neural networks and genetic algorithms

NARCIS (Netherlands)

Ruijter, W.; Spallino, R.; Warnet, Laurent; de Boer, Andries

2003-01-01

The objective of this paper is to present first results of a running study on optimization of aircraft components (composite panels of a typical vertical tail plane) by using Genetic Algorithms (GA) and Neural Networks (NN). The panels considered are standardized to some extent but still there is a

Hepatitis A virus infection: Epidemiology and genetic diversity

Directory of Open Access Journals (Sweden)

Báez Triana, Paula Andrea

2015-04-01

Full Text Available Hepatitis A virus infection is a global public health problem. The virus has a wide range of distribution and it is the main cause of acute hepatitis transmitted by the enteric route in Latin America. The viral particle is stable under environmental conditions and conserves its infectivity for several weeks, enabling its transmission by contaminated water and food. Worldwide, different epidemiological patterns have been identified, which may change over time by modification of social and economic variables in the population such as vaccination and the improvement of hygiene and primary health conditions. This leaves new populations susceptible to infection. In Latin America the circulation of genotype I and subgenotypes A and B has been described, but more research is needed to provide the knowledge needed to manage the prevention and control plans for the worldwide reduction of the prevalence of infection. For this paper, a literature review was performed on the SciELO, PubMed and ScienceDirect databases under the search terms "Hepatitis A", "Epidemiology," "Seroprevalence" and "Infection." From the results obtained, only papers published in English and Spanish to describe epidemiological and molecular studies of interest in Latin America were included.

Gene expression network reconstruction by convex feature selection when incorporating genetic perturbations.

Directory of Open Access Journals (Sweden)

Benjamin A Logsdon

Full Text Available Cellular gene expression measurements contain regulatory information that can be used to discover novel network relationships. Here, we present a new algorithm for network reconstruction powered by the adaptive lasso, a theoretically and empirically well-behaved method for selecting the regulatory features of a network. Any algorithms designed for network discovery that make use of directed probabilistic graphs require perturbations, produced by either experiments or naturally occurring genetic variation, to successfully infer unique regulatory relationships from gene expression data. Our approach makes use of appropriately selected cis-expression Quantitative Trait Loci (cis-eQTL, which provide a sufficient set of independent perturbations for maximum network resolution. We compare the performance of our network reconstruction algorithm to four other approaches: the PC-algorithm, QTLnet, the QDG algorithm, and the NEO algorithm, all of which have been used to reconstruct directed networks among phenotypes leveraging QTL. We show that the adaptive lasso can outperform these algorithms for networks of ten genes and ten cis-eQTL, and is competitive with the QDG algorithm for networks with thirty genes and thirty cis-eQTL, with rich topologies and hundreds of samples. Using this novel approach, we identify unique sets of directed relationships in Saccharomyces cerevisiae when analyzing genome-wide gene expression data for an intercross between a wild strain and a lab strain. We recover novel putative network relationships between a tyrosine biosynthesis gene (TYR1, and genes involved in endocytosis (RCY1, the spindle checkpoint (BUB2, sulfonate catabolism (JLP1, and cell-cell communication (PRM7. Our algorithm provides a synthesis of feature selection methods and graphical model theory that has the potential to reveal new directed regulatory relationships from the analysis of population level genetic and gene expression data.

Combining neural networks and genetic algorithms for hydrological flow forecasting

Science.gov (United States)

Neruda, Roman; Srejber, Jan; Neruda, Martin; Pascenko, Petr

2010-05-01

We present a neural network approach to rainfall-runoff modeling for small size river basins based on several time series of hourly measured data. Different neural networks are considered for short time runoff predictions (from one to six hours lead time) based on runoff and rainfall data observed in previous time steps. Correlation analysis shows that runoff data, short time rainfall history, and aggregated API values are the most significant data for the prediction. Neural models of multilayer perceptron and radial basis function networks with different numbers of units are used and compared with more traditional linear time series predictors. Out of possible 48 hours of relevant history of all the input variables, the most important ones are selected by means of input filters created by a genetic algorithm. The genetic algorithm works with population of binary encoded vectors defining input selection patterns. Standard genetic operators of two-point crossover, random bit-flipping mutation, and tournament selection were used. The evaluation of objective function of each individual consists of several rounds of building and testing a particular neural network model. The whole procedure is rather computational exacting (taking hours to days on a desktop PC), thus a high-performance mainframe computer has been used for our experiments. Results based on two years worth data from the Ploucnice river in Northern Bohemia suggest that main problems connected with this approach to modeling are ovetraining that can lead to poor generalization, and relatively small number of extreme events which makes it difficult for a model to predict the amplitude of the event. Thus, experiments with both absolute and relative runoff predictions were carried out. In general it can be concluded that the neural models show about 5 per cent improvement in terms of efficiency coefficient over liner models. Multilayer perceptrons with one hidden layer trained by back propagation algorithm and

Mediating Role of the Reward Network in the Relationship between the Dopamine Multilocus Genetic Profile and Depression

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Liang Gong

2017-09-01

Full Text Available Multiple genetic loci in the dopamine (DA pathway have been associated with depression symptoms in patients with major depressive disorder (MDD. However, the neural mechanisms underlying the polygenic effects of the DA pathway on depression remain unclear. We used an imaging genetic approach to investigate the polygenic effects of the DA pathway on the reward network in MDD. Fifty-three patients and 37 cognitively normal (CN subjects were recruited and underwent resting-state functional magnetic resonance imaging (R-fMRI scans. Multivariate linear regression analysis was employed to measure the effects of disease and multilocus genetic profile scores (MGPS on the reward network, which was constructed using the nucleus accumbens (NAc functional connectivity (NAFC network. DA-MGPS was widely associated within the NAFC network, mainly in the inferior frontal cortex, insula, hypothalamus, superior temporal gyrus, and occipital cortex. The pattern of DA-MGPS effects on the fronto-striatal pathway differed in MDD patients compared with CN subjects. More importantly, NAc-putamen connectivity mediates the association between DA MGPS and anxious depression traits in MDD patients. Our findings suggest that the DA multilocus genetic profile makes a considerable contribution to the reward network and anxious depression in MDD patients. These results expand our understanding of the pathophysiology of polygenic effects underlying brain network abnormalities in MDD.

A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems

International Nuclear Information System (INIS)

Sahoo, N.C.; Prasad, K.

2006-01-01

This paper presents a fuzzy genetic approach for reconfiguration of radial distribution systems (RDS) so as to maximize the voltage stability of the network for a specific set of loads. The network reconfiguration involves a mechanism for selection of the best set of branches to be opened, one from each loop, such that the reconfigured RDS possesses desired performance characteristics. This discrete solution space is better handled by the proposed scheme, which maximizes a suitable optimizing function (computed using two different approaches). In the first approach, this function is chosen as the average of a voltage stability index of all the buses in the RDS, while in the second approach, the complete RDS is reduced to a two bus equivalent system and the optimizing function is the voltage stability index of this reduced two bus system. The fuzzy genetic algorithm uses a suitable coding and decoding scheme for maintaining the radial nature of the network at every stage of genetic evolution, and it also uses a fuzzy rule based mutation controller for efficient search of the solution space. This method, tested on 69 bus and 33 bus RDSs, shows promising results for the both approaches. It is also observed that the network losses are reduced when the voltage stability is enhanced by the network reconfiguration

Simulating Visual Learning and Optical Illusions via a Network-Based Genetic Algorithm

Science.gov (United States)

Siu, Theodore; Vivar, Miguel; Shinbrot, Troy

We present a neural network model that uses a genetic algorithm to identify spatial patterns. We show that the model both learns and reproduces common visual patterns and optical illusions. Surprisingly, we find that the illusions generated are a direct consequence of the network architecture used. We discuss the implications of our results and the insights that we gain on how humans fall for optical illusions

Scaling up: human genetics as a Cold War network.

Science.gov (United States)

Lindee, Susan

2014-09-01

In this commentary I explore how the papers here illuminate the processes of collection that have been so central to the history of human genetics since 1945. The development of human population genetics in the Cold War period produced databases and biobanks that have endured into the present, and that continue to be used and debated. In the decades after the bomb, scientists collected and transferred human biological materials and information from populations of interest, and as they moved these biological resources or biosocial resources acquired new meanings and uses. The papers here collate these practices and map their desires and ironies. They explore how a large international network of geneticists, biological anthropologists, virologists and other physicians and scientists interacted with local informants, research subjects and public officials. They also track the networks and standards that mobilized the transfer of information, genealogies, tissue and blood samples. As Joanna Radin suggests here, the massive collections of human biological materials and data were often understood to be resources for an "as-yet-unknown" future. The stories told here contain elements of surveillance, extraction, salvage and eschatology. Copyright © 2014 Elsevier Ltd. All rights reserved.

High Performance Data mining by Genetic Neural Network

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Dadmehr Rahbari

2013-10-01

Full Text Available Data mining in computer science is the process of discovering interesting and useful patterns and relationships in large volumes of data. Most methods for mining problems is based on artificial intelligence algorithms. Neural network optimization based on three basic parameters topology, weights and the learning rate is a powerful method. We introduce optimal method for solving this problem. In this paper genetic algorithm with mutation and crossover operators change the network structure and optimized that. Dataset used for our work is stroke disease with twenty features that optimized number of that achieved by new hybrid algorithm. Result of this work is very well incomparison with other similar method. Low present of error show that our method is our new approach to efficient, high-performance data mining problems is introduced.

Possible implication of the genetic composition of the Lutzomyia longipalpis (Diptera: Psychodidae) populations in the epidemiology of the visceral leishmaniasis.

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Rocha, Leonardo de Souza; Falqueto, Aloisio; Dos Santos, Claudiney Biral; Grimaldi, Gabriel Júnior; Cupolillo, Elisa

2011-09-01

Lutzomyia longipalpis (Diptera: Psychodidae) is the principal vector of American visceral leishmaniasis. Several studies have indicated that the Lu. longipalpis population structure is complex. It has been suggested that genetic divergence caused by genetic drift, selection, or both may affect the vectorial capacity of Lu. longipalpis. However, it remains unclear whether genetic differences among Lu. longipalpis populations are directly implicated in the transmission features of visceral leishmaniasis. We evaluated the genetic composition and the patterns of genetic differentiation among Lu. longipalpis populations collected from regions with different patterns of transmission of visceral leishmaniasis by analyzing the sequence variation in the mitochondrial cytochrome b gene. Furthermore, we investigated the temporal distribution of haplotypes and compared our results with those obtained in a previous study. Our data indicate that there are differences in the haplotype composition and that there has been significant differentiation between the analyzed populations. Our results reveal that measures used to control visceral leishmaniasis might have influenced the genetic composition of the vector population. This finding raises important questions concerning the epidemiology of visceral leishmaniasis, because these differences in the genetic structures among populations of Lu. longipalpis may have implications with respect to their efficiency as vectors for visceral leishmaniasis.

Network clustering analysis using mixture exponential-family random graph models and its application in genetic interaction data.

Science.gov (United States)

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.

Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms

DEFF Research Database (Denmark)

Vinther, Kasper; Green, Torben; Østergaard, Søren

2017-01-01

This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additio...... space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model....

Differential network analysis reveals genetic effects on catalepsy modules.

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Ovidiu D Iancu

Full Text Available We performed short-term bi-directional selective breeding for haloperidol-induced catalepsy, starting from three mouse populations of increasingly complex genetic structure: an F2 intercross, a heterogeneous stock (HS formed by crossing four inbred strains (HS4 and a heterogeneous stock (HS-CC formed from the inbred strain founders of the Collaborative Cross (CC. All three selections were successful, with large differences in haloperidol response emerging within three generations. Using a custom differential network analysis procedure, we found that gene coexpression patterns changed significantly; importantly, a number of these changes were concordant across genetic backgrounds. In contrast, absolute gene-expression changes were modest and not concordant across genetic backgrounds, in spite of the large and similar phenotypic differences. By inferring strain contributions from the parental lines, we are able to identify significant differences in allelic content between the selected lines concurrent with large changes in transcript connectivity. Importantly, this observation implies that genetic polymorphisms can affect transcript and module connectivity without large changes in absolute expression levels. We conclude that, in this case, selective breeding acts at the subnetwork level, with the same modules but not the same transcripts affected across the three selections.

Spinocerebellar ataxias in Venezuela: genetic epidemiology and their most likely ethnic descent.

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Paradisi, Irene; Ikonomu, Vassiliki; Arias, Sergio

2016-03-01

Dominantly inherited ataxias (spinocerebellar ataxias, SCAs) are a genetically heterogeneous group of neurologic diseases characterized by progressive cerebellar and spinal tract degeneration with ataxia and other signs, common to all known subtypes. Several types are relatively frequent worldwide, but in several countries, one specific SCA may show a higher prevalence owing to founder phenomena. In Venezuela, genetic epidemiological features of SCAs have been assessed during the last 30 years; mutations in ATXN1 (SCA1), ATXN2 (SCA2), ATXN3 (SCA3), CACNA1A (SCA6), ATXN7 (SCA7), ATXN8 (SCA8), ATXN10 (SCA10), TBP (SCA17) and ATN1 (dentatorubral pallidoluysian atrophy, DRPLA) loci were searched among 115 independent families. SCA7 was the most frequent subtype (26.6%), followed by SCA3 (25.0%), SCA2 (21.9%), SCA1 (17.2%), SCA10 (4.7%) and DRPLA (3.1%); in 43% of the families, the subtype remained unidentified. SCA7 mutations displayed strong geographic aggregation in two independent founder foci, and SCA1 showed a very remote founder effect for a subset of families. SCA10 families were scattered across the country, but all had an identical in-phase haplotype carried also by Mexican, Brazilian and Sioux patients, supporting a very old common Amerindian origin. Prevalence for dominant SCAs in Venezuela was estimated as 1:25 000 nuclear families, provenances of which are either Caucasoid, African or Amerindian.

Dynamic Load Balanced Clustering using Elitism based Random Immigrant Genetic Approach for Wireless Sensor Networks

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K. Mohaideen Pitchai

2017-07-01

Full Text Available Wireless Sensor Network (WSN consists of a large number of small sensors with restricted energy. Prolonged network lifespan, scalability, node mobility and load balancing are important needs for several WSN applications. Clustering the sensor nodes is an efficient technique to reach these goals. WSN have the characteristics of topology dynamics because of factors like energy conservation and node movement that leads to Dynamic Load Balanced Clustering Problem (DLBCP. In this paper, Elitism based Random Immigrant Genetic Approach (ERIGA is proposed to solve DLBCP which adapts to topology dynamics. ERIGA uses the dynamic Genetic Algorithm (GA components for solving the DLBCP. The performance of load balanced clustering process is enhanced with the help of this dynamic GA. As a result, the ERIGA achieves to elect suitable cluster heads which balances the network load and increases the lifespan of the network.

Parkinson’s Disease in Sub-Saharan Africa: A Review of Epidemiology, Genetics and Access to Care

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Bandmann, Oliver; Walker, Richard

2018-01-01

A low prevalence of Parkinson’s disease (PD) has been reported in the Sub-Saharan Africa (SSA) region. The genetic causes and clinical features of PD in this region have been poorly described. Very few reports have examined the availability and access to evidence-based quality care for people living with PD in this region. We reviewed all publications focusing on idiopathic PD from SSA published up to May 2016 and observed a prevalence of PD ranging from 7/100,000 in Ethiopia to 67/100,000 in Nigeria. The most recent community-based study reported a mean age at onset of 69.4 years. The infrequent occurrence of mutations in established PD genes was also observed in the region. Treatments were non-existent or at best irregular. Additionally, there is a lack of well-trained medical personnel and multidisciplinary teams in most countries in this region. Drugs for treating PD are either not available or unaffordable. Large-scale genetic and epidemiological studies are therefore needed in SSA to provide further insights into the roles of genetics and other etiological factors in the pathogenesis of PD. The quality of care also requires urgent improvement to meet the basic level of care required by PD patients. PMID:29860783

A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems

Energy Technology Data Exchange (ETDEWEB)

Sahoo, N.C. [Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, 75450 Melaka (Malaysia); Prasad, K. [Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, 75450 Melaka (Malaysia)

2006-11-15

This paper presents a fuzzy genetic approach for reconfiguration of radial distribution systems (RDS) so as to maximize the voltage stability of the network for a specific set of loads. The network reconfiguration involves a mechanism for selection of the best set of branches to be opened, one from each loop, such that the reconfigured RDS possesses desired performance characteristics. This discrete solution space is better handled by the proposed scheme, which maximizes a suitable optimizing function (computed using two different approaches). In the first approach, this function is chosen as the average of a voltage stability index of all the buses in the RDS, while in the second approach, the complete RDS is reduced to a two bus equivalent system and the optimizing function is the voltage stability index of this reduced two bus system. The fuzzy genetic algorithm uses a suitable coding and decoding scheme for maintaining the radial nature of the network at every stage of genetic evolution, and it also uses a fuzzy rule based mutation controller for efficient search of the solution space. This method, tested on 69 bus and 33 bus RDSs, shows promising results for the both approaches. It is also observed that the network losses are reduced when the voltage stability is enhanced by the network reconfiguration. (author)

The Changing Epidemiology of Autism Spectrum Disorders.

Science.gov (United States)

Lyall, Kristen; Croen, Lisa; Daniels, Julie; Fallin, M Daniele; Ladd-Acosta, Christine; Lee, Brian K; Park, Bo Y; Snyder, Nathaniel W; Schendel, Diana; Volk, Heather; Windham, Gayle C; Newschaffer, Craig

2017-03-20

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with lifelong impacts. Genetic and environmental factors contribute to ASD etiology, which remains incompletely understood. Research on ASD epidemiology has made significant advances in the past decade. Current prevalence is estimated to be at least 1.5% in developed countries, with recent increases primarily among those without comorbid intellectual disability. Genetic studies have identified a number of rare de novo mutations and gained footing in the areas of polygenic risk, epigenetics, and gene-by-environment interaction. Epidemiologic investigations focused on nongenetic factors have established advanced parental age and preterm birth as ASD risk factors, indicated that prenatal exposure to air pollution and short interpregnancy interval are potential risk factors, and suggested the need for further exploration of certain prenatal nutrients, metabolic conditions, and exposure to endocrine-disrupting chemicals. We discuss future challenges and goals for ASD epidemiology as well as public health implications.

Biomarkers in molecular epidemiology study of oral squamous cell carcinoma in the era of precision medicine

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Qing-Hao Zhu

2017-01-01

Full Text Available Oral cancer, which occurs in the mouth, lips, and tongue, is a multifactorial disease whose etiology involves environment, genetic, and epigenetic factors. Tobacco use and alcohol consumption are regarded as the primary risk factors for oral squamous cell carcinoma (OSCC, and betel use, other chemicals, radiation, environmental, and genetics are reported as relevant risk factors for oral carcinogenesis. The human papillomavirus infection is an independent risk factor. Traditional epidemiology studies have revealed that environmental carcinogens are risk factors for OSCC. Molecular epidemiology studies have revealed that the susceptibility to OSCC is influenced by both environmental and genetic risk factors. However, the details and mechanisms of risk factors involved in OSCC are unclear. Advanced methods and techniques used in human genome studies provide great opportunities for researchers to explore and identify (a the details of such risk factors and (b genetic susceptibility involved in OSCC. Human genome epidemiology is a new branch of epidemiology, which leads the epidemiology study from the molecular epidemiology era into the era of genome-wide association study. In the era of precision medicine, molecular epidemiology studies should focus on biomarkers for cancer genomics and their potential utility in clinical practice. Here, we briefly reviewed several molecular epidemiology studies of OSCC, focusing on biomarkers as valuable utility in risk assessment, clinical screening, diagnosis, and prognosis prediction of OSCC in the era of precision medicine.

The French Chronic Kidney Disease-Renal Epidemiology and Information Network (CKD-REIN) cohort study.

Science.gov (United States)

Stengel, Bénédicte; Combe, Christian; Jacquelinet, Christian; Briançon, Serge; Fouque, Denis; Laville, Maurice; Frimat, Luc; Pascal, Christophe; Herpe, Yves-Edouard; Deleuze, Jean-François; Schanstra, Joost; Pisoni, Ron L; Robinson, Bruce M; Massy, Ziad A

2014-08-01

While much has been learned about the epidemiology and treatment of end-stage renal disease (ESRD) in the last 30 years, chronic kidney disease (CKD) before the end-stage has been less investigated. Not enough is known about factors associated with CKD progression and complications, as well as its transition to ESRD. We designed the CKD-renal epidemiology and information network (REIN) cohort to provide a research platform to address these key questions and to assess clinical practices and costs in patients with moderate or advanced CKD. A total of 46 clinic sites and 4 renal care networks participate in the cohort. A stratified selection of clinic sites yields a sample that represents a diversity of settings, e.g. geographic region, and public versus for-profit and non-for-profit private clinics. In each site, 60-90 patients with CKD are enrolled at a routine clinic visit during a 12-month enrolment phase: 3600 total, including 1800 with Stage 3 and 1800 with Stage 4 CKD. Follow-up will continue for 5 years, including after initiation of renal replacement therapy. Data will be collected from medical records at inclusion and at yearly intervals, as well as from self-administered patient questionnaires and provider-level questionnaires. Patients will also be interviewed at baseline, and at 1, 3 and 5 years. Healthcare costs will also be determined. Blood and urine samples will be collected and stored for future studies on all patients at enrolment and at study end, and at 1 and 3 years in a subsample of 1200. The CKD-REIN cohort will serve to improve our understanding of the biological, clinical and healthcare system determinants associated with CKD progression and adverse outcomes as well as of international variations in collaboration with the CKD Outcome and Practice Pattern Study (CKDopps). It will foster CKD epidemiology and outcomes research and provide evidence to improve the health and quality of life of patients with CKD and the performances of the

Computational Genetic Regulatory Networks Evolvable, Self-organizing Systems

CERN Document Server

Knabe, Johannes F

2013-01-01

Genetic Regulatory Networks (GRNs) in biological organisms are primary engines for cells to enact their engagements with environments, via incessant, continually active coupling. In differentiated multicellular organisms, tremendous complexity has arisen in the course of evolution of life on earth. Engineering and science have so far achieved no working system that can compare with this complexity, depth and scope of organization. Abstracting the dynamics of genetic regulatory control to a computational framework in which artificial GRNs in artificial simulated cells differentiate while connected in a changing topology, it is possible to apply Darwinian evolution in silico to study the capacity of such developmental/differentiated GRNs to evolve. In this volume an evolutionary GRN paradigm is investigated for its evolvability and robustness in models of biological clocks, in simple differentiated multicellularity, and in evolving artificial developing 'organisms' which grow and express an ontogeny starting fr...

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

Directory of Open Access Journals (Sweden)

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.

Environmental Noise, Genetic Diversity and the Evolution of Evolvability and Robustness in Model Gene Networks

Science.gov (United States)

Steiner, Christopher F.

2012-01-01

The ability of organisms to adapt and persist in the face of environmental change is accepted as a fundamental feature of natural systems. More contentious is whether the capacity of organisms to adapt (or “evolvability”) can itself evolve and the mechanisms underlying such responses. Using model gene networks, I provide evidence that evolvability emerges more readily when populations experience positively autocorrelated environmental noise (red noise) compared to populations in stable or randomly varying (white noise) environments. Evolvability was correlated with increasing genetic robustness to effects on network viability and decreasing robustness to effects on phenotypic expression; populations whose networks displayed greater viability robustness and lower phenotypic robustness produced more additive genetic variation and adapted more rapidly in novel environments. Patterns of selection for robustness varied antagonistically with epistatic effects of mutations on viability and phenotypic expression, suggesting that trade-offs between these properties may constrain their evolutionary responses. Evolution of evolvability and robustness was stronger in sexual populations compared to asexual populations indicating that enhanced genetic variation under fluctuating selection combined with recombination load is a primary driver of the emergence of evolvability. These results provide insight into the mechanisms potentially underlying rapid adaptation as well as the environmental conditions that drive the evolution of genetic interactions. PMID:23284934

General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns

Energy Technology Data Exchange (ETDEWEB)

Bornholdt, S. [Heidelberg Univ., (Germany). Inst., fuer Theoretische Physik; Graudenz, D. [Lawrence Berkeley Lab., CA (United States)

1993-07-01

A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.

General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns

International Nuclear Information System (INIS)

Bornholdt, S.

1993-07-01

A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback

Prediction of Aerodynamic Coefficients for Wind Tunnel Data using a Genetic Algorithm Optimized Neural Network

Science.gov (United States)

Rajkumar, T.; Aragon, Cecilia; Bardina, Jorge; Britten, Roy

2002-01-01

A fast, reliable way of predicting aerodynamic coefficients is produced using a neural network optimized by a genetic algorithm. Basic aerodynamic coefficients (e.g. lift, drag, pitching moment) are modelled as functions of angle of attack and Mach number. The neural network is first trained on a relatively rich set of data from wind tunnel tests of numerical simulations to learn an overall model. Most of the aerodynamic parameters can be well-fitted using polynomial functions. A new set of data, which can be relatively sparse, is then supplied to the network to produce a new model consistent with the previous model and the new data. Because the new model interpolates realistically between the sparse test data points, it is suitable for use in piloted simulations. The genetic algorithm is used to choose a neural network architecture to give best results, avoiding over-and under-fitting of the test data.

Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms.

Science.gov (United States)

Ferentinos, Konstantinos P

2005-09-01

Two neural network (NN) applications in the field of biological engineering are developed, designed and parameterized by an evolutionary method based on the evolutionary process of genetic algorithms. The developed systems are a fault detection NN model and a predictive modeling NN system. An indirect or 'weak specification' representation was used for the encoding of NN topologies and training parameters into genes of the genetic algorithm (GA). Some a priori knowledge of the demands in network topology for specific application cases is required by this approach, so that the infinite search space of the problem is limited to some reasonable degree. Both one-hidden-layer and two-hidden-layer network architectures were explored by the GA. Except for the network architecture, each gene of the GA also encoded the type of activation functions in both hidden and output nodes of the NN and the type of minimization algorithm that was used by the backpropagation algorithm for the training of the NN. Both models achieved satisfactory performance, while the GA system proved to be a powerful tool that can successfully replace the problematic trial-and-error approach that is usually used for these tasks.

Genetic dissection of acute ethanol responsive gene networks in prefrontal cortex: functional and mechanistic implications.

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Aaron R Wolen

Full Text Available Individual differences in initial sensitivity to ethanol are strongly related to the heritable risk of alcoholism in humans. To elucidate key molecular networks that modulate ethanol sensitivity we performed the first systems genetics analysis of ethanol-responsive gene expression in brain regions of the mesocorticolimbic reward circuit (prefrontal cortex, nucleus accumbens, and ventral midbrain across a highly diverse family of 27 isogenic mouse strains (BXD panel before and after treatment with ethanol.Acute ethanol altered the expression of ~2,750 genes in one or more regions and 400 transcripts were jointly modulated in all three. Ethanol-responsive gene networks were extracted with a powerful graph theoretical method that efficiently summarized ethanol's effects. These networks correlated with acute behavioral responses to ethanol and other drugs of abuse. As predicted, networks were heavily populated by genes controlling synaptic transmission and neuroplasticity. Several of the most densely interconnected network hubs, including Kcnma1 and Gsk3β, are known to influence behavioral or physiological responses to ethanol, validating our overall approach. Other major hub genes like Grm3, Pten and Nrg3 represent novel targets of ethanol effects. Networks were under strong genetic control by variants that we mapped to a small number of chromosomal loci. Using a novel combination of genetic, bioinformatic and network-based approaches, we identified high priority cis-regulatory candidate genes, including Scn1b, Gria1, Sncb and Nell2.The ethanol-responsive gene networks identified here represent a previously uncharacterized intermediate phenotype between DNA variation and ethanol sensitivity in mice. Networks involved in synaptic transmission were strongly regulated by ethanol and could contribute to behavioral plasticity seen with chronic ethanol. Our novel finding that hub genes and a small number of loci exert major influence over the ethanol

[Genetic, epidemiologic and clinical study of familial prostate cancer].

Science.gov (United States)

Valéri, Antoine

2002-01-01

Prostate cancer (CaP) is the most frequent cancer among men over 50 and its frequency increases with age. It has become a significant public health problem due to the ageing population. Epidemiologists report familial aggregation in 15 to 25% of cases and inherited susceptibility with autosomal dominant or X-linked model in 5 to 10% of cases. Clinical and biological features of familial CaP remain controversial. To perform: (1) Genetic study of familial Cap (mapping of susceptibility genes), (2) epidemiologic study (prevalence, associated cancers in the genealogy, model of transmission), and clinical study of familial CaP. (I) conducting a nationwide family collection (ProGène study) with 2+ CaP we have performed a genomewide linkage analysis and identified a predisposing locus on 1q42.2-43 named PCaP (Predisposing to Cancer of the Prostate); (II) conducting a systematic genealogic analysis of 691 CaP followed up in 3 University departments of urology (Hospitals of Brest, Paris St Louis and Nancy) we have observed: (1) 14.2% of familial and 3.6% of hereditary CaP, (2) a higher risk of breast cancer in first degree relatives of probands (CaP+) in familial CaP than in sporadic CaP and in early onset CaP (< 55 years) when compared with late onset CaP ([dG]75 years), (3) an autosomal dominant model with brother-brother dependance), (4) the lack of specific clinical or biological feature (except for early onset) in hereditary CaP when compared with sporadic CaP. (1) The mapping of a susceptibility locus will permit the cloning of a predisposing gene on 1q42.2-43, offer the possibility of genetic screening in families at risk and permit genotype/phenotype correlation studies; (2) the transmission model will improve parameteric linkage studies; (3) the lack of distinct specific clinical patterns suggest diagnostic and follow up modalities for familial and hereditary CaP similar to sporadic cancer while encouraging early screening of families at risk, given the earlier

Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks.

Science.gov (United States)

Cheng, Long; Hou, Zeng-Guang; Lin, Yingzi; Tan, Min; Zhang, Wenjun Chris; Wu, Fang-Xiang

2011-05-01

A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach.

Systems genetics of obesity in an F2 pig model by genome-wide association, genetic network and pathway analyses

DEFF Research Database (Denmark)

Kogelman, Lisette; Pant, Sameer Dinkar; Fredholm, Merete

2014-01-01

.g. metabolic processes. WISH networks based on genotypic correlations allowed further identification of various gene ontology terms and pathways related to obesity and related traits, which were not identified by the GWA study. In conclusion, this is the first study to develop a (genetic) obesity index...... investigations focusing on single genetic variants have achieved limited success, and the importance of including genetic interactions is becoming evident. Here, the aim was to perform an integrative genomic analysis in an F2 pig resource population that was constructed with an aim to maximize genetic variation...... of obesity-related phenotypes and genotyped using the 60K SNP chip. Firstly, Genome Wide Association (GWA) analysis was performed on the Obesity Index to locate candidate genomic regions that were further validated using combined Linkage Disequilibrium Linkage Analysis and investigated by evaluation...

Genetic and Molecular Epidemiological Characterization of a Novel Adenovirus in Antarctic Penguins Collected between 2008 and 2013.

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Sook-Young Lee

Full Text Available Antarctica is considered a relatively uncontaminated region with regard to the infectious diseases because of its extreme environment, and isolated geography. For the genetic characterization and molecular epidemiology of the newly found penguin adenovirus in Antarctica, entire genome sequencing and annual survey of penguin adenovirus were conducted. The entire genome sequences of penguin adenoviruses were completed for two Chinstrap penguins (Pygoscelis antarctica and two Gentoo penguins (Pygoscelis papua. The whole genome lengths and G+C content of penguin adenoviruses were found to be 24,630-24,662 bp and 35.5-35.6%, respectively. Notably, the presence of putative sialidase gene was not identified in penguin adenoviruses by Rapid Amplification of cDNA Ends (RACE-PCR as well as consensus specific PCR. The penguin adenoviruses were demonstrated to be a new species within the genus Siadenovirus, with a distance of 29.9-39.3% (amino acid, 32.1-47.9% in DNA polymerase gene, and showed the closest relationship with turkey adenovirus 3 (TAdV-3 in phylogenetic analysis. During the 2008-2013 study period, the penguin adenoviruses were annually detected in 22 of 78 penguins (28.2%, and the molecular epidemiological study of the penguin adenovirus indicates a predominant infection in Chinstrap penguin population (12/30, 40%. Interestingly, the genome of penguin adenovirus could be detected in several internal samples, except the lymph node and brain. In conclusion, an analysis of the entire adenoviral genomes from Antarctic penguins was conducted, and the penguin adenoviruses, containing unique genetic character, were identified as a new species within the genus Siadenovirus. Moreover, it was annually detected in Antarctic penguins, suggesting its circulation within the penguin population.

Complete genome sequence of hypervirulent and outbreak-associated Acinetobacter baumannii strain LAC-4: epidemiology, resistance genetic determinants and potential virulence factors

Science.gov (United States)

Ou, Hong-Yu; Kuang, Shan N.; He, Xinyi; Molgora, Brenda M.; Ewing, Peter J.; Deng, Zixin; Osby, Melanie; Chen, Wangxue; Xu, H. Howard

2015-01-01

Acinetobacter baumannii is an important human pathogen due to its multi-drug resistance. In this study, the genome of an ST10 outbreak A. baumannii isolate LAC-4 was completely sequenced to better understand its epidemiology, antibiotic resistance genetic determinants and potential virulence factors. Compared with 20 other complete genomes of A. baumannii, LAC-4 genome harbors at least 12 copies of five distinct insertion sequences. It contains 12 and 14 copies of two novel IS elements, ISAba25 and ISAba26, respectively. Additionally, three novel composite transposons were identified: Tn6250, Tn6251 and Tn6252, two of which contain resistance genes. The antibiotic resistance genetic determinants on the LAC-4 genome correlate well with observed antimicrobial susceptibility patterns. Moreover, twelve genomic islands (GI) were identified in LAC-4 genome. Among them, the 33.4-kb GI12 contains a large number of genes which constitute the K (capsule) locus. LAC-4 harbors several unique putative virulence factor loci. Furthermore, LAC-4 and all 19 other outbreak isolates were found to harbor a heme oxygenase gene (hemO)-containing gene cluster. The sequencing of the first complete genome of an ST10 A. baumannii clinical strain should accelerate our understanding of the epidemiology, mechanisms of resistance and virulence of A. baumannii. PMID:25728466

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

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Chandra Nagasuma R

2009-02-01

Full Text Available Abstract Background A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN from transcript profiling data. Results The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting problem and solved finally by formulating a Linear Program (LP. A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known

A new approach to self-organizing fuzzy polynomial neural networks guided by genetic optimization

International Nuclear Information System (INIS)

Oh, Sung-Kwun; Pedrycz, Witold

2005-01-01

In this study, we introduce a new topology of Fuzzy Polynomial Neural Networks (FPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology. The underlying methodology involves mechanisms of genetic optimization, especially genetic algorithms (GAs). Let us recall that the design of the 'conventional' FPNNs uses an extended Group Method of Data Handling (GMDH) and exploits a fixed fuzzy inference type located at each FPN of the FPNN as well as considers a fixed number of input nodes at FPNs (or nodes) located in each layer. The proposed FPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. The structural optimization is realized via GAs whereas in the case of the parametric optimization we proceed with a standard least square method based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. The performance of the proposed gFPNN is quantified through experimentation that exploits standard data already being used in fuzzy modeling. The results reveal superiority of the proposed networks over the existing fuzzy and neural models

Effects of smoking on the genetic risk of obesity: the population architecture using genomics and epidemiology study

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Fesinmeyer Megan D

2013-01-01

Full Text Available Abstract Background Although smoking behavior is known to affect body mass index (BMI, the potential for smoking to influence genetic associations with BMI is largely unexplored. Methods As part of the ‘Population Architecture using Genomics and Epidemiology (PAGE’ Consortium, we investigated interaction between genetic risk factors associated with BMI and smoking for 10 single nucleotide polymorphisms (SNPs previously identified in genome-wide association studies. We included 6 studies with a total of 56,466 subjects (16,750 African Americans (AA and 39,716 European Americans (EA. We assessed effect modification by testing an interaction term for each SNP and smoking (current vs. former/never in the linear regression and by stratified analyses. Results We did not observe strong evidence for interactions and only observed two interactions with p-values TMEM18, the risk allele (C was associated with BMI only among AA females who were former/never smokers (β = 0.018, p = 0.002, vs. current smokers (β = 0.001, p = 0.95, pinteraction = 0.10. For rs9939609/FTO, the A allele was more strongly associated with BMI among current smoker EA females (β = 0.017, p = 3.5x10-5, vs. former/never smokers (β = 0.006, p = 0.05, pinteraction = 0.08. Conclusions These analyses provide limited evidence that smoking status may modify genetic effects of previously identified genetic risk factors for BMI. Larger studies are needed to follow up our results. Clinical Trial Registration NCT00000611

Criticality is an emergent property of genetic networks that exhibit evolvability.

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Christian Torres-Sosa

Full Text Available Accumulating experimental evidence suggests that the gene regulatory networks of living organisms operate in the critical phase, namely, at the transition between ordered and chaotic dynamics. Such critical dynamics of the network permits the coexistence of robustness and flexibility which are necessary to ensure homeostatic stability (of a given phenotype while allowing for switching between multiple phenotypes (network states as occurs in development and in response to environmental change. However, the mechanisms through which genetic networks evolve such critical behavior have remained elusive. Here we present an evolutionary model in which criticality naturally emerges from the need to balance between the two essential components of evolvability: phenotype conservation and phenotype innovation under mutations. We simulated the Darwinian evolution of random Boolean networks that mutate gene regulatory interactions and grow by gene duplication. The mutating networks were subjected to selection for networks that both (i preserve all the already acquired phenotypes (dynamical attractor states and (ii generate new ones. Our results show that this interplay between extending the phenotypic landscape (innovation while conserving the existing phenotypes (conservation suffices to cause the evolution of all the networks in a population towards criticality. Furthermore, the networks produced by this evolutionary process exhibit structures with hubs (global regulators similar to the observed topology of real gene regulatory networks. Thus, dynamical criticality and certain elementary topological properties of gene regulatory networks can emerge as a byproduct of the evolvability of the phenotypic landscape.

A genetic algorithm solution for the operation of green LTE networks with energy and environment considerations

KAUST Repository

Ghazzai, Hakim; Yaacoub, Elias E.; Alouini, Mohamed-Slim; Abu-Dayya, Adnan A.

2012-01-01

, as additional power sources in smart grids, becomes a real challenge to network operators to reduce power costs. In this paper, we propose a method based on genetic algorithms that decreases the energy consumption of a Long-Term Evolution (LTE) cellular network

The genetic network of greater sage-grouse: Range-wide identification of keystone hubs of connectivity

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Todd B. Cross; Michael K. Schwartz; David E. Naugle; Brad C. Fedy; Jeffrey R. Row; Sara J. Oyler-McCance

2018-01-01

Genetic networks can characterize complex genetic relationships among groups of individuals, which can be used to rank nodes most important to the overall connectivity of the system. Ranking allows scarce resources to be guided toward nodes integral to connectivity. The greater sage-grouse (Centrocercus urophasianus) is a species of conservation concern that breeds on...

Molecular epidemiology, and possible real-world applications in breast cancer.

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Ito, Hidemi; Matsuo, Keitaro

2016-01-01

Gene-environment interaction, a key idea in molecular epidemiology, has enabled the development of personalized medicine. This concept includes personalized prevention. While genome-wide association studies have identified a number of genetic susceptibility loci in breast cancer risk, however, the application of this knowledge to practical prevention is still underway. Here, we briefly review the history of molecular epidemiology and its progress in breast cancer epidemiology. We then introduce our experience with the trial combination of GWAS-identified loci and well-established lifestyle and reproductive risk factors in the risk prediction of breast cancer. Finally, we report our exploration of the cumulative risk of breast cancer based on this risk prediction model as a potential tool for individual risk communication, including genetic risk factors and gene-environment interaction with obesity.

Mapping epidemiology's past to inform its future: metaknowledge analysis of epidemiologic topics in leading journals, 1974-2013.

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Trinquart, Ludovic; Galea, Sandro

2015-07-15

An empiric perspective on what epidemiology has studied over time might inform discussions about future directions for the discipline. We aimed to identify the main areas of epidemiologic inquiry and determine how they evolved over time in 5 high-impact epidemiologic journals. We analyzed the titles and abstracts of 20,895 articles that were published between 1974 and 2013. In 5 time periods that reflected approximately equal numbers of articles, we identified the main topics by clustering terms based on co-occurrence. Infectious disease and cardiovascular disease epidemiology were the prevailing topics over the 5 periods. Cancer epidemiology was a major topic from 1974 to 2001 but disappeared thereafter. Nutritional epidemiology gained relative importance from 1974 to 2013. Environmental epidemiology appeared during 1996-2001 and continued to be important, whereas 2 clusters related to methodology and meta-analysis in genetics appeared during 2008-2013. Several areas of epidemiology, including injury or psychiatric epidemiology, did not make an appearance as major topics at any time. In an ancillary analysis of 6 high-impact general medicine journals, we found patterns of epidemiologic articles that were overall consistent with the findings in epidemiologic journals. This metaknowledge investigation allowed identification of the dominant topics in and conversely those that were absent from 5 major epidemiologic journals. We discuss implications for the field. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Combined Simulated Annealing and Genetic Algorithm Approach to Bus Network Design

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Liu, Li; Olszewski, Piotr; Goh, Pong-Chai

A new method - combined simulated annealing (SA) and genetic algorithm (GA) approach is proposed to solve the problem of bus route design and frequency setting for a given road network with fixed bus stop locations and fixed travel demand. The method involves two steps: a set of candidate routes is generated first and then the best subset of these routes is selected by the combined SA and GA procedure. SA is the main process to search for a better solution to minimize the total system cost, comprising user and operator costs. GA is used as a sub-process to generate new solutions. Bus demand assignment on two alternative paths is performed at the solution evaluation stage. The method was implemented on four theoretical grid networks of different size and a benchmark network. Several GA operators (crossover and mutation) were utilized and tested for their effectiveness. The results show that the proposed method can efficiently converge to the optimal solution on a small network but computation time increases significantly with network size. The method can also be used for other transport operation management problems.

Molecular markers in the epidemiology and diagnosis of coccidioidomycosis.

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Duarte-Escalante, Esperanza; Frías-De-León, María Guadalupe; Zúñiga, Gerardo; Martínez-Herrera, Erick; Acosta-Altamirano, Gustavo; Reyes-Montes, María Del Rocío

2014-01-01

The prevalence of coccidioidomycosis in endemic areas has been observed to increase daily. To understand the causes of the spread of the disease and design strategies for fungal detection in clinical and environmental samples, scientists have resorted to molecular tools that allow fungal detection in a natural environment, reliable identification in clinical cases and the study of biological characteristics, such as reproductive and genetic structure, demographic history and diversification. We conducted a review of the most important molecular markers in the epidemiology of Coccidioides spp. and the diagnosis of coccidioidomycosis. A literature search was performed for scientific publications concerning the application of molecular tools for the epidemiology and diagnosis of coccidioidomycosis. The use of molecular markers in the epidemiological study and diagnosis of coccidioidomycosis has allowed for the typing of Coccidioides spp. isolates, improved understanding of their mode of reproduction, genetic variation and speciation and resulted in the development specific, rapid and sensitive strategies for detecting the fungus in environmental and clinical samples. Molecular markers have revealed genetic variability in Coccidioides spp. This finding influences changes in the epidemiology of coccidioidomycosis, such as the emergence of more virulent or antifungal resistant genotypes. Furthermore, the molecular markers currently used to identify Coccidioides immitis and Coccidioides posadasii are specific and sensitive. However, they must be validated to determine their application in diagnosis. This manuscript is part of the series of works presented at the "V International Workshop: Molecular genetic approaches to the study of human pathogenic fungi" (Oaxaca, Mexico, 2012). Copyright © 2013 Revista Iberoamericana de Micología. Published by Elsevier Espana. All rights reserved.

Between “design” and “bricolage”: Genetic networks, levels of selection, and adaptive evolution

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Wilkins, Adam S.

2007-01-01

The extent to which “developmental constraints” in complex organisms restrict evolutionary directions remains contentious. Yet, other forms of internal constraint, which have received less attention, may also exist. It will be argued here that a set of partial constraints below the level of phenotypes, those involving genes and molecules, influences and channels the set of possible evolutionary trajectories. At the top-most organizational level there are the genetic network modules, whose operations directly underlie complex morphological traits. The properties of these network modules, however, have themselves been set by the evolutionary history of the component genes and their interactions. Characterization of the components, structures, and operational dynamics of specific genetic networks should lead to a better understanding not only of the morphological traits they underlie but of the biases that influence the directions of evolutionary change. Furthermore, such knowledge may permit assessment of the relative degrees of probability of short evolutionary trajectories, those on the microevolutionary scale. In effect, a “network perspective” may help transform evolutionary biology into a scientific enterprise with greater predictive capability than it has hitherto possessed. PMID:17494754

Network reconfiguration for loss reduction in electrical distribution system using genetic algorithm

International Nuclear Information System (INIS)

Adail, A.S.A.A.

2012-01-01

Distribution system is a critical links between the utility and the nuclear installation. During feeding electricity to that installation there are power losses. The quality of the network depends on the reduction of these losses. Distribution system which feeds the nuclear installation must have a higher quality power. For example, in Inshas site, electrical power is supplied to it from two incoming feeders (one from new abu-zabal substation and the other from old abu-zabal substation). Each feeder is designed to carry the full load, while the operator preferred to connect with a new abu-zabal substation, which has a good power quality. Bad power quality affects directly the nuclear reactor and has a negative impact on the installed sensitive equipment's of the operation. The thesis is Studying the electrical losses in a distribution system (causes and effected factors), feeder reconfiguration methods, and applying of genetic algorithm in an electric distribution power system. In the end, this study proposes an optimization technique based on genetic algorithms for distribution network reconfiguration to reduce the network losses to minimum. The proposed method is applied to IEEE test network; that contain 3 feeders and 16 nodes. The technique is applied through two groups, distribution have general loads, and nuclear loads. In the groups the technique applied to seven cases at normal operation state, system fault condition as well as different loads conditions. Simulated results are drawn to show the accuracy of the technique.

[Multiple sclerosis epidemiological situation update: pertinence and set-up of a population based registry of new cases in Catalonia].

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Otero, S; Batlle, J; Bonaventura, I; Brieva, Ll; Bufill, E; Cano, A; Carmona, O; Escartín, A; Marco, M; Moral, E; Munteis, E; Nos, C; Pericot, I; Perkal, H; Ramió-Torrentà, Ll; Ramo-Tello, C; Saiz, A; Sastre-Garriga, J; Tintoré, M; Vaqué, J; Montalban, X

2010-05-16

The first epidemiological studies on multiple sclerosis (MS) around the world pictured a north to south latitudinal gradient that led to the first genetic and environmental pathogenic hypothesis. MS incidence seems to be increasing during the past 20 years based on recent data from prospective studies performed in Europe, America and Asia. This phenomenon could be explained by a better case ascertainment as well as a change in causal factors. The few prospective studies in our area together with the increase in the disease in other regions, justifies an epidemiological MS project in order to describe the incidence and temporal trends of MS. A prospective multicenter MS registry has been established according to the actual requirements of an epidemiological surveillance system. Case definition is based on the fulfillment of the McDonald diagnostic criteria. The registry setting is the geographical area of Cataluna (northeastern Spain), using a wide network of hospitals specialized in MS management. Recent epidemiological studies have described an increase in MS incidence. In order to contrast this finding in our area, we consider appropriate to set up a population based registry.

A UV-Induced Genetic Network Links the RSC Complex to Nucleotide Excision Repair and Shows Dose-Dependent Rewiring

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Rohith Srivas

2013-12-01

Full Text Available Efficient repair of UV-induced DNA damage requires the precise coordination of nucleotide excision repair (NER with numerous other biological processes. To map this crosstalk, we generated a differential genetic interaction map centered on quantitative growth measurements of >45,000 double mutants before and after different doses of UV radiation. Integration of genetic data with physical interaction networks identified a global map of 89 UV-induced functional interactions among 62 protein complexes, including a number of links between the RSC complex and several NER factors. We show that RSC is recruited to both silenced and transcribed loci following UV damage where it facilitates efficient repair by promoting nucleosome remodeling. Finally, a comparison of the response to high versus low levels of UV shows that the degree of genetic rewiring correlates with dose of UV and reveals a network of dose-specific interactions. This study makes available a large resource of UV-induced interactions, and it illustrates a methodology for identifying dose-dependent interactions based on quantitative shifts in genetic networks.

Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering

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Dhaeseleer, Patrik; Liang, Shoudan; Somogyi, Roland

2000-01-01

Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.

The effect of ethnicity and genetic ancestry on the epidemiology, clinical features and outcome of systemic lupus erythematosus.

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Lewis, Myles J; Jawad, Ali S

2017-04-01

In this in-depth review, we examine the worldwide epidemiology of SLE and summarize current knowledge on the influence of race/ethnicity on clinical manifestations, disease activity, damage accumulation and outcome in SLE. Susceptibility to SLE has a strong genetic component, and trans-ancestral genetic studies have revealed a substantial commonality of shared genetic risk variants across different genetic ancestries that predispose to the development of SLE. The highest increased risk of developing SLE is observed in black individuals (incidence 5- to 9-fold increased, prevalence 2- to 3-fold increased), with an increased risk also observed in South Asians, East Asians and other non-white groups, compared with white individuals. Black, East Asian, South Asian and Hispanic individuals with SLE tend to develop more severe disease with a greater number of manifestations and accumulate damage from lupus more rapidly. Increased genetic risk burden in these populations, associated with increased autoantibody reactivity in non-white individuals with SLE, may explain the more severe lupus phenotype. Even after taking into account socio-economic factors, race/ethnicity remains a key determinant of poor outcome, such as end-stage renal failure and mortality, in SLE. Community measures to expedite diagnosis through increased awareness in at-risk racial/ethnic populations and ethnically personalized treatment algorithms may help in future to improve long-term outcomes in SLE. © The Author 2016. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

An automatic method to generate domain-specific investigator networks using PubMed abstracts

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Gwinn Marta

2007-06-01

Full Text Available Abstract Background Collaboration among investigators has become critical to scientific research. This includes ad hoc collaboration established through personal contacts as well as formal consortia established by funding agencies. Continued growth in online resources for scientific research and communication has promoted the development of highly networked research communities. Extending these networks globally requires identifying additional investigators in a given domain, profiling their research interests, and collecting current contact information. We present a novel strategy for building investigator networks dynamically and producing detailed investigator profiles using data available in PubMed abstracts. Results We developed a novel strategy to obtain detailed investigator information by automatically parsing the affiliation string in PubMed records. We illustrated the results by using a published literature database in human genome epidemiology (HuGE Pub Lit as a test case. Our parsing strategy extracted country information from 92.1% of the affiliation strings in a random sample of PubMed records and in 97.0% of HuGE records, with accuracies of 94.0% and 91.0%, respectively. Institution information was parsed from 91.3% of the general PubMed records (accuracy 86.8% and from 94.2% of HuGE PubMed records (accuracy 87.0. We demonstrated the application of our approach to dynamic creation of investigator networks by creating a prototype information system containing a large database of PubMed abstracts relevant to human genome epidemiology (HuGE Pub Lit, indexed using PubMed medical subject headings converted to Unified Medical Language System concepts. Our method was able to identify 70–90% of the investigators/collaborators in three different human genetics fields; it also successfully identified 9 of 10 genetics investigators within the PREBIC network, an existing preterm birth research network. Conclusion We successfully created a

An automatic method to generate domain-specific investigator networks using PubMed abstracts

Science.gov (United States)

Yu, Wei; Yesupriya, Ajay; Wulf, Anja; Qu, Junfeng; Gwinn, Marta; Khoury, Muin J

2007-01-01

Background Collaboration among investigators has become critical to scientific research. This includes ad hoc collaboration established through personal contacts as well as formal consortia established by funding agencies. Continued growth in online resources for scientific research and communication has promoted the development of highly networked research communities. Extending these networks globally requires identifying additional investigators in a given domain, profiling their research interests, and collecting current contact information. We present a novel strategy for building investigator networks dynamically and producing detailed investigator profiles using data available in PubMed abstracts. Results We developed a novel strategy to obtain detailed investigator information by automatically parsing the affiliation string in PubMed records. We illustrated the results by using a published literature database in human genome epidemiology (HuGE Pub Lit) as a test case. Our parsing strategy extracted country information from 92.1% of the affiliation strings in a random sample of PubMed records and in 97.0% of HuGE records, with accuracies of 94.0% and 91.0%, respectively. Institution information was parsed from 91.3% of the general PubMed records (accuracy 86.8%) and from 94.2% of HuGE PubMed records (accuracy 87.0). We demonstrated the application of our approach to dynamic creation of investigator networks by creating a prototype information system containing a large database of PubMed abstracts relevant to human genome epidemiology (HuGE Pub Lit), indexed using PubMed medical subject headings converted to Unified Medical Language System concepts. Our method was able to identify 70–90% of the investigators/collaborators in three different human genetics fields; it also successfully identified 9 of 10 genetics investigators within the PREBIC network, an existing preterm birth research network. Conclusion We successfully created a web-based prototype

Identification method of gas-liquid two-phase flow regime based on image wavelet packet information entropy and genetic neural network

International Nuclear Information System (INIS)

Zhou Yunlong; Chen Fei; Sun Bin

2008-01-01

Based on the characteristic that wavelet packet transform image can be decomposed by different scales, a flow regime identification method based on image wavelet packet information entropy feature and genetic neural network was proposed. Gas-liquid two-phase flow images were captured by digital high speed video systems in horizontal pipe. The information entropy feature from transformation coefficients were extracted using image processing techniques and multi-resolution analysis. The genetic neural network was trained using those eigenvectors, which was reduced by the principal component analysis, as flow regime samples, and the flow regime intelligent identification was realized. The test result showed that image wavelet packet information entropy feature could excellently reflect the difference between seven typical flow regimes, and the genetic neural network with genetic algorithm and BP algorithm merits were with the characteristics of fast convergence for simulation and avoidance of local minimum. The recognition possibility of the network could reach up to about 100%, and a new and effective method was presented for on-line flow regime. (authors)

Exploitation of genetic interaction network topology for the prediction of epistatic behavior

KAUST Repository

Alanis Lobato, Gregorio

2013-10-01

Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.

Exploitation of genetic interaction network topology for the prediction of epistatic behavior

KAUST Repository

Alanis Lobato, Gregorio; Cannistraci, Carlo; Ravasi, Timothy

2013-01-01

Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.

Knowledge sharing in infection prevention in routine and outbreak situations: a survey of the Society for Healthcare Epidemiology of America Research Network

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Rami Sommerstein

2017-08-01

Full Text Available Abstract In this cross-sectional Society for Healthcare Epidemiology of America Research Network survey on knowledge sharing in infection prevention we identified a rudimentary understanding of how to communicate and share knowledge within healthcare institutions. Our data support the need of further research in this important field.

Molecular and epidemiological study of enterovirus D68 in Taiwan.

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Huang, Yuan-Pin; Lin, Tsuey-Li; Lin, Ting-Han; Wu, Ho-Sheng

2017-08-01

As an immunofluorescence assay for enterovirus D68 (EV-D68) is not available in the enteroviruses surveillance network in Taiwan, EV-D68 may be the actual pathogen of untypeable enterovirus-suspected isolates. The untypeable isolates collected from 2007 through 2014 were identified by nucleic acid amplification-based methods and sequencing of the VP1 region to analyze the phylogeny and epidemiology of EV-D68 in Taiwan. Twenty-nine EV-D68 isolates were sequenced, including 15 Cluster 3 and 14 Cluster 1 viruses. Approximately 41% of the patients were children under 5 years of age and their infections peaked in August. The ratio of male to female patients was 1.5 and 3.67 for Cluster 3 and Cluster 1, respectively. Fever and respiratory symptoms were commonly reported in EV-D68-infected patients. The results of phylogenetic analyses showed that EV-D68 isolates between 2007 and 2014 belonged to different clusters and existed for years, indicating that endemic circulation of EV-D68 existed in Taiwan. This study showed that EV-D68 has been endemic in Taiwan for some years despite a small number of positive cases. The continuous monitoring and efforts towards the improvement of diagnostic techniques are required to complete the surveillance system. This study provided the genetic and epidemiological information which could contribute to understanding the etiology and epidemiology of EV-D68. Copyright © 2015. Published by Elsevier B.V.

[Sporulation or competence development? A genetic regulatory network model of cell-fate determination in Bacillus subtilis].

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Lu, Zhenghui; Zhou, Yuling; Zhang, Xiaozhou; Zhang, Guimin

2015-11-01

Bacillus subtilis is a generally recognized as safe (GRAS) strain that has been widely used in industries including fodder, food, and biological control. In addition, B. subtilis expression system also plays a significant role in the production of industrial enzymes. However, its application is limited by its low sporulation frequency and transformation efficiency. Immense studies have been done on interpreting the molecular mechanisms of sporulation and competence development, whereas only few of them were focused on improving sporulation frequency and transformation efficiency of B. subtilis by genetic modification. The main challenge is that sporulation and competence development, as the two major developmental events in the stationary phase of B. subtilis, are regulated by the complicated intracellular genetic regulatory systems. In addition, mutual regulatory mechanisms also exist in these two developmental events. With the development of genetic and metabolic engineering, constructing genetic regulatory networks is currently one of the most attractive research fields, together with the genetic information of cell growth, metabolism, and development, to guide the industrial application. In this review, the mechanisms of sporulation and competence development of B. subtilis, their interactions, and the genetic regulation of cell growth were interpreted. In addition, the roles of these regulatory networks in guiding basic and applied research of B. subtilis and its related species were discussed.

Panel 1: Epidemiology and Diagnosis.

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Homøe, Preben; Kværner, Kari; Casey, Janet R; Damoiseaux, Roger A M J; van Dongen, Thijs M A; Gunasekera, Hasantha; Jensen, Ramon G; Kvestad, Ellen; Morris, Peter S; Weinreich, Heather M

2017-04-01

Objective To create a literature review between 2011 and June 1, 2015, on advances in otitis media (OM) epidemiology and diagnosis (including relevant audiology studies). Data Sources Electronic search engines (PubMed, EMBASE, and Cochrane Library) with a predefined search strategy. Review Methods Articles with appropriate epidemiologic methodology for OM, including acute mastoiditis and eustachian tube dysfunction. Items included OM worldwide and in high-risk populations, OM-related hearing loss, news in OM diagnostics, prenatal risk factors and comorbidities, postnatal risk factors, genetics, microbiological epidemiology, guidelines, and quality of life. Conclusions Diagnostic evidence and genetic studies are increasing; guidelines are introduced worldwide; and there is evidence of benefit of pneumococcal conjugate vaccines. New risk factors and comordities are identified in the study period, and quality of life is affected in children and their families. Implications for Practice Chronic suppurative OM occurs worldwide and contributes to lifelong hearing loss. Uniform definitions are still lacking and should be provided. An association between HIV and chronic suppurative OM has been found. Tympanometry is recommended for diagnosis, with or without pneumatic otoscopy. Video otoscopy, algorithms, and validated questionnaires may assist clinicians. Childhood obesity is associated with OM. Heritability accounts for 20% to 50% of OM diagnoses. OM-prone children seem to produce weaker immunologic responses to pneumococcal conjugate vaccines. Clinicians tend to individualize treatment without adhering to guidelines.

Alzheimer’s disease is not “brain aging”: neuropathological, genetic, and epidemiological human studies

Science.gov (United States)

Head, Elizabeth; Schmitt, Frederick A.; Davis, Paulina R.; Neltner, Janna H.; Jicha, Gregory A.; Abner, Erin L.; Smith, Charles D.; Van Eldik, Linda J.; Kryscio, Richard J.; Scheff, Stephen W.

2011-01-01

Human studies are reviewed concerning whether “aging”-related mechanisms contribute to Alzheimer’s disease (AD) pathogenesis. AD is defined by specific neuropathology: neuritic amyloid plaques and neocortical neurofibrillary tangles. AD pathology is driven by genetic factors related not to aging per se, but instead to the amyloid precursor protein (APP). In contrast to genes involved in APP-related mechanisms, there is no firm connection between genes implicated in human “accelerated aging” diseases (progerias) and AD. The epidemiology of AD in advanced age is highly relevant but deceptively challenging to address given the low autopsy rates in most countries. In extreme old age, brain diseases other than AD approximate AD prevalence while the impact of AD pathology appears to peak by age 95 and decline thereafter. Many distinct brain diseases other than AD afflict older human brains and contribute to cognitive impairment. Additional prevalent pathologies include cerebrovascular disease and hippocampal sclerosis, both high-morbidity brain diseases that appear to peak in incidence later than AD chronologically. Because of these common brain diseases of extreme old age, the epidemiology differs between clinical “dementia” and the subset of dementia cases with AD pathology. Additional aging-associated mechanisms for cognitive decline such as diabetes and synapse loss have been linked to AD and these hypotheses are discussed. Criteria are proposed to define an “aging-linked” disease, and AD fails all of these criteria. In conclusion, it may be most fruitful to focus attention on specific pathways involved in AD rather than attributing it to an inevitable consequence of aging. PMID:21516511

Impact of HFE genetic testing on clinical presentation of hereditary hemochromatosis: new epidemiological data

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Ka Chandran

2005-06-01

Full Text Available Abstract Background Hereditary hemochromatosis (HH is a common inherited disorder of iron metabolism in Northern European populations. The discovery of a candidate gene in 1996 (HFE, and of its main mutation (C282Y, has radically altered the way to diagnose this disease. The aim of this study was to assess the impact of the HFE gene discovery on the clinical presentation and epidemiology of HH. Methods We studied our cohort of 415 patients homozygous for the C282Y allele and included in a phlebotomy program in a blood centre in western Brittany, France. Results In this cohort, 56.9% of the patients were male and 21.9% began their phlebotomy program before the implementation of the genetic test. A significant decrease in the sex ratio was noticed following implementation of this DNA test, from 3.79 to 1.03 (p -5, meaning that the proportion of diagnosed females relatives to males greatly increased. The profile of HH patients at diagnosis changed after the DNA test became available. Serum ferritin and iron values were lower and there was a reduced frequency of clinical signs displayed at diagnosis, particularly skin pigmentation (20.1 vs. 40.4%, OR = 0.37, p Conclusion This study highlights the importance of the HFE gene discovery, which has simplified the diagnosis of HH and modified its clinical presentation and epidemiology. This study precisely measures these changes. Enhanced diagnosis of HFE-related HH at an early stage and implementation of phlebotomy treatment are anticipated to maintain normal life expectancy for these patients.

Optimization the Initial Weights of Artificial Neural Networks via Genetic Algorithm Applied to Hip Bone Fracture Prediction

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Yu-Tzu Chang

2012-01-01

Full Text Available This paper aims to find the optimal set of initial weights to enhance the accuracy of artificial neural networks (ANNs by using genetic algorithms (GA. The sample in this study included 228 patients with first low-trauma hip fracture and 215 patients without hip fracture, both of them were interviewed with 78 questions. We used logistic regression to select 5 important factors (i.e., bone mineral density, experience of fracture, average hand grip strength, intake of coffee, and peak expiratory flow rate for building artificial neural networks to predict the probabilities of hip fractures. Three-layer (one hidden layer ANNs models with back-propagation training algorithms were adopted. The purpose in this paper is to find the optimal initial weights of neural networks via genetic algorithm to improve the predictability. Area under the ROC curve (AUC was used to assess the performance of neural networks. The study results showed the genetic algorithm obtained an AUC of 0.858±0.00493 on modeling data and 0.802 ± 0.03318 on testing data. They were slightly better than the results of our previous study (0.868±0.00387 and 0.796±0.02559, resp.. Thus, the preliminary study for only using simple GA has been proved to be effective for improving the accuracy of artificial neural networks.

Multi-objective optimization of water supply network rehabilitation with non-dominated sorting Genetic Algorithm-Ⅱ

Institute of Scientific and Technical Information of China (English)

Xi JIN; Jie ZHANG; Jin-liang GAO; Wen-yan WU

2008-01-01

Through the transformation of hydraulic constraints into the objective functions associated with a water supply network rehabilitation problem, a non-dominated sorting Genetic Aigorithm-Ⅱ (NSGA-Ⅱ) can be used to solve the altered multi-objective optimization model. The introduction of NSGA-Ⅱ into water supply network optimal rehabilitation problem solves the conflict between one fitness value of standard genetic algorithm (SGA) and multi-objectives of rehabilitation problem. And the uncertainties brought by using weight coefficients or punish functions in conventional methods are controlled. And also by introduction of artificial inducement mutation (AIM) operation, the convergence speed of population is accelerated; this operation not only improves the convergence speed, but also improves the rationality and feasibility of solutions.

Systems level analysis of systemic sclerosis shows a network of immune and profibrotic pathways connected with genetic polymorphisms.

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J Matthew Mahoney

2015-01-01

Full Text Available Systemic sclerosis (SSc is a rare systemic autoimmune disease characterized by skin and organ fibrosis. The pathogenesis of SSc and its progression are poorly understood. The SSc intrinsic gene expression subsets (inflammatory, fibroproliferative, normal-like, and limited are observed in multiple clinical cohorts of patients with SSc. Analysis of longitudinal skin biopsies suggests that a patient's subset assignment is stable over 6-12 months. Genetically, SSc is multi-factorial with many genetic risk loci for SSc generally and for specific clinical manifestations. Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes using a gene-gene interaction network, and place the genetic risk loci in the context of the intrinsic subsets. To identify gene expression modules common to three independent datasets from three different clinical centers, we developed a consensus clustering procedure based on mutual information of partitions, an information theory concept, and performed a meta-analysis of these genome-wide gene expression datasets. We created a gene-gene interaction network of the conserved molecular features across the intrinsic subsets and analyzed their connections with SSc-associated genetic polymorphisms. The network is composed of distinct, but interconnected, components related to interferon activation, M2 macrophages, adaptive immunity, extracellular matrix remodeling, and cell proliferation. The network shows extensive connections between the inflammatory- and fibroproliferative-specific genes. The network also shows connections between these subset-specific genes and 30 SSc-associated polymorphic genes including STAT4, BLK, IRF7, NOTCH4, PLAUR, CSK, IRAK1, and several human leukocyte antigen (HLA genes. Our analyses suggest that the gene expression changes underlying the SSc subsets may be long-lived, but mechanistically interconnected

The role of social networking sites in medical genetics research.

Science.gov (United States)

Reaves, Allison Cook; Bianchi, Diana W

2013-05-01

Social networking sites (SNS) have potential value in the field of medical genetics as a means of research subject recruitment and source of data. This article examines the current role of SNS in medical genetics research and potential applications for these sites in future studies. Facebook is the primary SNS considered, given the prevalence of its use in the United States and role in a small but growing number of studies. To date, utilization of SNS in medical genetics research has been primarily limited to three studies that recruited subjects from populations of Facebook users [McGuire et al. (2009); Am J Bioeth 9: 3-10; Janvier et al. (2012); Pediatrics 130: 293-298; Leighton et al. (2012); Public Health Genomics 15: 11-21]. These studies and a number of other medical and public health studies that have used Facebook as a context for recruiting research subjects are discussed. Approaches for Facebook-based subject recruitment are identified, including paid Facebook advertising, snowball sampling, targeted searching and posting. The use of these methods in medical genetics research has the potential to facilitate cost-effective research on both large, heterogeneous populations and small, hard-to-access sub-populations. Copyright © 2013 Wiley Periodicals, Inc.

Mycobacterium bovis in Burkina Faso: epidemiologic and genetic links between human and cattle isolates.

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Sanou, Adama; Tarnagda, Zekiba; Kanyala, Estelle; Zingué, Dezemon; Nouctara, Moumini; Ganamé, Zakaria; Combary, Adjima; Hien, Hervé; Dembele, Mathurin; Kabore, Antoinette; Meda, Nicolas; Van de Perre, Philippe; Neveu, Dorine; Bañuls, Anne Laure; Godreuil, Sylvain

2014-10-01

In sub-Saharan Africa, bovine tuberculosis (bTB) is a potential hazard for animals and humans health. The goal of this study was to improve our understanding of bTB epidemiology in Burkina Faso and especially Mycobacterium bovis transmission within and between the bovine and human populations. Twenty six M. bovis strains were isolated from 101 cattle carcasses with suspected bTB lesions during routine meat inspections at the Bobo Dioulasso and Ouagadougou slaughterhouses. In addition, 7 M. bovis strains were isolated from 576 patients with pulmonary tuberculosis. Spoligotyping, RDAf1 deletion and MIRU-VNTR typing were used for strains genotyping. The isolation of M. bovis strains was confirmed by spoligotyping and 12 spoligotype signatures were detected. Together, the spoligotyping and MIRU-VNTR data allowed grouping the 33 M. bovis isolates in seven clusters including isolates exclusively from cattle (5) or humans (1) or from both (1). Moreover, these data (genetic analyses and phenetic tree) showed that the M. bovis isolates belonged to the African 1 (Af1) clonal complex (81.8%) and the putative African 5 (Af5) clonal complex (18.2%), in agreement with the results of RDAf1 deletion typing. This is the first detailed molecular characterization of M. bovis strains from humans and cattle in Burkina Faso. The distribution of the two Af1 and putative Af5 clonal complexes is comparable to what has been reported in neighbouring countries. Furthermore, the strain genetic profiles suggest that M. bovis circulates across the borders and that the Burkina Faso strains originate from different countries, but have a country-specific evolution. The genetic characterization suggests that, currently, M. bovis transmission occurs mainly between cattle, occasionally between cattle and humans and potentially between humans. This study emphasizes the bTB risk in cattle but also in humans and the difficulty to set up proper disease control strategies in Burkina Faso.

A Neural Network: Family Competition Genetic Algorithm and Its Applications in Electromagnetic Optimization

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P.-Y. Chen

2009-01-01

Full Text Available This study proposes a neural network-family competition genetic algorithm (NN-FCGA for solving the electromagnetic (EM optimization and other general-purpose optimization problems. The NN-FCGA is a hybrid evolutionary-based algorithm, combining the good approximation performance of neural network (NN and the robust and effective optimum search ability of the family competition genetic algorithms (FCGA to accelerate the optimization process. In this study, the NN-FCGA is used to extract a set of optimal design parameters for two representative design examples: the multiple section low-pass filter and the polygonal electromagnetic absorber. Our results demonstrate that the optimal electromagnetic properties given by the NN-FCGA are comparable to those of the FCGA, but reducing a large amount of computation time and a well-trained NN model that can serve as a nonlinear approximator was developed during the optimization process of the NN-FCGA.

The Congenital Heart Disease Genetic Network Study: Cohort description.

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Thanh T Hoang

Full Text Available The Pediatric Cardiac Genomics Consortium (PCGC designed the Congenital Heart Disease Genetic Network Study to provide phenotype and genotype data for a large congenital heart defects (CHDs cohort. This article describes the PCGC cohort, overall and by major types of CHDs (e.g., conotruncal defects and subtypes of conotrucal heart defects (e.g., tetralogy of Fallot and left ventricular outflow tract obstructions (e.g., hypoplastic left heart syndrome. Cases with CHDs were recruited through ten sites, 2010-2014. Information on cases (N = 9,727 and their parents was collected through interviews and medical record abstraction. Four case characteristics, eleven parental characteristics, and thirteen parent-reported neurodevelopment outcomes were summarized using counts and frequencies and compared across CHD types and subtypes. Eleven percent of cases had a genetic diagnosis. Among cases without a genetic diagnosis, the majority had conotruncal heart defects (40% or left ventricular outflow tract obstruction (21%. Across CHD types, there were significant differences (p<0.05 in the distribution of all four case characteristics (e.g., sex, four parental characteristics (e.g., maternal pregestational diabetes, and five neurodevelopmental outcomes (e.g., learning disabilities. Several characteristics (e.g., sex were also significantly different across CHD subtypes. The PCGC cohort is one of the largest CHD cohorts available for the study of genetic determinants of risk and outcomes. The majority of cases do not have a genetic diagnosis. This description of the PCGC cohort, including differences across CHD types and subtypes, provides a reference work for investigators who are interested in collaborating with or using publically available resources from the PCGC.

Awareness and uptake of direct-to-consumer genetic testing among cancer cases, their relatives, and controls: the Northwest Cancer Genetics Network.

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Hall, Taryn O; Renz, Anne D; Snapinn, Katherine W; Bowen, Deborah J; Edwards, Karen L

2012-07-01

To determine if awareness of, interest in, and use of direct-to-consumer (DTC) genetic testing is greater in a sample of high-risk individuals (cancer cases and their relatives), compared to controls. Participants were recruited from the Northwest Cancer Genetics Network. A follow-up survey was mailed to participants to assess DTC genetic testing awareness, interest, and use. One thousand two hundred sixty-seven participants responded to the survey. Forty-nine percent of respondents were aware of DTC genetic testing. Of those aware, 19% indicated interest in obtaining and testing. Additional information supplied by respondents who reported use of DTC genetic tests indicated that 55% of these respondents likely engaged in clinical genetic testing, rather than DTC genetic testing. Awareness of DTC genetic testing was greater in our sample of high-risk individuals than in controls and population-based studies. Although interest in and use of these tests among cases in our sample were equivalent to other population-based studies, interest in testing was higher among relatives and people who self-referred for a registry focused on cancer than among cases and controls. Additionally, our results suggest that there may be some confusion about what constitutes DTC genetic testing.

Molecular Epidemiology Identifies HIV Transmission Networks Associated With Younger Age and Heterosexual Exposure Among Korean Individuals

OpenAIRE

Chin, Bum Sik; Chaillon, Antoine; Mehta, Sanjay R.; Wertheim, Joel O.; Kim, Gayeon; Shin, Hyoung-Shik; Smith, Davey M.

2016-01-01

To evaluate if HIV transmission networks could be elucidated from data collected in a short time frame, 131 HIV-1 pol sequences were analyzed which were generated from treatment-naïve Korean individuals who were sequentially identified over 1 year. A transmission linkage was inferred when there was a genetic distance

Uses of the Twins UK genetic database.

Science.gov (United States)

Spector, Tim D

2007-11-01

Tim Spector is a Professor of Genetic Epidemiology at King's College London and Director of the Twin Research and Genetic Epidemiology Unit at St Thomas' Hospital, London. Professor Spector graduated from St Bartholomew's Hospital Medical School, London, in 1982. After working in General Medicine, he completed a MSc in Epidemiology, and his MD degree at the University of London in 1989. He founded the UK Twins Registry of 10,000 twins in 1993, which is one of the largest collections of genotype and phenotype information on twins worldwide, whose breadth of research has expanded to cover a wide range of common complex traits many of which were previously thought to be mainly due to aging and the environment. He has published over 350 research articles on common diseases. He has written several original articles on the genetics of a wide range of diseases and traits including back pain, acne, inflammation, obesity, memory, musical ability and sexuality. He is the principal investigator of the EU Euroclot and Treat OA study, and a partner in five others. He has written several books, focusing on osteoporosis and genetics and, in 2003, he published a popular book on genetics: Your Genes Unzipped.

Prune-belly syndrome: case series and review of the literature regarding early prenatal diagnosis, epidemiology, genetic factors, treatment, and prognosis.

Science.gov (United States)

Tonni, Gabriele; Ida, Vito; Alessandro, Ventura; Bonasoni, Maria Paola

2013-02-01

Prune-belly syndrome (PBS) is a rare congenital syndrome characterized by deficient abdominal muscles, urinary tract malformation, and in males, cryptorchidism and has an estimated incidence of 1 in 35,000 to 1 in 50,000 live births. The syndrome might be due to severe bladder outlet obstruction or to abdominal muscle deficiency secondary to a migrational defect of the lateral mesoblast between weeks 6 and 7 of pregnancy. The current review of the medical record reports a special focus on epidemiology, genetic factors, early prenatal diagnosis clusters, treatment, and prognosis of PBS.

Classification and prediction of river network ephemerality and its relevance for waterborne disease epidemiology

Science.gov (United States)

Perez-Saez, Javier; Mande, Theophile; Larsen, Joshua; Ceperley, Natalie; Rinaldo, Andrea

2017-12-01

The transmission of waterborne diseases hinges on the interactions between hydrology and ecology of hosts, vectors and parasites, with the long-term absence of water constituting a strict lower bound. However, the link between spatio-temporal patterns of hydrological ephemerality and waterborne disease transmission is poorly understood and difficult to account for. The use of limited biophysical and hydroclimate information from otherwise data scarce regions is therefore needed to characterize, classify, and predict river network ephemerality in a spatially explicit framework. Here, we develop a novel large-scale ephemerality classification and prediction methodology based on monthly discharge data, water and energy availability, and remote-sensing measures of vegetation, that is relevant to epidemiology, and maintains a mechanistic link to catchment hydrologic processes. Specifically, with reference to the context of Burkina Faso in sub-Saharan Africa, we extract a relevant set of catchment covariates that include the aridity index, annual runoff estimation using the Budyko framework, and hysteretical relations between precipitation and vegetation. Five ephemerality classes, from permanent to strongly ephemeral, are defined from the duration of 0-flow periods that also accounts for the sensitivity of river discharge to the long-lasting drought of the 70's-80's in West Africa. Using such classes, a gradient-boosted tree-based prediction yielded three distinct geographic regions of ephemerality. Importantly, we observe a strong epidemiological association between our predictions of hydrologic ephemerality and the known spatial patterns of schistosomiasis, an endemic parasitic waterborne disease in which infection occurs with human-water contact, and requires aquatic snails as an intermediate host. The general nature of our approach and its relevance for predicting the hydrologic controls on schistosomiasis occurrence provides a pathway for the explicit inclusion of

Integration of Molecular Pathology, Epidemiology, and Social Science for Global Precision Medicine

Science.gov (United States)

Nishi, Akihiro; Milner, Danny A; Giovannucci, Edward L.; Nishihara, Reiko; Tan, Andy S.; Kawachi, Ichiro; Ogino, Shuji

2015-01-01

Summary The precision medicine concept and the unique disease principle imply that each patient has unique pathogenic processes resulting from heterogeneous cellular genetic and epigenetic alterations, and interactions between cells (including immune cells) and exposures, including dietary, environmental, microbial, and lifestyle factors. As a core method field in population health science and medicine, epidemiology is a growing scientific discipline that can analyze disease risk factors, and develop statistical methodologies to maximize utilization of big data on populations and disease pathology. The evolving transdisciplinary field of molecular pathological epidemiology (MPE) can advance biomedical and health research by linking exposures to molecular pathologic signatures, enhancing causal inference, and identifying potential biomarkers for clinical impact. The MPE approach can be applied to any diseases, although it has been most commonly used in neoplastic diseases (including breast, lung and colorectal cancers) because of availability of various molecular diagnostic tests. However, use of state-of-the-art genomic, epigenomic and other omic technologies and expensive drugs in modern healthcare systems increases racial, ethnic and socioeconomic disparities. To address this, we propose to integrate molecular pathology, epidemiology, and social science. Social epidemiology integrates the latter two fields. The integrative social MPE model can embrace sociology, economics and precision medicine, address global health disparities and inequalities, and elucidate biological effects of social environments, behaviors, and networks. We foresee advancements of molecular medicine, including molecular diagnostics, biomedical imaging, and targeted therapeutics, which should benefit individuals in a global population, by means of an interdisciplinary approach of integrative MPE and social health science. PMID:26636627

Integration of molecular pathology, epidemiology and social science for global precision medicine.

Science.gov (United States)

Nishi, Akihiro; Milner, Danny A; Giovannucci, Edward L; Nishihara, Reiko; Tan, Andy S; Kawachi, Ichiro; Ogino, Shuji

2016-01-01

The precision medicine concept and the unique disease principle imply that each patient has unique pathogenic processes resulting from heterogeneous cellular genetic and epigenetic alterations and interactions between cells (including immune cells) and exposures, including dietary, environmental, microbial and lifestyle factors. As a core method field in population health science and medicine, epidemiology is a growing scientific discipline that can analyze disease risk factors and develop statistical methodologies to maximize utilization of big data on populations and disease pathology. The evolving transdisciplinary field of molecular pathological epidemiology (MPE) can advance biomedical and health research by linking exposures to molecular pathologic signatures, enhancing causal inference and identifying potential biomarkers for clinical impact. The MPE approach can be applied to any diseases, although it has been most commonly used in neoplastic diseases (including breast, lung and colorectal cancers) because of availability of various molecular diagnostic tests. However, use of state-of-the-art genomic, epigenomic and other omic technologies and expensive drugs in modern healthcare systems increases racial, ethnic and socioeconomic disparities. To address this, we propose to integrate molecular pathology, epidemiology and social science. Social epidemiology integrates the latter two fields. The integrative social MPE model can embrace sociology, economics and precision medicine, address global health disparities and inequalities, and elucidate biological effects of social environments, behaviors and networks. We foresee advancements of molecular medicine, including molecular diagnostics, biomedical imaging and targeted therapeutics, which should benefit individuals in a global population, by means of an interdisciplinary approach of integrative MPE and social health science.

Topology of genetic associations between regional gray matter volume and intellectual ability: Evidence for a high capacity network.

Science.gov (United States)

Bohlken, Marc M; Brouwer, Rachel M; Mandl, René C W; Hedman, Anna M; van den Heuvel, Martijn P; van Haren, Neeltje E M; Kahn, René S; Hulshoff Pol, Hilleke E

2016-01-01

Intelligence is associated with a network of distributed gray matter areas including the frontal and parietal higher association cortices and primary processing areas of the temporal and occipital lobes. Efficient information transfer between gray matter regions implicated in intelligence is thought to be critical for this trait to emerge. Genetic factors implicated in intelligence and gray matter may promote a high capacity for information transfer. Whether these genetic factors act globally or on local gray matter areas separately is not known. Brain maps of phenotypic and genetic associations between gray matter volume and intelligence were made using structural equation modeling of 3T MRI T1-weighted scans acquired in 167 adult twins of the newly acquired U-TWIN cohort. Subsequently, structural connectivity analyses (DTI) were performed to test the hypothesis that gray matter regions associated with intellectual ability form a densely connected core. Gray matter regions associated with intellectual ability were situated in the right prefrontal, bilateral temporal, bilateral parietal, right occipital and subcortical regions. Regions implicated in intelligence had high structural connectivity density compared to 10,000 reference networks (p=0.031). The genetic association with intelligence was for 39% explained by a genetic source unique to these regions (independent of total brain volume), this source specifically implicated the right supramarginal gyrus. Using a twin design, we show that intelligence is genetically represented in a spatially distributed and densely connected network of gray matter regions providing a high capacity infrastructure. Although genes for intelligence have overlap with those for total brain volume, we present evidence that there are genes for intelligence that act specifically on the subset of brain areas that form an efficient brain network. Copyright © 2015 Elsevier Inc. All rights reserved.

Global Stability of Complex-Valued Genetic Regulatory Networks with Delays on Time Scales

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Wang Yajing

2016-01-01

Full Text Available In this paper, the global exponential stability of complex-valued genetic regulatory networks with delays is investigated. Besides presenting conditions guaranteeing the existence of a unique equilibrium pattern, its global exponential stability is discussed. Some numerical examples for different time scales.

Epidemiología y genética: ¿alianza estratégica en el nuevo milenio? Epidemiology and genetics: a strategic alliance in the new millennium?

Directory of Open Access Journals (Sweden)

Gustavo Bergonzoli

2005-01-01

Full Text Available Although the information derived from biological markers could conceivably be used to overcome some of the problems intrinsic to virtually all epidemiologic study designs-case definition, true exposure level, host susceptibility and resistance to factors of interest, the misclassification of study subjects (false positive and false negative test results, etc.-, we are still unable to resolve all such problems with the tools available at present. Biological markers seem more promising as potential indicators of the degree of susceptibility than as indicators of disease occurrence, an application requiring further technical refinement. Currently biological markers are employed in public health mainly to screen for particular diseases. Unfortunately, these markers have their limitations. For one thing, it is unlikely that they will completely eliminate the problem of false positive and false negative results, since DNA from solid tumors undergoes slight degradation due to necrosis and since genetic markers are susceptible to the effects of exposure to medication, diet, sex, ethnicity, and even the circadian cycle. And even if false positives and negatives were ultimately eliminated, it would be impossible to use many of the analytical tools based on two by two tables, such as the chi squared test, logistic regression, the Poisson regression, Cox' proportional hazards ratio, etc., since such tools rely on comparisons of the number of false positives and negatives in the exposed and non-exposed groups. Finally, albeit no less important, certain ethical issues must be carefully considered before allowing the massive use of human genetic markers, which could lead to violations of the rights of individuals, families, and communities if carried out in an indiscriminate, unregulated fashion. Epidemiology is rapidly broadening its scope, a trend that will continue into the future; new analytical tools will be developed, and the working hypotheses to which

Primer Part 1-The building blocks of epilepsy genetics.

Science.gov (United States)

Helbig, Ingo; Heinzen, Erin L; Mefford, Heather C

2016-06-01

This is the first of a two-part primer on the genetics of the epilepsies within the Genetic Literacy Series of the Genetics Commission of the International League Against Epilepsy. In Part 1, we cover the foundations of epilepsy genetics including genetic epidemiology and the range of genetic variants that can affect the risk for developing epilepsy. We discuss various epidemiologic study designs that have been applied to the genetics of the epilepsies including population studies, which provide compelling evidence for a strong genetic contribution in many epilepsies. We discuss genetic risk factors varying in size, frequency, inheritance pattern, effect size, and phenotypic specificity, and provide examples of how genetic risk factors within the various categories increase the risk for epilepsy. We end by highlighting trends in epilepsy genetics including the increasing use of massive parallel sequencing technologies. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

EPIDEMIOLOGY OF CHOLERA OUTBREAK IN KAMPALA ...

African Journals Online (AJOL)

hi-tech

77 No. 7 July 2000. EPIDEMIOLOGY OF CHOLERA OUTBREAK IN KAMPALA, UGANDA ... spread much (106 cases in 1995), resulting in a low level of immunity ... An intensive social ... development of a network of community health workers,.

Epidemiology of idiopathic pulmonary fibrosis

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Ley B

2013-11-01

Full Text Available Brett Ley, Harold R Collard Department of Medicine, Division of Pulmonary and Critical Care Medicine, University of California San Francisco, San Francisco, California, USA Abstract: Idiopathic pulmonary fibrosis is a chronic fibrotic lung disease of unknown cause that occurs in adults and has a poor prognosis. Its epidemiology has been difficult to study because of its rarity and evolution in diagnostic and coding practices. Though uncommon, it is likely underappreciated both in terms of its occurrence (ie, incidence, prevalence and public health impact (ie, health care costs and resource utilization. Incidence and mortality appear to be on the rise, and prevalence is expected to increase with the aging population. Potential risk factors include occupational and environmental exposures, tobacco smoking, gastroesophageal reflux, and genetic factors. An accurate understanding of its epidemiology is important, especially as novel therapies are emerging. Keywords: idiopathic pulmonary fibrosis, epidemiology, incidence, prevalence, mortality, risk factors

Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks

International Nuclear Information System (INIS)

Zameer, Aneela; Arshad, Junaid; Khan, Asifullah; Raja, Muhammad Asif Zahoor

2017-01-01

Highlights: • Genetic programming based ensemble of neural networks is employed for short term wind power prediction. • Proposed predictor shows resilience against abrupt changes in weather. • Genetic programming evolves nonlinear mapping between meteorological measures and wind-power. • Proposed approach gives mathematical expressions of wind power to its independent variables. • Proposed model shows relatively accurate and steady wind-power prediction performance. - Abstract: The inherent instability of wind power production leads to critical problems for smooth power generation from wind turbines, which then requires an accurate forecast of wind power. In this study, an effective short term wind power prediction methodology is presented, which uses an intelligent ensemble regressor that comprises Artificial Neural Networks and Genetic Programming. In contrast to existing series based combination of wind power predictors, whereby the error or variation in the leading predictor is propagated down the stream to the next predictors, the proposed intelligent ensemble predictor avoids this shortcoming by introducing Genetical Programming based semi-stochastic combination of neural networks. It is observed that the decision of the individual base regressors may vary due to the frequent and inherent fluctuations in the atmospheric conditions and thus meteorological properties. The novelty of the reported work lies in creating ensemble to generate an intelligent, collective and robust decision space and thereby avoiding large errors due to the sensitivity of the individual wind predictors. The proposed ensemble based regressor, Genetic Programming based ensemble of Artificial Neural Networks, has been implemented and tested on data taken from five different wind farms located in Europe. Obtained numerical results of the proposed model in terms of various error measures are compared with the recent artificial intelligence based strategies to demonstrate the

Genetic architecture of wood properties based on association analysis and co-expression networks in white spruce.

Science.gov (United States)

Lamara, Mebarek; Raherison, Elie; Lenz, Patrick; Beaulieu, Jean; Bousquet, Jean; MacKay, John

2016-04-01

Association studies are widely utilized to analyze complex traits but their ability to disclose genetic architectures is often limited by statistical constraints, and functional insights are usually minimal in nonmodel organisms like forest trees. We developed an approach to integrate association mapping results with co-expression networks. We tested single nucleotide polymorphisms (SNPs) in 2652 candidate genes for statistical associations with wood density, stiffness, microfibril angle and ring width in a population of 1694 white spruce trees (Picea glauca). Associations mapping identified 229-292 genes per wood trait using a statistical significance level of P wood associated genes and several known MYB and NAC regulators were identified as network hubs. The network revealed a link between the gene PgNAC8, wood stiffness and microfibril angle, as well as considerable within-season variation for both genetic control of wood traits and gene expression. Trait associations were distributed throughout the network suggesting complex interactions and pleiotropic effects. Our findings indicate that integration of association mapping and co-expression networks enhances our understanding of complex wood traits. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Fault Diagnosis of Hydraulic Servo Valve Based on Genetic Optimization RBF-BP Neural Network

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Li-Ping FAN

2014-04-01

Full Text Available Electro-hydraulic servo valves are core components of the hydraulic servo system of rolling mills. It is necessary to adopt an effective fault diagnosis method to keep the hydraulic servo valve in a good work state. In this paper, RBF and BP neural network are integrated effectively to build a double hidden layers RBF-BP neural network for fault diagnosis. In the process of training the neural network, genetic algorithm (GA is used to initialize and optimize the connection weights and thresholds of the network. Several typical fault states are detected by the constructed GA-optimized fault diagnosis scheme. Simulation results shown that the proposed fault diagnosis scheme can give satisfactory effect.

Genetic, Psychological, and Personal Network Factors Associated With Changes in Binge Drinking Over 2 Years Among Mexican Heritage Adolescents in the USA.

Science.gov (United States)

Song, Sunmi; Marcum, Christopher Steven; Wilkinson, Anna V; Shete, Sanjay; Koehly, Laura M

2018-04-24

Despite prevalent binge drinking and alcohol-dependent symptoms among Hispanics, few studies have examined how multidimensional factors influence Hispanic adolescents' binge drinking. Purpose This study examines the effects of genetic, psychological, and social network factors on binge drinking over time among Mexican heritage adolescents in the USA and whether there are correlations among genetic variants that are associated with binge drinking and psychological and network characteristics. Mexican heritage adolescents (n = 731) participated in a longitudinal study, which included genetic testing at baseline, alcohol use assessments at first and second follow-ups, and questionnaires on sensation seeking, impulsivity, and peer and family network characteristics at second follow-up. Logistic regression and Spearman correlation analyses were performed. After adjusting for demographic characteristics, underlying genetic clustering, and binge drinking at first follow-up, two genetic variants on tryptophan hydroxylase 2 (TPH2; rs17110451, rs7963717), sensation seeking and impulsivity, and having a greater fraction of peers who drink or encourage drinking alcohol were associated with greater risk whereas another genetic variant on TPH2 (rs11178999) and having a greater fraction of close family relationships were associated with reduced risk for binge drinking at second follow-up. Genetic variants in TPH1 (rs591556) were associated with sensation seeking and impulsivity, while genetic variants in TPH2 (rs17110451) were associated with the fraction of drinkers in family. Results reveal that genetic variants in the serotonin pathway, behavioral disinhibition traits, and social networks exert joint influences on binge drinking in Mexican heritage adolescents in the USA.

eQTL Networks Reveal Complex Genetic Architecture in the Immature Soybean Seed

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Yung-Tsi Bolon

2014-03-01

Full Text Available The complex network of regulatory factors and interactions involved in transcriptional regulation within the seed is not well understood. To evaluate gene expression regulation in the immature seed, we utilized a genetical genomics approach on a soybean [ (L. Merr.] recombinant inbred line (RIL population and produced a genome-wide expression quantitative trait loci (eQTL dataset. The validity of the dataset was confirmed by mapping the eQTL hotspot for flavonoid biosynthesis-related genes to a region containing repeats of chalcone synthase (CHS genes known to correspond to the soybean inhibitor locus that regulates seed color. We then identified eQTL for genes with seed-specific expression and discovered striking eQTL hotspots at distinct genomic intervals on chromosomes (Chr 20, 7, and 13. The main eQTL hotspot for transcriptional regulation of fatty acid biosynthesis genes also coincided with regulation of oleosin genes. Transcriptional upregulation of genesets from eQTL with opposite allelic effects were also found. Gene–eQTL networks were constructed and candidate regulatory genes were identified from these three key loci specific to seed expression and enriched in genes involved in seed oil accumulation. Our data provides new insight into the complex nature of gene networks in the immature soybean seed and the genetic architecture that contributes to seed development.

Landscape attributes and life history variability shape genetic structure of trout populations in a stream network

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Neville, H.M.; Dunham, J.B.; Peacock, M.M.

2006-01-01

Spatial and temporal landscape patterns have long been recognized to influence biological processes, but these processes often operate at scales that are difficult to study by conventional means. Inferences from genetic markers can overcome some of these limitations. We used a landscape genetics approach to test hypotheses concerning landscape processes influencing the demography of Lahontan cutthroat trout in a complex stream network in the Great Basin desert of the western US. Predictions were tested with population- and individual-based analyses of microsatellite DNA variation, reflecting patterns of dispersal, population stability, and local effective population sizes. Complementary genetic inferences suggested samples from migratory corridors housed a mixture of fish from tributaries, as predicted based on assumed migratory life histories in those habitats. Also as predicted, populations presumed to have greater proportions of migratory fish or from physically connected, large, or high quality habitats had higher genetic variability and reduced genetic differentiation from other populations. Populations thought to contain largely non-migratory individuals generally showed the opposite pattern, suggesting behavioral isolation. Estimated effective sizes were small, and we identified significant and severe genetic bottlenecks in several populations that were isolated, recently founded, or that inhabit streams that desiccate frequently. Overall, this work suggested that Lahontan cutthroat trout populations in stream networks are affected by a combination of landscape and metapopulation processes. Results also demonstrated that genetic patterns can reveal unexpected processes, even within a system that is well studied from a conventional ecological perspective. ?? Springer 2006.

MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers.

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Allot, Alexis; Chennen, Kirsley; Nevers, Yannis; Poidevin, Laetitia; Kress, Arnaud; Ripp, Raymond; Thompson, Julie Dawn; Poch, Olivier; Lecompte, Odile

2017-06-16

The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user's specific interests and provides an efficient way to share information with collaborators. Furthermore, the user's behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends. ©Alexis Allot, Kirsley Chennen, Yannis

Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

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Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

2007-01-01

To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

Journal of Genetics | Indian Academy of Sciences

Indian Academy of Sciences (India)

population stratification; ancestry informative markers (AIMs); race; ethnicity; genetic epidemiology; forensic genetics; Hispanics. ... Department of Microbiology, Mount Sinai School of Medicine, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY 10029 ...

Quality control in mutation analysis: the European Molecular Genetics Quality Network (EMQN).

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Müller, C R

2001-08-01

The demand for clinical molecular genetics testing has steadily grown since its introduction in the 1980s. In order to reach and maintain the agreed quality standards of laboratory medicine, the same internal and external quality assurance (IQA/EQA) criteria have to be applied as for "conventional" clinical chemistry or pathology. In 1996 the European Molecular Genetics Quality Network (EMQN) was established in order to spread QA standards across Europe and to harmonise the existing national activities. EMQN is operated by a central co-ordinator and 17 national partners from 15 EU countries; since 1998 it is being funded by the EU commission for a 3-year period. EMQN promotes QA by two tools: by providing disease-specific best practice meetings (BPM) and EQA schemes. A typical BPM is focussed on one disease or group of related disorders. International experts report on the latest news of gene characterisation and function and the state-of-the-art techniques for mutation detection. Disease-specific EQA schemes are provided by experts in the field. DNA samples are sent out together with mock clinical referrals and a diagnostic question is asked. Written reports must be returned which are marked for genotyping and interpretation. So far, three BPMs have been held and six EQA schemes are in operation at various stages. Although mutation types and diagnostic techniques varied considerably between schemes, the overall technical performance showed a high diagnostic standard. Nevertheless, serious genotyping errors have been occurred in some schemes which underline the necessity of quality assurance efforts. The European Molecular Genetics Quality Network provides a necessary platform for the internal and external quality assurance of molecular genetic testing.

Melanocortin-1 receptor, skin cancer and phenotypic characteristics (M-SKIP project: study design and methods for pooling results of genetic epidemiological studies

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Raimondi Sara

2012-08-01

Full Text Available Abstract Background For complex diseases like cancer, pooled-analysis of individual data represents a powerful tool to investigate the joint contribution of genetic, phenotypic and environmental factors to the development of a disease. Pooled-analysis of epidemiological studies has many advantages over meta-analysis, and preliminary results may be obtained faster and with lower costs than with prospective consortia. Design and methods Based on our experience with the study design of the Melanocortin-1 receptor (MC1R gene, SKin cancer and Phenotypic characteristics (M-SKIP project, we describe the most important steps in planning and conducting a pooled-analysis of genetic epidemiological studies. We then present the statistical analysis plan that we are going to apply, giving particular attention to methods of analysis recently proposed to account for between-study heterogeneity and to explore the joint contribution of genetic, phenotypic and environmental factors in the development of a disease. Within the M-SKIP project, data on 10,959 skin cancer cases and 14,785 controls from 31 international investigators were checked for quality and recoded for standardization. We first proposed to fit the aggregated data with random-effects logistic regression models. However, for the M-SKIP project, a two-stage analysis will be preferred to overcome the problem regarding the availability of different study covariates. The joint contribution of MC1R variants and phenotypic characteristics to skin cancer development will be studied via logic regression modeling. Discussion Methodological guidelines to correctly design and conduct pooled-analyses are needed to facilitate application of such methods, thus providing a better summary of the actual findings on specific fields.

Melanocortin-1 receptor, skin cancer and phenotypic characteristics (M-SKIP) project: study design and methods for pooling results of genetic epidemiological studies

Science.gov (United States)

2012-01-01

Background For complex diseases like cancer, pooled-analysis of individual data represents a powerful tool to investigate the joint contribution of genetic, phenotypic and environmental factors to the development of a disease. Pooled-analysis of epidemiological studies has many advantages over meta-analysis, and preliminary results may be obtained faster and with lower costs than with prospective consortia. Design and methods Based on our experience with the study design of the Melanocortin-1 receptor (MC1R) gene, SKin cancer and Phenotypic characteristics (M-SKIP) project, we describe the most important steps in planning and conducting a pooled-analysis of genetic epidemiological studies. We then present the statistical analysis plan that we are going to apply, giving particular attention to methods of analysis recently proposed to account for between-study heterogeneity and to explore the joint contribution of genetic, phenotypic and environmental factors in the development of a disease. Within the M-SKIP project, data on 10,959 skin cancer cases and 14,785 controls from 31 international investigators were checked for quality and recoded for standardization. We first proposed to fit the aggregated data with random-effects logistic regression models. However, for the M-SKIP project, a two-stage analysis will be preferred to overcome the problem regarding the availability of different study covariates. The joint contribution of MC1R variants and phenotypic characteristics to skin cancer development will be studied via logic regression modeling. Discussion Methodological guidelines to correctly design and conduct pooled-analyses are needed to facilitate application of such methods, thus providing a better summary of the actual findings on specific fields. PMID:22862891

Dual gene activation and knockout screen reveals directional dependencies in genetic networks. | Office of Cancer Genomics

Science.gov (United States)

Understanding the direction of information flow is essential for characterizing how genetic networks affect phenotypes. However, methods to find genetic interactions largely fail to reveal directional dependencies. We combine two orthogonal Cas9 proteins from Streptococcus pyogenes and Staphylococcus aureus to carry out a dual screen in which one gene is activated while a second gene is deleted in the same cell. We analyze the quantitative effects of activation and knockout to calculate genetic interaction and directionality scores for each gene pair.

Selection bias in genetic-epidemiological studies of cleft lip and palate

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Christensen, K.; Holm, N.V.; Kock, K. (Odense Univ. (Denmark)); Olsen, J. (Aarhus Univ. (Denmark)); Fogh-Anderson, P.

1992-09-01

The possible impact of selection bias in genetic and epidemiological studies of cleft lip and palate was studied, using three nationwide ascertainment sources and an autopsy study in a 10% sample of the Danish population. A total of 670 cases were identified. Two national record systems, when used together, were found suitable for ascertaining facial cleft in live births. More than 95% ascertainment was obtained by means of surgical files for cleft lip (with or without cleft palate) without associated malformations/syndromes. However, surgical files could be a poor source for studying isolated cleft palate (CP) (only a 60% and biased ascertainment), and they cannot be used to study the prevalence of associated malformations or syndromes in facial cleft cases. The male:female ratio was 0.88 in surgically treated cases of CP and was 1.5 in nonoperated CP cases, making the overall sex ratio for CP 1.1 (95% confidence limits 0.86-1.4) The sex ratio for CP without associated malformation was 1.1 (95% confidence limits 0.84-1.6). One of the major test criteria in CP multifactorial threshold models (higher CP liability among male CP relatives) must be reconsidered, if other investigations confirm that a CP sex-ratio reversal to male predominance occurs when high ascertainment is achieved. 24 refs., 1 fig., 4 tabs.

Reporting of Human Genome Epidemiology (HuGE association studies: An empirical assessment

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Gwinn Marta

2008-05-01

Full Text Available Abstract Background Several thousand human genome epidemiology association studies are published every year investigating the relationship between common genetic variants and diverse phenotypes. Transparent reporting of study methods and results allows readers to better assess the validity of study findings. Here, we document reporting practices of human genome epidemiology studies. Methods Articles were randomly selected from a continuously updated database of human genome epidemiology association studies to be representative of genetic epidemiology literature. The main analysis evaluated 315 articles published in 2001–2003. For a comparative update, we evaluated 28 more recent articles published in 2006, focusing on issues that were poorly reported in 2001–2003. Results During both time periods, most studies comprised relatively small study populations and examined one or more genetic variants within a single gene. Articles were inconsistent in reporting the data needed to assess selection bias and the methods used to minimize misclassification (of the genotype, outcome, and environmental exposure or to identify population stratification. Statistical power, the use of unrelated study participants, and the use of replicate samples were reported more often in articles published during 2006 when compared with the earlier sample. Conclusion We conclude that many items needed to assess error and bias in human genome epidemiology association studies are not consistently reported. Although some improvements were seen over time, reporting guidelines and online supplemental material may help enhance the transparency of this literature.

Fine-scale population structure and riverscape genetics of brook trout (Salvelinus fontinalis) distributed continuously along headwater channel networks

Science.gov (United States)

Kanno, Yoichiro; Vokoun, Jason C.; Letcher, Benjamin H.

2011-01-01

Linear and heterogeneous habitat makes headwater stream networks an ideal ecosystem in which to test the influence of environmental factors on spatial genetic patterns of obligatory aquatic species. We investigated fine-scale population structure and influence of stream habitat on individual-level genetic differentiation in brook trout (Salvelinus fontinalis) by genotyping eight microsatellite loci in 740 individuals in two headwater channel networks (7.7 and 4.4 km) in Connecticut, USA. A weak but statistically significant isolation-by-distance pattern was common in both sites. In the field, many tagged individuals were recaptured in the same 50-m reaches within a single field season (summer to fall). One study site was characterized with a hierarchical population structure, where seasonal barriers (natural falls of 1.5–2.5 m in height during summer base-flow condition) greatly reduced gene flow and perceptible spatial patterns emerged because of the presence of tributaries, each with a group of genetically distinguishable individuals. Genetic differentiation increased when pairs of individuals were separated by high stream gradient (steep channel slope) or warm stream temperature in this site, although the evidence of their influence was equivocal. In a second site, evidence for genetic clusters was weak at best, but genetic differentiation between individuals was positively correlated with number of tributary confluences. We concluded that the population-level movement of brook trout was limited in the study headwater stream networks, resulting in the fine-scale population structure (genetic clusters and clines) even at distances of a few kilometres, and gene flow was mitigated by ‘riverscape’ variables, particularly by physical barriers, waterway distance (i.e. isolation-by-distance) and the presence of tributaries.

Prediction of Aerodynamic Coefficient using Genetic Algorithm Optimized Neural Network for Sparse Data

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Rajkumar, T.; Bardina, Jorge; Clancy, Daniel (Technical Monitor)

2002-01-01

Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic

A Nondominated Genetic Algorithm Procedure for Multiobjective Discrete Network Design under Demand Uncertainty

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Bian Changzhi

2015-01-01

Full Text Available This paper addresses the multiobjective discrete network design problem under demand uncertainty. The OD travel demands are supposed to be random variables with the given probability distribution. The problem is formulated as a bilevel stochastic optimization model where the decision maker’s objective is to minimize the construction cost, the expectation, and the standard deviation of total travel time simultaneously and the user’s route choice is described using user equilibrium model on the improved network under all scenarios of uncertain demand. The proposed model generates globally near-optimal Pareto solutions for network configurations based on the Monte Carlo simulation and nondominated sorting genetic algorithms II. Numerical experiments implemented on Nguyen-Dupuis test network show trade-offs among construction cost, the expectation, and standard deviation of total travel time under uncertainty are obvious. Investment on transportation facilities is an efficient method to improve the network performance and reduce risk under demand uncertainty, but it has an obvious marginal decreasing effect.

Detection of Healthcare-Related Extended-Spectrum Beta-Lactamase-Producing Escherichia coli Transmission Events Using Combined Genetic and Phenotypic Epidemiology.

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Anne F Voor In 't Holt

Full Text Available Since the year 2000 there has been a sharp increase in the prevalence of healthcare-related infections caused by extended-spectrum beta-lactamase (ESBL-producing Escherichia coli. However, the high community prevalence of ESBL-producing E. coli isolates means that many E. coli typing techniques may not be suitable for detecting E. coli transmission events. Therefore, we investigated if High-throughput MultiLocus Sequence Typing (HiMLST and/or Raman spectroscopy were suitable techniques for detecting recent E. coli transmission events.This study was conducted from January until December 2010 at Erasmus University Medical Center, Rotterdam, the Netherlands. Isolates were typed using HiMLST and Raman spectroscopy. A genetic cluster was defined as two or more patients carrying identical isolates. We used predefined definitions for epidemiological relatedness to assess healthcare-related transmission.We included 194 patients; strains of 112 patients were typed using HiMLST and strains of 194 patients were typed using Raman spectroscopy. Raman spectroscopy identified 16 clusters while HiMLST identified 10 clusters. However, no healthcare-related transmission events were detected. When combining data from both typing techniques, we identified eight clusters (n = 34 patients, as well as 78 patients with a non-cluster isolate. However, we could not detect any healthcare-related transmission in these 8 clusters.Although clusters were genetically detected using HiMLST and Raman spectroscopy, no definite epidemiological relationships could be demonstrated which makes the possibility of healthcare-related transmission events highly unlikely. Our results suggest that typing of ESBL-producing E. coli using HiMLST and/or Raman spectroscopy is not helpful in detecting E. coli healthcare-related transmission events.

Jimena: efficient computing and system state identification for genetic regulatory networks.

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Karl, Stefan; Dandekar, Thomas

2013-10-11

Boolean networks capture switching behavior of many naturally occurring regulatory networks. For semi-quantitative modeling, interpolation between ON and OFF states is necessary. The high degree polynomial interpolation of Boolean genetic regulatory networks (GRNs) in cellular processes such as apoptosis or proliferation allows for the modeling of a wider range of node interactions than continuous activator-inhibitor models, but suffers from scaling problems for networks which contain nodes with more than ~10 inputs. Many GRNs from literature or new gene expression experiments exceed those limitations and a new approach was developed. (i) As a part of our new GRN simulation framework Jimena we introduce and setup Boolean-tree-based data structures; (ii) corresponding algorithms greatly expedite the calculation of the polynomial interpolation in almost all cases, thereby expanding the range of networks which can be simulated by this model in reasonable time. (iii) Stable states for discrete models are efficiently counted and identified using binary decision diagrams. As application example, we show how system states can now be sampled efficiently in small up to large scale hormone disease networks (Arabidopsis thaliana development and immunity, pathogen Pseudomonas syringae and modulation by cytokinins and plant hormones). Jimena simulates currently available GRNs about 10-100 times faster than the previous implementation of the polynomial interpolation model and even greater gains are achieved for large scale-free networks. This speed-up also facilitates a much more thorough sampling of continuous state spaces which may lead to the identification of new stable states. Mutants of large networks can be constructed and analyzed very quickly enabling new insights into network robustness and behavior.

Magnetoencephalography Reveals a Widespread Increase in Network Connectivity in Idiopathic/Genetic Generalized Epilepsy.

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Adham Elshahabi

Full Text Available Idiopathic/genetic generalized epilepsy (IGE/GGE is characterized by seizures, which start and rapidly engage widely distributed networks, and result in symptoms such as absences, generalized myoclonic and primary generalized tonic-clonic seizures. Although routine magnetic resonance imaging is apparently normal, many studies have reported structural alterations in IGE/GGE patients using diffusion tensor imaging and voxel-based morphometry. Changes have also been reported in functional networks during generalized spike wave discharges. However, network function in the resting-state without epileptiforme discharges has been less well studied. We hypothesize that resting-state networks are more representative of the underlying pathophysiology and abnormal network synchrony. We studied functional network connectivity derived from whole-brain magnetoencephalography recordings in thirteen IGE/GGE and nineteen healthy controls. Using graph theoretical network analysis, we found a widespread increase in connectivity in patients compared to controls. These changes were most pronounced in the motor network, the mesio-frontal and temporal cortex. We did not, however, find any significant difference between the normalized clustering coefficients, indicating preserved gross network architecture. Our findings suggest that increased resting state connectivity could be an important factor for seizure spread and/or generation in IGE/GGE, and could serve as a biomarker for the disease.

Molecular Epidemiology and Genomics of Group A Streptococcus

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Bessen, Debra E.; McShan, W. Michael; Nguyen, Scott V.; Shetty, Amol; Agrawal, Sonia; Tettelin, Hervé

2014-01-01

Streptococcus pyogenes (group A streptococcus; GAS) is a strict human pathogen with a very high prevalence worldwide. This review highlights the genetic organization of the species and the important ecological considerations that impact its evolution. Recent advances are presented on the topics of molecular epidemiology, population biology, molecular basis for genetic change, genome structure and genetic flux, phylogenomics and closely related streptococcal species, and the long- and short-term evolution of GAS. The application of whole genome sequence data to addressing key biological questions is discussed. PMID:25460818

Modeling delay in genetic networks: from delay birth-death processes to delay stochastic differential equations.

Science.gov (United States)

Gupta, Chinmaya; López, José Manuel; Azencott, Robert; Bennett, Matthew R; Josić, Krešimir; Ott, William

2014-05-28

Delay is an important and ubiquitous aspect of many biochemical processes. For example, delay plays a central role in the dynamics of genetic regulatory networks as it stems from the sequential assembly of first mRNA and then protein. Genetic regulatory networks are therefore frequently modeled as stochastic birth-death processes with delay. Here, we examine the relationship between delay birth-death processes and their appropriate approximating delay chemical Langevin equations. We prove a quantitative bound on the error between the pathwise realizations of these two processes. Our results hold for both fixed delay and distributed delay. Simulations demonstrate that the delay chemical Langevin approximation is accurate even at moderate system sizes. It captures dynamical features such as the oscillatory behavior in negative feedback circuits, cross-correlations between nodes in a network, and spatial and temporal information in two commonly studied motifs of metastability in biochemical systems. Overall, these results provide a foundation for using delay stochastic differential equations to approximate the dynamics of birth-death processes with delay.

Modeling delay in genetic networks: From delay birth-death processes to delay stochastic differential equations

Energy Technology Data Exchange (ETDEWEB)

Gupta, Chinmaya; López, José Manuel; Azencott, Robert; Ott, William [Department of Mathematics, University of Houston, Houston, Texas 77004 (United States); Bennett, Matthew R. [Department of Biochemistry and Cell Biology, Rice University, Houston, Texas 77204, USA and Institute of Biosciences and Bioengineering, Rice University, Houston, Texas 77005 (United States); Josić, Krešimir [Department of Mathematics, University of Houston, Houston, Texas 77004 (United States); Department of Biology and Biochemistry, University of Houston, Houston, Texas 77204 (United States)

2014-05-28

Delay is an important and ubiquitous aspect of many biochemical processes. For example, delay plays a central role in the dynamics of genetic regulatory networks as it stems from the sequential assembly of first mRNA and then protein. Genetic regulatory networks are therefore frequently modeled as stochastic birth-death processes with delay. Here, we examine the relationship between delay birth-death processes and their appropriate approximating delay chemical Langevin equations. We prove a quantitative bound on the error between the pathwise realizations of these two processes. Our results hold for both fixed delay and distributed delay. Simulations demonstrate that the delay chemical Langevin approximation is accurate even at moderate system sizes. It captures dynamical features such as the oscillatory behavior in negative feedback circuits, cross-correlations between nodes in a network, and spatial and temporal information in two commonly studied motifs of metastability in biochemical systems. Overall, these results provide a foundation for using delay stochastic differential equations to approximate the dynamics of birth-death processes with delay.

Modeling delay in genetic networks: From delay birth-death processes to delay stochastic differential equations

International Nuclear Information System (INIS)

Gupta, Chinmaya; López, José Manuel; Azencott, Robert; Ott, William; Bennett, Matthew R.; Josić, Krešimir

2014-01-01

Delay is an important and ubiquitous aspect of many biochemical processes. For example, delay plays a central role in the dynamics of genetic regulatory networks as it stems from the sequential assembly of first mRNA and then protein. Genetic regulatory networks are therefore frequently modeled as stochastic birth-death processes with delay. Here, we examine the relationship between delay birth-death processes and their appropriate approximating delay chemical Langevin equations. We prove a quantitative bound on the error between the pathwise realizations of these two processes. Our results hold for both fixed delay and distributed delay. Simulations demonstrate that the delay chemical Langevin approximation is accurate even at moderate system sizes. It captures dynamical features such as the oscillatory behavior in negative feedback circuits, cross-correlations between nodes in a network, and spatial and temporal information in two commonly studied motifs of metastability in biochemical systems. Overall, these results provide a foundation for using delay stochastic differential equations to approximate the dynamics of birth-death processes with delay

Rationale for an integrated approach to genetic epidemiology.

Science.gov (United States)

Laberge, Claude M; Knoppers, Bartha Maria

1992-10-01

Genetic knowledge is now in the public domain and its interpretation by the media and the citizens brings the issues into the public forum of discussion for the necessary ethical, legal and socio-cultural evaluation of its application. Science is being perceived by some as dangerous and as requiring international regulation. Others feel that genetic knowledge will be the breakthrough that will permit medical progress and individual autonomy with regards to personal health and lifestyle choices. The mapping of the human genome has already yielded valuable information on an increasing number of diseases and their variants. Prevailing popular and journalistic archetypes ("imaginaires") used in the media are perceived by the producers as slowing down the possible application of genetic knowledge. The answers to these dilemmas are not readily apparent nor are they prescribed by classical philosophy of medicine. Since genetic knowledge eventually resides with the individual who carries the genes of disease and/or susceptibility, a logical approach to integration of this knowledge at a societal level would seem to reside with individual education and decision-making. The politics of the ensuing social debate could transform the current social contract since an individual's interests need to be balanced against those of his or her immediate family in the sharing of information. The ethical foundations of such a contract requires the genetic education of "Everyone" as a matter of urgent priority. Genetic education should not serve ideological power struggles between the medical establishment and the ethical-legal alliance. Instead, it should ensure the transfer of knowledge to physicians, to patients, to users, to planners, to social science and humanities researchers and to politicians, so that they may make "informed" and free decisions....

Journal of Genetics | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Genetics. Shanker Jayashree. Articles written in Journal of Genetics. Volume 94 Issue 3 September 2015 pp 539-549 Review Article. Genetic epidemiology of coronary artery disease: an Asian Indian perspective · Shanker Jayashree Maitra Arindam Kakkar V. Vijay · More Details Abstract Fulltext ...

Generation of intervention strategy for a genetic regulatory network represented by a family of Markov Chains.

Science.gov (United States)

Berlow, Noah; Pal, Ranadip

2011-01-01

Genetic Regulatory Networks (GRNs) are frequently modeled as Markov Chains providing the transition probabilities of moving from one state of the network to another. The inverse problem of inference of the Markov Chain from noisy and limited experimental data is an ill posed problem and often generates multiple model possibilities instead of a unique one. In this article, we address the issue of intervention in a genetic regulatory network represented by a family of Markov Chains. The purpose of intervention is to alter the steady state probability distribution of the GRN as the steady states are considered to be representative of the phenotypes. We consider robust stationary control policies with best expected behavior. The extreme computational complexity involved in search of robust stationary control policies is mitigated by using a sequential approach to control policy generation and utilizing computationally efficient techniques for updating the stationary probability distribution of a Markov chain following a rank one perturbation.

Predicting recurrent aphthous ulceration using genetic algorithms-optimized neural networks

Directory of Open Access Journals (Sweden)

Najla S Dar-Odeh

2010-05-01

Full Text Available Najla S Dar-Odeh1, Othman M Alsmadi2, Faris Bakri3, Zaer Abu-Hammour2, Asem A Shehabi3, Mahmoud K Al-Omiri1, Shatha M K Abu-Hammad4, Hamzeh Al-Mashni4, Mohammad B Saeed4, Wael Muqbil4, Osama A Abu-Hammad1 1Faculty of Dentistry, 2Faculty of Engineering and Technology, 3Faculty of Medicine, University of Jordan, Amman, Jordan; 4Dental Department, University of Jordan Hospital, Amman, JordanObjective: To construct and optimize a neural network that is capable of predicting the occurrence of recurrent aphthous ulceration (RAU based on a set of appropriate input data.Participants and methods: Artificial neural networks (ANN software employing genetic algorithms to optimize the architecture neural networks was used. Input and output data of 86 participants (predisposing factors and status of the participants with regards to recurrent aphthous ulceration were used to construct and train the neural networks. The optimized neural networks were then tested using untrained data of a further 10 participants.Results: The optimized neural network, which produced the most accurate predictions for the presence or absence of recurrent aphthous ulceration was found to employ: gender, hematological (with or without ferritin and mycological data of the participants, frequency of tooth brushing, and consumption of vegetables and fruits.Conclusions: Factors appearing to be related to recurrent aphthous ulceration and appropriate for use as input data to construct ANNs that predict recurrent aphthous ulceration were found to include the following: gender, hemoglobin, serum vitamin B12, serum ferritin, red cell folate, salivary candidal colony count, frequency of tooth brushing, and the number of fruits or vegetables consumed daily.Keywords: artifical neural networks, recurrent, aphthous ulceration, ulcer

Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks

Directory of Open Access Journals (Sweden)

Fayroz F. Sherif

2015-01-01

Full Text Available Single nucleotide polymorphisms (SNPs contribute most of the genetic variation to the human genome. SNPs associate with many complex and common diseases like Alzheimer’s disease (AD. Discovering SNP biomarkers at different loci can improve early diagnosis and treatment of these diseases. Bayesian network provides a comprehensible and modular framework for representing interactions between genes or single SNPs. Here, different Bayesian network structure learning algorithms have been applied in whole genome sequencing (WGS data for detecting the causal AD SNPs and gene-SNP interactions. We focused on polymorphisms in the top ten genes associated with AD and identified by genome-wide association (GWA studies. New SNP biomarkers were observed to be significantly associated with Alzheimer’s disease. These SNPs are rs7530069, rs113464261, rs114506298, rs73504429, rs7929589, rs76306710, and rs668134. The obtained results demonstrated the effectiveness of using BN for identifying AD causal SNPs with acceptable accuracy. The results guarantee that the SNP set detected by Markov blanket based methods has a strong association with AD disease and achieves better performance than both naïve Bayes and tree augmented naïve Bayes. Minimal augmented Markov blanket reaches accuracy of 66.13% and sensitivity of 88.87% versus 61.58% and 59.43% in naïve Bayes, respectively.

Clinico-epidemiologic features of oculocutaneous albinism in ...

African Journals Online (AJOL)

Background: Oculocutaneous albinism (OCA) is a genetically heterogeneous group of disorders characterized by the absence or reduced pigmentation of the skin, hair and eyes. To assess the clinico-epidemiologic features of different forms of OCA among Egyptian patients, we performed a retrospective study to determine ...

Design and Feasibility of an International Study Assessing the Prevalence of Contact Allergy to Fragrances in the General Population : The European Dermato-Epidemiology Network Fragrance Study

NARCIS (Netherlands)

Rossi, Marta; Coenraads, Pieter-Jan; Diepgen, Thomas; Svensson, Ake; Elsner, Peter; Goncalo, Margarida; Bruze, Magnus; Naldi, Luigi

2010-01-01

Background/Aims: Data on contact allergy to fragrances in the general population are limited. Data from allergological services suggest that the frequency of contact allergy to fragrances is increasing. The European Dermato-Epidemiology Network (EDEN) Fragrance Study aims to obtain reliable data on

Mycobacterium bovis in Burkina Faso: epidemiologic and genetic links between human and cattle isolates.

Directory of Open Access Journals (Sweden)

Adama Sanou

2014-10-01

Full Text Available In sub-Saharan Africa, bovine tuberculosis (bTB is a potential hazard for animals and humans health. The goal of this study was to improve our understanding of bTB epidemiology in Burkina Faso and especially Mycobacterium bovis transmission within and between the bovine and human populations.Twenty six M. bovis strains were isolated from 101 cattle carcasses with suspected bTB lesions during routine meat inspections at the Bobo Dioulasso and Ouagadougou slaughterhouses. In addition, 7 M. bovis strains were isolated from 576 patients with pulmonary tuberculosis. Spoligotyping, RDAf1 deletion and MIRU-VNTR typing were used for strains genotyping. The isolation of M. bovis strains was confirmed by spoligotyping and 12 spoligotype signatures were detected. Together, the spoligotyping and MIRU-VNTR data allowed grouping the 33 M. bovis isolates in seven clusters including isolates exclusively from cattle (5 or humans (1 or from both (1. Moreover, these data (genetic analyses and phenetic tree showed that the M. bovis isolates belonged to the African 1 (Af1 clonal complex (81.8% and the putative African 5 (Af5 clonal complex (18.2%, in agreement with the results of RDAf1 deletion typing.This is the first detailed molecular characterization of M. bovis strains from humans and cattle in Burkina Faso. The distribution of the two Af1 and putative Af5 clonal complexes is comparable to what has been reported in neighbouring countries. Furthermore, the strain genetic profiles suggest that M. bovis circulates across the borders and that the Burkina Faso strains originate from different countries, but have a country-specific evolution. The genetic characterization suggests that, currently, M. bovis transmission occurs mainly between cattle, occasionally between cattle and humans and potentially between humans. This study emphasizes the bTB risk in cattle but also in humans and the difficulty to set up proper disease control strategies in Burkina Faso.

Translational Epidemiology in Psychiatry

Science.gov (United States)

Weissman, Myrna M.; Brown, Alan S.; Talati, Ardesheer

2012-01-01

Translational research generally refers to the application of knowledge generated by advances in basic sciences research translated into new approaches for diagnosis, prevention, and treatment of disease. This direction is called bench-to-bedside. Psychiatry has similarly emphasized the basic sciences as the starting point of translational research. This article introduces the term translational epidemiology for psychiatry research as a bidirectional concept in which the knowledge generated from the bedside or the population can also be translated to the benches of laboratory science. Epidemiologic studies are primarily observational but can generate representative samples, novel designs, and hypotheses that can be translated into more tractable experimental approaches in the clinical and basic sciences. This bedside-to-bench concept has not been explicated in psychiatry, although there are an increasing number of examples in the research literature. This article describes selected epidemiologic designs, providing examples and opportunities for translational research from community surveys and prospective, birth cohort, and family-based designs. Rapid developments in informatics, emphases on large sample collection for genetic and biomarker studies, and interest in personalized medicine—which requires information on relative and absolute risk factors—make this topic timely. The approach described has implications for providing fresh metaphors to communicate complex issues in interdisciplinary collaborations and for training in epidemiology and other sciences in psychiatry. PMID:21646577

A Selfish Constraint Satisfaction Genetic Algorithms for Planning a Long-Distance Transportation Network

Science.gov (United States)

Onoyama, Takashi; Maekawa, Takuya; Kubota, Sen; Tsuruta, Setuso; Komoda, Norihisa

To build a cooperative logistics network covering multiple enterprises, a planning method that can build a long-distance transportation network is required. Many strict constraints are imposed on this type of problem. To solve these strict-constraint problems, a selfish constraint satisfaction genetic algorithm (GA) is proposed. In this GA, each gene of an individual satisfies only its constraint selfishly, disregarding the constraints of other genes in the same individuals. Moreover, a constraint pre-checking method is also applied to improve the GA convergence speed. The experimental result shows the proposed method can obtain an accurate solution in a practical response time.

Application of a hybrid model of neural networks and genetic algorithms to evaluate landslide susceptibility

Science.gov (United States)

Wang, H. B.; Li, J. W.; Zhou, B.; Yuan, Z. Q.; Chen, Y. P.

2013-03-01

In the last few decades, the development of Geographical Information Systems (GIS) technology has provided a method for the evaluation of landslide susceptibility and hazard. Slope units were found to be appropriate for the fundamental morphological elements in landslide susceptibility evaluation. Following the DEM construction in a loess area susceptible to landslides, the direct-reverse DEM technology was employed to generate 216 slope units in the studied area. After a detailed investigation, the landslide inventory was mapped in which 39 landslides, including paleo-landslides, old landslides and recent landslides, were present. Of the 216 slope units, 123 involved landslides. To analyze the mechanism of these landslides, six environmental factors were selected to evaluate landslide occurrence: slope angle, aspect, the height and shape of the slope, distance to river and human activities. These factors were extracted in terms of the slope unit within the ArcGIS software. The spatial analysis demonstrates that most of the landslides are located on convex slopes at an elevation of 100-150 m with slope angles from 135°-225° and 40°-60°. Landslide occurrence was then checked according to these environmental factors using an artificial neural network with back propagation, optimized by genetic algorithms. A dataset of 120 slope units was chosen for training the neural network model, i.e., 80 units with landslide presence and 40 units without landslide presence. The parameters of genetic algorithms and neural networks were then set: population size of 100, crossover probability of 0.65, mutation probability of 0.01, momentum factor of 0.60, learning rate of 0.7, max learning number of 10 000, and target error of 0.000001. After training on the datasets, the susceptibility of landslides was mapped for the land-use plan and hazard mitigation. Comparing the susceptibility map with landslide inventory, it was noted that the prediction accuracy of landslide occurrence

Optimal groundwater remediation using artificial neural networks and the genetic algorithm

Energy Technology Data Exchange (ETDEWEB)

Rogers, Leah L. [Stanford Univ., CA (United States)

1992-08-01

An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the GA searches through realizations or patterns of pumping and uses the trained network to predict the outcome of the realizations. This approach has advantages of parallel processing of the groundwater simulations and the ability to ``recycle`` or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models.

Optimal groundwater remediation using artificial neural networks and the genetic algorithm

International Nuclear Information System (INIS)

Rogers, L.L.

1992-08-01

An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the GA searches through realizations or patterns of pumping and uses the trained network to predict the outcome of the realizations. This approach has advantages of parallel processing of the groundwater simulations and the ability to ''recycle'' or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models

GABAergic synapse properties may explain genetic variation in hippocampal network oscillations in mice

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Tim S Heistek

2010-06-01

Full Text Available Cognitive ability and the properties of brain oscillation are highly heritable in humans. Genetic variation underlying oscillatory activity might give rise to differences in cognition and behavior. How genetic diversity translates into altered properties of oscillations and synchronization of neuronal activity is unknown. To address this issue, we investigated cellular and synaptic mechanisms of hippocampal fast network oscillations in eight genetically distinct inbred mouse strains. The frequency of carbachol-induced oscillations differed substantially between mouse strains. Since GABAergic inhibition sets oscillation frequency, we studied the properties of inhibitory synaptic inputs (IPSCs received by CA3 and CA1 pyramidal cells of three mouse strains that showed the highest, lowest and intermediate frequencies of oscillations. In CA3 pyramidal cells, the frequency of rhythmic IPSC input showed the same strain differences as the frequency of field oscillations. Furthermore, IPSC decay times in both CA1 and CA3 pyramidal cells were faster in mouse strains with higher oscillation frequencies than in mouse strains with lower oscillation frequency, suggesting that differences in GABAA-receptor subunit composition exist between these strains. Indeed, gene expression of GABAA-receptor β2 (Gabrb2 and β3 (Gabrb2 subunits was higher in mouse strains with faster decay kinetics compared with mouse strains with slower decay kinetics. Hippocampal pyramidal neurons in mouse strains with higher oscillation frequencies and faster decay kinetics fired action potential at higher frequencies. These data indicate that differences in genetic background may result in different GABAA-receptor subunit expression, which affects the rhythm of pyramidal neuron firing and fast network activity through GABA synapse kinetics.

A possible correlation between the host genetic background in the epidemiology of Hepatitis B virus in the Amazon region of Brazil

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A. K. C. R. Santos

1995-08-01

Full Text Available The Amazon region of Brazil is an area of great interest because of the large distribution of hepatitis B virus in specific Western areas. Seven urban communities and 24 Indian groups were visited in a total of 4,244 persons. Each individual was interviewed in order to obtain demographic and familial information. Whole blood was collected for serology and genetic determinations. Eleven genetic markers and three HBV markers were tested. Among the most relevant results it was possible to show that (i there was a large variation of previous exposure to HBV in both urban and non-urban groups ranging from 0 to 59.2%; (ii there was a different pattern of epidemiological distribution of HBV that was present even among a same linguistic Indian group, with mixed patterns of correlation between HBsAg and anti-HBs and (iii the prevalence of HBV markers (HBsAg and anti-HBs were significantly higher (P=0.0001 among the Indian population (18.8% than the urban groups (12.5%. Its possible that the host genetic background could influence and modulate the replication of the virus in order to generate HB carrier state.

Advancing ecological understandings through technological transformations in noninvasive genetics

Science.gov (United States)

Albano Beja-Pereira; Rita Oliveira; Paulo C. Alves; Michael K. Schwartz; Gordon Luikart

2009-01-01

Noninvasive genetic approaches continue to improve studies in molecular ecology, conservation genetics and related disciplines such as forensics and epidemiology. Noninvasive sampling allows genetic studies without disturbing or even seeing the target individuals. Although noninvasive genetic sampling has been used for wildlife studies since the 1990s, technological...

Optimal redistribution of an urban air quality monitoring network using atmospheric dispersion model and genetic algorithm

Science.gov (United States)

Hao, Yufang; Xie, Shaodong

2018-03-01

Air quality monitoring networks play a significant role in identifying the spatiotemporal patterns of air pollution, and they need to be deployed efficiently, with a minimum number of sites. The revision and optimal adjustment of existing monitoring networks is crucial for cities that have undergone rapid urban expansion and experience temporal variations in pollution patterns. The approach based on the Weather Research and Forecasting-California PUFF (WRF-CALPUFF) model and genetic algorithm (GA) was developed to design an optimal monitoring network. The maximization of coverage with minimum overlap and the ability to detect violations of standards were developed as the design objectives for redistributed networks. The non-dominated sorting genetic algorithm was applied to optimize the network size and site locations simultaneously for Shijiazhuang city, one of the most polluted cities in China. The assessment on the current network identified the insufficient spatial coverage of SO2 and NO2 monitoring for the expanding city. The optimization results showed that significant improvements were achieved in multiple objectives by redistributing the original network. Efficient coverage of the resulting designs improved to 60.99% and 76.06% of the urban area for SO2 and NO2, respectively. The redistributing design for multi-pollutant including 8 sites was also proposed, with the spatial representation covered 52.30% of the urban area and the overlapped areas decreased by 85.87% compared with the original network. The abilities to detect violations of standards were not improved as much as the other two objectives due to the conflicting nature between the multiple objectives. Additionally, the results demonstrated that the algorithm was slightly sensitive to the parameter settings, with the number of generations presented the most significant effect. Overall, our study presents an effective and feasible procedure for air quality network optimization at a city scale.

A genetic algorithm solution for the operation of green LTE networks with energy and environment considerations

KAUST Repository

Ghazzai, Hakim

2012-01-01

The Base Station (BS) sleeping strategy has become a well-known technique to achieve energy savings in cellular networks by switching off redundant BSs mainly for lightly loaded networks. Besides, the exploitation of renewable energies, as additional power sources in smart grids, becomes a real challenge to network operators to reduce power costs. In this paper, we propose a method based on genetic algorithms that decreases the energy consumption of a Long-Term Evolution (LTE) cellular network by not only shutting down underutilized BSs but also by optimizing the amounts of energy procured from the smart grid without affecting the desired Quality of Service. © 2012 Springer-Verlag.

Robust symptom networks in recurrent major depression across different levels of genetic and environmental risk

NARCIS (Netherlands)

van Loo, H.M.; Van Borkulo, C.D.; Peterson, R.E.; Fried, E.I.; Aggen, S.H.; Borsboom, D.; Kendler, K.S.

BACKGROUND: Genetic risk and environmental adversity-both important risk factors for major depression (MD)-are thought to differentially impact on depressive symptom types and associations. Does heterogeneity in these risk factors result in different depressive symptom networks in patients with MD?

Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms

Directory of Open Access Journals (Sweden)

Manuel Fogue

2018-01-01

Full Text Available Vehicular networks make use of the Roadside Units (RSUs to enhance the communication capabilities of the vehicles in order to forward control messages and/or to provide Internet access to vehicles, drivers and passengers. Unfortunately, within vehicular networks, the wireless signal propagation is mostly affected by buildings and other obstacles (e.g., urban fixtures, in particular when considering the IEEE 802.11p standard. Therefore, a crowded RSU deployment may be required to ensure vehicular communications within urban environments. Furthermore, some applications, notably those applications related to safety, require a fast and reliable warning data transmission to the emergency services and traffic authorities. However, communication is not always possible in vehicular environments due to the lack of connectivity even employing multiple hops. To overcome the signal propagation problem and delayed warning notification time issues, an effective, smart, cost-effective and all-purpose RSU deployment policy should be put into place. In this paper, we propose the genetic algorithm for roadside unit deployment (GARSUD system, which uses a genetic algorithm that is capable of automatically providing an RSU deployment suitable for any given road map layout. Our simulation results show that GARSUD is able to reduce the warning notification time (the time required to inform emergency authorities in traffic danger situations and to improve vehicular communication capabilities within different density scenarios and complexity layouts.

Survey Definitions of Gout for Epidemiologic Studies: Comparison With Crystal Identification as the Gold Standard

NARCIS (Netherlands)

Dalbeth, N.; Schumacher, H.R.; Fransen, J.; Neogi, T.; Jansen, T.L; Brown, M.; Louthrenoo, W.; Vazquez-Mellado, J.; Eliseev, M.; McCarthy, G.; Stamp, L.K.; Perez-Ruiz, F.; Sivera, F.; Ea, H.K.; Gerritsen, M.; Scire, C.A.; Cavagna, L.; Lin, C.; Chou, Y.Y.; Tausche, A.K.; Rocha Castelar-Pinheiro, G. da; Janssen, M; Chen, J.H.; Cimmino, M.A.; Uhlig, T.; Taylor, W.J.

2016-01-01

OBJECTIVE: To identify the best-performing survey definition of gout from items commonly available in epidemiologic studies. METHODS: Survey definitions of gout were identified from 34 epidemiologic studies contributing to the Global Urate Genetics Consortium (GUGC) genome-wide association study.

Do motifs reflect evolved function?--No convergent evolution of genetic regulatory network subgraph topologies.

Science.gov (United States)

Knabe, Johannes F; Nehaniv, Chrystopher L; Schilstra, Maria J

2008-01-01

Methods that analyse the topological structure of networks have recently become quite popular. Whether motifs (subgraph patterns that occur more often than in randomized networks) have specific functions as elementary computational circuits has been cause for debate. As the question is difficult to resolve with currently available biological data, we approach the issue using networks that abstractly model natural genetic regulatory networks (GRNs) which are evolved to show dynamical behaviors. Specifically one group of networks was evolved to be capable of exhibiting two different behaviors ("differentiation") in contrast to a group with a single target behavior. In both groups we find motif distribution differences within the groups to be larger than differences between them, indicating that evolutionary niches (target functions) do not necessarily mold network structure uniquely. These results show that variability operators can have a stronger influence on network topologies than selection pressures, especially when many topologies can create similar dynamics. Moreover, analysis of motif functional relevance by lesioning did not suggest that motifs were of greater importance to the functioning of the network than arbitrary subgraph patterns. Only when drastically restricting network size, so that one motif corresponds to a whole functionally evolved network, was preference for particular connection patterns found. This suggests that in non-restricted, bigger networks, entanglement with the rest of the network hinders topological subgraph analysis.

The leukemias: Epidemiologic aspects

International Nuclear Information System (INIS)

Linet, M.S.

1984-01-01

Particularly geared to physicians and cancer researchers, this study of the epidemiology and etiology of leukemia analyzes the four major leukemia subtypes in terms of genetic and familial determinant factors and examines the incidence, distribution and frequency of reported leukemia clusters. Linet discusses the connection between other types of malignancies, their treatments, and the subsequent development of leukemia and evaluates the impact on leukemia onset of such environmental factors as radiation therapy, drugs, and occupational hazards

A MODIFIED GENETIC ALGORITHM FOR FINDING FUZZY SHORTEST PATHS IN UNCERTAIN NETWORKS

Directory of Open Access Journals (Sweden)

A. A. Heidari

2016-06-01

Full Text Available In realistic network analysis, there are several uncertainties in the measurements and computation of the arcs and vertices. These uncertainties should also be considered in realizing the shortest path problem (SPP due to the inherent fuzziness in the body of expert's knowledge. In this paper, we investigated the SPP under uncertainty to evaluate our modified genetic strategy. We improved the performance of genetic algorithm (GA to investigate a class of shortest path problems on networks with vague arc weights. The solutions of the uncertain SPP with considering fuzzy path lengths are examined and compared in detail. As a robust metaheuristic, GA algorithm is modified and evaluated to tackle the fuzzy SPP (FSPP with uncertain arcs. For this purpose, first, a dynamic operation is implemented to enrich the exploration/exploitation patterns of the conventional procedure and mitigate the premature convergence of GA technique. Then, the modified GA (MGA strategy is used to resolve the FSPP. The attained results of the proposed strategy are compared to those of GA with regard to the cost, quality of paths and CPU times. Numerical instances are provided to demonstrate the success of the proposed MGA-FSPP strategy in comparison with GA. The simulations affirm that not only the proposed technique can outperform GA, but also the qualities of the paths are effectively improved. The results clarify that the competence of the proposed GA is preferred in view of quality quantities. The results also demonstrate that the proposed method can efficiently be utilized to handle FSPP in uncertain networks.

Optimization of the Compensation of a Meshed MV Network by a Modified Genetic Algorithm

DEFF Research Database (Denmark)

Nielsen, Hans; Paar, M.; Toman, P.

2007-01-01

The article discusses the utilization of a modified genetic algorithm (GA) for the optimization of the shunt compensation in meshed and radial MV distribution networks. The algorithm looks for minimum costs of the network power losses and minimum capital and operating costs of applied capacitors......, all of this under limitations specified by a multicriteria penalization function. The parallel evolution branches in the GA are used for the purpose of the optimization accelaration. The application of this GA has been implemented in Matlab. The evaluation part of the GA implementation is based...... on the steady-state analysis using a linear one-line diagram model of a power network. The results of steady-state solutions are compared with the results from the DIgSILENT PowerFactory program. Its practical applicability is demonstrated on examples of 22 kV and meshed overhead distribution networks....

A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network

International Nuclear Information System (INIS)

Yu, Feng; Xu, Xiaozhong

2014-01-01

Highlights: • A detailed data processing will make more accurate results prediction. • Taking a full account of more load factors to improve the prediction precision. • Improved BP network obtains higher learning convergence. • Genetic algorithm optimized by chaotic cat map enhances the global search ability. • The combined GA–BP model improved by modified additional momentum factor is superior to others. - Abstract: This paper proposes an appropriate combinational approach which is based on improved BP neural network for short-term gas load forecasting, and the network is optimized by the real-coded genetic algorithm. Firstly, several kinds of modifications are carried out on the standard neural network to accelerate the convergence speed of network, including improved additional momentum factor, improved self-adaptive learning rate and improved momentum and self-adaptive learning rate. Then, it is available to use the global search capability of optimized genetic algorithm to determine the initial weights and thresholds of BP neural network to avoid being trapped in local minima. The ability of GA is enhanced by cat chaotic mapping. In light of the characteristic of natural gas load for Shanghai, a series of data preprocessing methods are adopted and more comprehensive load factors are taken into account to improve the prediction accuracy. Such improvements facilitate forecasting efficiency and exert maximum performance of the model. As a result, the integration model improved by modified additional momentum factor gets more ideal solutions for short-term gas load forecasting, through analyses and comparisons of the above several different combinational algorithms

Review of epidemiological studies of human populations exposed to ionizing radiation

International Nuclear Information System (INIS)

Rao, B.S.

2002-01-01

Epidemiological studies undertaken in many radiation exposed cohorts have played an important role in the quantification of radiation risk. Follow up of nearly 100,000 A-bomb survivors by the Radiation Effects Research Foundation (RERF), constitutes the most comprehensive human epidemiological study. The study population covered both sexes, different age groups and dose ranges from a few mSv to 2-3 Sv. Among nearly 90,000 cohorts, as on 1990, 54% are alive. Among these, 35,000 are those exposed as children at the age<20 years. Nearly 20 % of the mortalities (8,040) were due to cancer. It was estimated from the analysis of these data that among the cancers observed in LSS cohorts, 425±45 cases (335 solid cancers+90 leukaemias) were attributable to radiation exposure. Assuming a value of two for DDREF, ICRP 60, 1991 estimated a cancer risk of 5% per Sv for low dose and low dose rate exposure conditions. There have been a number of efforts to study the human populations exposed to low level radiations. Epidemiological studies on nuclear workers from USA, UK and Canada constituting 95,673 workers spanning 2,124,526 person years was reported by Cardis et al. (1995). Total number of deaths were 15,825, of which 3,976 were cancer mortalities. The excess relative risk for all cancers excluding leukaemia is -0.07 per Sv (-0.4- +0.3) and for leukaemia (excluding CLL) is 2.18 (0.1-5.7). Epidemiological studies in high background radiation areas (HBRA) of Yangjiang, China and coastal Kerala showed no detectable increase in the incidence of cancers or of any genetic disorders. Epidemiological studies in human populations exposed to elevated background radiation for several generations did not show any increase in the genetic disorders. Recent information on the background incidence of monogenic disorders in human populations and the recoverability factor of induced genetic changes suggests a risk much lower than the earlier ICRP estimates. Many other epidemiological studies of

Nuclear reactors project optimization based on neural network and genetic algorithm; Otimizacao em projetos de reatores nucleares baseada em rede neural e algoritmo genetico

Energy Technology Data Exchange (ETDEWEB)

Pereira, Claudio M.N.A. [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil); Schirru, Roberto; Martinez, Aquilino S. [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia

1997-12-01

This work presents a prototype of a system for nuclear reactor core design optimization based on genetic algorithms and artificial neural networks. A neural network is modeled and trained in order to predict the flux and the neutron multiplication factor values based in the enrichment, network pitch and cladding thickness, with average error less than 2%. The values predicted by the neural network are used by a genetic algorithm in this heuristic search, guided by an objective function that rewards the high flux values and penalizes multiplication factors far from the required value. Associating the quick prediction - that may substitute the reactor physics calculation code - with the global optimization capacity of the genetic algorithm, it was obtained a quick and effective system for nuclear reactor core design optimization. (author). 11 refs., 8 figs., 3 tabs.

The landscape genetics of infectious disease emergence and spread.

Science.gov (United States)

Biek, Roman; Real, Leslie A

2010-09-01

The spread of parasites is inherently a spatial process often embedded in physically complex landscapes. It is therefore not surprising that infectious disease researchers are increasingly taking a landscape genetics perspective to elucidate mechanisms underlying basic ecological processes driving infectious disease dynamics and to understand the linkage between spatially dependent population processes and the geographic distribution of genetic variation within both hosts and parasites. The increasing availability of genetic information on hosts and parasites when coupled to their ecological interactions can lead to insights for predicting patterns of disease emergence, spread and control. Here, we review research progress in this area based on four different motivations for the application of landscape genetics approaches: (i) assessing the spatial organization of genetic variation in parasites as a function of environmental variability, (ii) using host population genetic structure as a means to parameterize ecological dynamics that indirectly influence parasite populations, for example, gene flow and movement pathways across heterogeneous landscapes and the concurrent transport of infectious agents, (iii) elucidating the temporal and spatial scales of disease processes and (iv) reconstructing and understanding infectious disease invasion. Throughout this review, we emphasize that landscape genetic principles are relevant to infection dynamics across a range of scales from within host dynamics to global geographic patterns and that they can also be applied to unconventional 'landscapes' such as heterogeneous contact networks underlying the spread of human and livestock diseases. We conclude by discussing some general considerations and problems for inferring epidemiological processes from genetic data and try to identify possible future directions and applications for this rapidly expanding field.

Molecular epidemiology: new rules for new tools?

Science.gov (United States)

Merlo, Domenico Franco; Sormani, Maria Pia; Bruzzi, Paolo

2006-08-30

Molecular epidemiology combines biological markers and epidemiological observations in the study of the environmental and genetic determinants of cancer and other diseases. The potential advantages associated with biomarkers are manifold and include: (a) increased sensitivity and specificity to carcinogenic exposures; (b) more precise evaluation of the interplay between genetic and environmental determinants of cancer; (c) earlier detection of carcinogenic effects of exposure; (d) characterization of disease subtypes-etiologies patterns; (e) evaluation of primary prevention measures. These, in turn, may translate into better tools for etiologic research, individual risk assessment, and, ultimately, primary and secondary prevention. An area that has not received sufficient attention concerns the validation of these biomarkers as surrogate endpoints for cancer risk. Validation of a candidate biomarker's surrogacy is the demonstration that it possesses the properties required for its use as a substitute for a true endpoint. The principles underlying the validation process underwent remarkable developments and discussion in therapeutic research. However, the challenges posed by the application of these principles to epidemiological research, where the basic tool for this validation (i.e., the randomized study) is seldom possible, have not been thoroughly explored. The validation process of surrogacy must be applied rigorously to intermediate biomarkers of cancer risk before using them as risk predictors at the individual as well as at the population level.

Epidemiology of brain tumors in childhood--a review

International Nuclear Information System (INIS)

Baldwin, Rachel Tobias; Preston-Martin, Susan

2004-01-01

Malignant brain tumors are the leading cause of cancer death among children and the second most common type of pediatric cancer. Despite several decades of epidemiologic investigation, the etiology of childhood brain tumors (CBT) is still largely unknown. A few genetic syndromes and ionizing radiation are established risk factors. Many environmental exposures and infectious agents have been suspected of playing a role in the development of CBT. This review, based on a search of the medical literature through August 2003, summarizes the epidemiologic evidence to date. The types of exposures discussed include ionizing radiation, N-nitroso compounds (NOC), pesticides, tobacco smoke, electromagnetic frequencies (EMF), infectious agents, medications, and parental occupational exposures. We have chosen to focus on perinatal exposures and review some of the recent evidence indicating that such exposures may play a significant role in the causation of CBT. The scientific community is rapidly learning more about the molecular mechanisms by which carcinogenesis occurs and how the brain develops. We believe that advances in genetic and molecular biologic technology, including improved histologic subtyping of tumors, will be of huge importance in the future of epidemiologic research and will lead to a more comprehensive understanding of CBT etiology. We discuss some of the early findings using these technologies

Gene Set Analyses of Genome-Wide Association Studies on 49 Quantitative Traits Measured in a Single Genetic Epidemiology Dataset

Directory of Open Access Journals (Sweden)

Jihye Kim

2013-09-01

Full Text Available Gene set analysis is a powerful tool for interpreting a genome-wide association study result and is gaining popularity these days. Comparison of the gene sets obtained for a variety of traits measured from a single genetic epidemiology dataset may give insights into the biological mechanisms underlying these traits. Based on the previously published single nucleotide polymorphism (SNP genotype data on 8,842 individuals enrolled in the Korea Association Resource project, we performed a series of systematic genome-wide association analyses for 49 quantitative traits of basic epidemiological, anthropometric, or blood chemistry parameters. Each analysis result was subjected to subsequent gene set analyses based on Gene Ontology (GO terms using gene set analysis software, GSA-SNP, identifying a set of GO terms significantly associated to each trait (pcorr < 0.05. Pairwise comparison of the traits in terms of the semantic similarity in their GO sets revealed surprising cases where phenotypically uncorrelated traits showed high similarity in terms of biological pathways. For example, the pH level was related to 7 other traits that showed low phenotypic correlations with it. A literature survey implies that these traits may be regulated partly by common pathways that involve neuronal or nerve systems.

Yeast Augmented Network Analysis (YANA: a new systems approach to identify therapeutic targets for human genetic diseases [v1; ref status: indexed, http://f1000r.es/3gk

Directory of Open Access Journals (Sweden)

David J. Wiley

2014-06-01

Full Text Available Genetic interaction networks that underlie most human diseases are highly complex and poorly defined. Better-defined networks will allow identification of a greater number of therapeutic targets. Here we introduce our Yeast Augmented Network Analysis (YANA approach and test it with the X-linked spinal muscular atrophy (SMA disease gene UBA1. First, we express UBA1 and a mutant variant in fission yeast and use high-throughput methods to identify fission yeast genetic modifiers of UBA1. Second, we analyze available protein-protein interaction network databases in both fission yeast and human to construct UBA1 genetic networks. Third, from these networks we identified potential therapeutic targets for SMA. Finally, we validate one of these targets in a vertebrate (zebrafish SMA model. This study demonstrates the power of combining synthetic and chemical genetics with a simple model system to identify human disease gene networks that can be exploited for treating human diseases.

A hybrid Genetic and Simulated Annealing Algorithm for Chordal Ring implementation in large-scale networks

DEFF Research Database (Denmark)

Riaz, M. Tahir; Gutierrez Lopez, Jose Manuel; Pedersen, Jens Myrup

2011-01-01

The paper presents a hybrid Genetic and Simulated Annealing algorithm for implementing Chordal Ring structure in optical backbone network. In recent years, topologies based on regular graph structures gained a lot of interest due to their good communication properties for physical topology of the...

Genetic epidemiology of sporadic colorectal cancer

Czech Academy of Sciences Publication Activity Database

Vodička, Pavel; Pardini, Barbara; Souček, P.; Novotný, J.; Naccarati, Alessio; Vodičková, Ludmila; Hánová, Monika; Tulupová, Elena; Poláková, Veronika; Halamková, J.; Hemminki, K.

2006-01-01

Roč. 18, Supplement 1 (2006), S8-S8 ISSN 1107-3756. [The 11th World Congress on Advances in Oncology and 9th International Symposium on Molecular Medicine . 12.10.2006-14.10.2006, Hersonissos] R&D Projects: GA ČR GA310/05/2626; GA MZd NR8563 Institutional research plan: CEZ:AV0Z50390512 Keywords : DNA repair genes Subject RIV: EB - Genetics ; Molecular Biology

Atopic dermatitis in diverse racial and ethnic groups-Variations in epidemiology, genetics, clinical presentation and treatment.

Science.gov (United States)

Kaufman, Bridget P; Guttman-Yassky, Emma; Alexis, Andrew F

2018-04-01

Atopic dermatitis (AD) is a chronic inflammatory skin condition that affects diverse ethnic groups with varying prevalence. Despite a predominance of studies in individuals of European ancestry, AD has been found to occur more frequently in Asian and Black individuals than Whites. Therefore, an understanding of the unique clinical features of AD in diverse ethnic groups, as well as the differences in genetic polymorphisms that influence susceptibility to AD and response to current therapies, is paramount for management of an increasingly diverse patient population. In this article, we review key nuances in the epidemiology, pathophysiology, clinical presentation and treatment of AD in non-White ethnic groups, which are largely underappreciated in the literature. We highlight the need for studies evaluating the tissue molecular and cellular phenotypes of AD in non-White patients, as well as greater inclusion of minority groups in clinical trials, to develop targeted treatments for a multi-ethnic population. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Molecular epidemiology of Epizootic haematopoietic necrosis virus (EHNV).

Science.gov (United States)

Hick, Paul M; Subramaniam, Kuttichantran; Thompson, Patrick M; Waltzek, Thomas B; Becker, Joy A; Whittington, Richard J

2017-11-01

Low genetic diversity of Epizootic haematopoietic necrosis virus (EHNV) was determined for the complete genome of 16 isolates spanning the natural range of hosts, geography and time since the first outbreaks of disease. Genomes ranged from 125,591-127,487 nucleotides with 97.47% pairwise identity and 106-109 genes. All isolates shared 101 core genes with 121 potential genes predicted within the pan-genome of this collection. There was high conservation within 90,181 nucleotides of the core genes with isolates separated by average genetic distance of 3.43 × 10 -4 substitutions per site. Evolutionary analysis of the core genome strongly supported historical epidemiological evidence of iatrogenic spread of EHNV to naïve hosts and establishment of endemic status in discrete ecological niches. There was no evidence of structural genome reorganization, however, the complement of non-core genes and variation in repeat elements enabled fine scale molecular epidemiological investigation of this unpredictable pathogen of fish. Copyright © 2017 Elsevier Inc. All rights reserved.

Network Biomarkers of Bladder Cancer Based on a Genome-Wide Genetic and Epigenetic Network Derived from Next-Generation Sequencing Data.

Science.gov (United States)

Li, Cheng-Wei; Chen, Bor-Sen

2016-01-01

Epigenetic and microRNA (miRNA) regulation are associated with carcinogenesis and the development of cancer. By using the available omics data, including those from next-generation sequencing (NGS), genome-wide methylation profiling, candidate integrated genetic and epigenetic network (IGEN) analysis, and drug response genome-wide microarray analysis, we constructed an IGEN system based on three coupling regression models that characterize protein-protein interaction networks (PPINs), gene regulatory networks (GRNs), miRNA regulatory networks (MRNs), and epigenetic regulatory networks (ERNs). By applying system identification method and principal genome-wide network projection (PGNP) to IGEN analysis, we identified the core network biomarkers to investigate bladder carcinogenic mechanisms and design multiple drug combinations for treating bladder cancer with minimal side-effects. The progression of DNA repair and cell proliferation in stage 1 bladder cancer ultimately results not only in the derepression of miR-200a and miR-200b but also in the regulation of the TNF pathway to metastasis-related genes or proteins, cell proliferation, and DNA repair in stage 4 bladder cancer. We designed a multiple drug combination comprising gefitinib, estradiol, yohimbine, and fulvestrant for treating stage 1 bladder cancer with minimal side-effects, and another multiple drug combination comprising gefitinib, estradiol, chlorpromazine, and LY294002 for treating stage 4 bladder cancer with minimal side-effects.

Towards systems genetic analyses in barley: Integration of phenotypic, expression and genotype data into GeneNetwork

Directory of Open Access Journals (Sweden)

Druka Arnis

2008-11-01

Full Text Available Abstract Background A typical genetical genomics experiment results in four separate data sets; genotype, gene expression, higher-order phenotypic data and metadata that describe the protocols, processing and the array platform. Used in concert, these data sets provide the opportunity to perform genetic analysis at a systems level. Their predictive power is largely determined by the gene expression dataset where tens of millions of data points can be generated using currently available mRNA profiling technologies. Such large, multidimensional data sets often have value beyond that extracted during their initial analysis and interpretation, particularly if conducted on widely distributed reference genetic materials. Besides quality and scale, access to the data is of primary importance as accessibility potentially allows the extraction of considerable added value from the same primary dataset by the wider research community. Although the number of genetical genomics experiments in different plant species is rapidly increasing, none to date has been presented in a form that allows quick and efficient on-line testing for possible associations between genes, loci and traits of interest by an entire research community. Description Using a reference population of 150 recombinant doubled haploid barley lines we generated novel phenotypic, mRNA abundance and SNP-based genotyping data sets, added them to a considerable volume of legacy trait data and entered them into the GeneNetwork http://www.genenetwork.org. GeneNetwork is a unified on-line analytical environment that enables the user to test genetic hypotheses about how component traits, such as mRNA abundance, may interact to condition more complex biological phenotypes (higher-order traits. Here we describe these barley data sets and demonstrate some of the functionalities GeneNetwork provides as an easily accessible and integrated analytical environment for exploring them. Conclusion By

Risk factors for Alzheimer's disease : a genetic-epidemiologic study

NARCIS (Netherlands)

C.M. van Duijn (Cornelia)

1992-01-01

textabstractThe work presented in this thesis has been motivated by the Jack of knowledge of risk factors for Alzheimer's disease. It has been long recognised that genetic factors are implicated, in particular in early-onset Alzheimer's disease.4 But to what extent are genetic factors involved?

Evaluation of Genetic Pattern of Non-Tuberculosis Mycobacterium Using VNTR Method

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Noorozi J

2011-06-01

Full Text Available Background and Objectives: Epidemiological studies of Non-tuberculosis Mycobacterium is important because of the drug resistance pattern and worldwide dissemination of these organisms. One of genetic fingerprinting methods for epidemiological studies is VNTR (Variable Number Tandem Repeat. In this study genetic pattern of atypical Mycobacterium was evaluated by VNTR method for epidemiologic studies. Methods: 48 pulmonary and non pulmonary specimens separated from patients with the symptoms of pulmonary tuberculosis (PTB and identified as Non-tuberculosis Mycobacteriumby phenotypic and PCR-RFLP methods were selected for this study. Clinical samples and their standard strains were evaluated according to VNTR pattern using the 7 genetic loci including ETR-B. ETR-F. ETR-C. MPTR-A. ETR-A. ETR-E. ETR-D.Results: The results of VNTR method showed that none of the 7 loci had any polymorphism in the standard strains of atypical mycobacterium. Some of these variable number tandem repeat in 42 clinical samples of non-tuberculosis Mycobacterium were polymorphic while the PCR product (for any loci was not found in the remaining 6 specimens. Conclusion: Although the used genetic loci of this study were suitable for epidemiological studies of Mycobacterium tuberculosis, these loci were not able to determine the diversity of genetics of non-tuberculosis Mycobacterium Therefore, it seems necessary that other loci be studied using VNTR method.

Multiobjecitve Sampling Design for Calibration of Water Distribution Network Model Using Genetic Algorithm and Neural Network

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Kourosh Behzadian

2008-03-01

Full Text Available In this paper, a novel multiobjective optimization model is presented for selecting optimal locations in the water distribution network (WDN with the aim of installing pressure loggers. The pressure data collected at optimal locations will be used later on in the calibration of the proposed WDN model. Objective functions consist of maximization of calibrated model prediction accuracy and minimization of the total cost for sampling design. In order to decrease the model run time, an optimization model has been developed using multiobjective genetic algorithm and adaptive neural network (MOGA-ANN. Neural networks (NNs are initially trained after a number of initial GA generations and periodically retrained and updated after generation of a specified number of full model-analyzed solutions. Trained NNs are replaced with the fitness evaluation of some chromosomes within the GA progress. Using cache prevents objective function evaluation of repetitive chromosomes within GA. Optimal solutions are obtained through pareto-optimal front with respect to the two objective functions. Results show that jointing NNs in MOGA for approximating portions of chromosomes’ fitness in each generation leads to considerable savings in model run time and can be promising for reducing run-time in optimization models with significant computational effort.

Capturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysis.

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Brett A McKinney

2009-03-01

Full Text Available Evidence from human genetic studies of several disorders suggests that interactions between alleles at multiple genes play an important role in influencing phenotypic expression. Analytical methods for identifying Mendelian disease genes are not appropriate when applied to common multigenic diseases, because such methods investigate association with the phenotype only one genetic locus at a time. New strategies are needed that can capture the spectrum of genetic effects, from Mendelian to multifactorial epistasis. Random Forests (RF and Relief-F are two powerful machine-learning methods that have been studied as filters for genetic case-control data due to their ability to account for the context of alleles at multiple genes when scoring the relevance of individual genetic variants to the phenotype. However, when variants interact strongly, the independence assumption of RF in the tree node-splitting criterion leads to diminished importance scores for relevant variants. Relief-F, on the other hand, was designed to detect strong interactions but is sensitive to large backgrounds of variants that are irrelevant to classification of the phenotype, which is an acute problem in genome-wide association studies. To overcome the weaknesses of these data mining approaches, we develop Evaporative Cooling (EC feature selection, a flexible machine learning method that can integrate multiple importance scores while removing irrelevant genetic variants. To characterize detailed interactions, we construct a genetic-association interaction network (GAIN, whose edges quantify the synergy between variants with respect to the phenotype. We use simulation analysis to show that EC is able to identify a wide range of interaction effects in genetic association data. We apply the EC filter to a smallpox vaccine cohort study of single nucleotide polymorphisms (SNPs and infer a GAIN for a collection of SNPs associated with adverse events. Our results suggest an important

HIV infection in India: Epidemiology, molecular epidemiology and ...

Indian Academy of Sciences (India)

PRAKASH KUMAR

The first case of HIV infection as well as first case of AIDS was reported in India ... There are very few studies on host genetic factors in India in context with ... In 2006, the surveillance network was expanded to. 1,122 sentinel sites covering almost every district in the ... A series of regional workshops in the country organized.

Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling.

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Christine T Ferrara

2008-03-01

Full Text Available Although numerous quantitative trait loci (QTL influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptin(ob/ob and the diabetes-susceptible BTBR leptin(ob/ob mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines. We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes.

Biostatistics and epidemiology a primer for health and biomedical professionals

CERN Document Server

Wassertheil-Smoller, Sylvia

2015-01-01

Since the publication of the first edition, Biostatistics and Epidemiology has attracted loyal readers from across specialty areas in the biomedical community. Not only does this textbook teach foundations of epidemiological design and statistical methods, but it also includes topics applicable to new areas of research. Areas covered in the fourth edition include a new chapter on risk prediction, risk reclassification and evaluation of biomarkers, new material on propensity analyses, and a vastly expanded chapter on genetic epidemiology, which  is particularly relevant to those who wish to understand the epidemiological and statistical aspects of scientific articles in this rapidly advancing field. Biostatistics and Epidemiology was written to be accessible for readers without backgrounds in mathematics. It provides clear explanations of underlying principles, as well as practical guidelines of "how to do it" and "how to interpret it."a philosophical explanation of the logic of science, subsections that ...

Optimisation of groundwater level monitoring networks using geostatistical modelling based on the Spartan family variogram and a genetic algorithm method

Science.gov (United States)

Parasyris, Antonios E.; Spanoudaki, Katerina; Kampanis, Nikolaos A.

2016-04-01

Groundwater level monitoring networks provide essential information for water resources management, especially in areas with significant groundwater exploitation for agricultural and domestic use. Given the high maintenance costs of these networks, development of tools, which can be used by regulators for efficient network design is essential. In this work, a monitoring network optimisation tool is presented. The network optimisation tool couples geostatistical modelling based on the Spartan family variogram with a genetic algorithm method and is applied to Mires basin in Crete, Greece, an area of high socioeconomic and agricultural interest, which suffers from groundwater overexploitation leading to a dramatic decrease of groundwater levels. The purpose of the optimisation tool is to determine which wells to exclude from the monitoring network because they add little or no beneficial information to groundwater level mapping of the area. Unlike previous relevant investigations, the network optimisation tool presented here uses Ordinary Kriging with the recently-established non-differentiable Spartan variogram for groundwater level mapping, which, based on a previous geostatistical study in the area leads to optimal groundwater level mapping. Seventy boreholes operate in the area for groundwater abstraction and water level monitoring. The Spartan variogram gives overall the most accurate groundwater level estimates followed closely by the power-law model. The geostatistical model is coupled to an integer genetic algorithm method programmed in MATLAB 2015a. The algorithm is used to find the set of wells whose removal leads to the minimum error between the original water level mapping using all the available wells in the network and the groundwater level mapping using the reduced well network (error is defined as the 2-norm of the difference between the original mapping matrix with 70 wells and the mapping matrix of the reduced well network). The solution to the

Discordant patterns of genetic and phenotypic differentiation in five grasshopper species codistributed across a microreserve network.

Science.gov (United States)

Ortego, Joaquín; García-Navas, Vicente; Noguerales, Víctor; Cordero, Pedro J

2015-12-01

Conservation plans can be greatly improved when information on the evolutionary and demographic consequences of habitat fragmentation is available for several codistributed species. Here, we study spatial patterns of phenotypic and genetic variation among five grasshopper species that are codistributed across a network of microreserves but show remarkable differences in dispersal-related morphology (body size and wing length), degree of habitat specialization and extent of fragmentation of their respective habitats in the study region. In particular, we tested the hypothesis that species with preferences for highly fragmented microhabitats show stronger genetic and phenotypic structure than codistributed generalist taxa inhabiting a continuous matrix of suitable habitat. We also hypothesized a higher resemblance of spatial patterns of genetic and phenotypic variability among species that have experienced a higher degree of habitat fragmentation due to their more similar responses to the parallel large-scale destruction of their natural habitats. In partial agreement with our first hypothesis, we found that genetic structure, but not phenotypic differentiation, was higher in species linked to highly fragmented habitats. We did not find support for congruent patterns of phenotypic and genetic variability among any studied species, indicating that they show idiosyncratic evolutionary trajectories and distinctive demographic responses to habitat fragmentation across a common landscape. This suggests that conservation practices in networks of protected areas require detailed ecological and evolutionary information on target species to focus management efforts on those taxa that are more sensitive to the effects of habitat fragmentation. © 2015 John Wiley & Sons Ltd.

Epidemiology and genetics of ventricular fibrillation during acute myocardial infarction

DEFF Research Database (Denmark)

Glinge, Charlotte; Sattler, Stefan; Jabbari, Reza

2016-01-01

of a family member is a risk factor for SCD and VF during acute myocardial infarction (MI), independent of traditional risk factors including family history of MI, suggesting a genetic component in the susceptibility to VF. To prevent SCD and VF due to MI, we need a better understanding of the genetic...... and molecular mechanisms causing VF in this apparently healthy population. Even though new insights and technologies have become available, the genetic predisposition to VF during MI remains poorly understood. Findings from a variety of different genetic studies have failed to reach reproducibility, although...... several genetic variants, both common and rare variants, have been associated to either VF or SCD. For this review, we searched PubMed for potentially relevant articles, using the following MeSH-terms: "sudden cardiac death", "ventricular fibrillation", "out-of-hospital cardiac arrest", "myocardial...

Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networks.

Science.gov (United States)

Fearnley, Liam G; Inouye, Michael

2016-10-01

Metabolomics is becoming feasible for population-scale studies of human disease. In this review, we survey epidemiological studies that leverage metabolomics and multi-omics to gain insight into disease mechanisms. We outline key practical, technological and analytical limitations while also highlighting recent successes in integrating these data. The use of multi-omics to infer reaction rates is discussed as a potential future direction for metabolomics research, as a means of identifying biomarkers as well as inferring causality. Furthermore, we highlight established analysis approaches as well as simulation-based methods currently used in single- and multi-cell levels in systems biology. © The Author 2016. Published by Oxford University Press on behalf of the International Epidemiological Association.

Identifying groups of critical edges in a realistic electrical network by multi-objective genetic algorithms

International Nuclear Information System (INIS)

Zio, E.; Golea, L.R.; Rocco S, C.M.

2012-01-01

In this paper, an analysis of the vulnerability of the Italian high-voltage (380 kV) electrical transmission network (HVIET) is carried out for the identification of the groups of links (or edges, or arcs) most critical considering the network structure and flow. Betweenness centrality and network connection efficiency variations are considered as measures of the importance of the network links. The search of the most critical ones is carried out within a multi-objective optimization problem aimed at the maximization of the importance of the groups and minimization of their dimension. The problem is solved using a genetic algorithm. The analysis is based only on information on the topology of the network and leads to the identification of the most important single component, couples of components, triplets and so forth. The comparison of the results obtained with those reported by previous analyses indicates that the proposed approach provides useful complementary information.

Colorectal Cancer in Iran: Molecular Epidemiology and Screening Strategies

Directory of Open Access Journals (Sweden)

Roya Dolatkhah

2015-01-01

Full Text Available Purpose. The increasing incidence of colorectal cancer (CRC in the past three decades in Iran has made it a major public health burden. This study aimed to report its epidemiologic features, molecular genetic aspects, survival, heredity, and screening pattern in Iran. Methods. A comprehensive literature review was conducted to identify the relevant published articles. We used medical subject headings, including colorectal cancer, molecular genetics, KRAS and BRAF mutations, screening, survival, epidemiologic study, and Iran. Results. Age standardized incidence rate of Iranian CRCs was 11.6 and 10.5 for men and women, respectively. Overall five-year survival rate was 41%, and the proportion of CRC among the younger age group was higher than that of western countries. Depending on ethnicity, geographical region, dietary, and genetic predisposition, mutation genes were considerably diverse and distinct among CRCs across Iran. The high occurrence of CRC in records of relatives of CRC patients showed that family history of CRC was more common among young CRCs. Conclusion. Appropriate screening strategies for CRC which is amenable to early detection through screening, especially in relatives of CRCs, should be considered as the first step in CRC screening programs.

Colorectal Cancer in Iran: Molecular Epidemiology and Screening Strategies

International Nuclear Information System (INIS)

Dolatkhah, R.; Somi, M. H.; Dolatkhah, R.; Kermani, I. A.; Dastgiri, S.

2015-01-01

The increasing incidence of colorectal cancer (CRC) in the past three decades in Iran has made it a major public health burden. This study aimed to report its epidemiologic features, molecular genetic aspects, survival, heredity, and screening pattern in Iran. Methods. A comprehensive literature review was conducted to identify the relevant published articles. We used medical subject headings, including colorectal cancer, molecular genetics, KRAS and BRAF mutations, screening, survival, epidemiologic study, and Iran. Results. Age standardized incidence rate of Iranian CRCs was 11.6 and 10.5 for men and women, respectively. Overall five-year survival rate was 41%, and the proportion of CRC among the younger age group was higher than that of western countries. Depending on ethnicity, geographical region, dietary, and genetic predisposition, mutation genes were considerably diverse and distinct among CRCs across Iran. The high occurrence of CRC in records of relatives of CRC patients showed that family history of CRC was more common among young CRCs. Conclusion. Appropriate screening strategies for CRC which is amenable to early detection through screening, especially in relatives of CRCs, should be considered as the first step in CRC screening programs.

STUDY OF SENSITIVITY OF THE PARAMETERS OF A GENETIC ALGORITHM FOR DESIGN OF WATER DISTRIBUTION NETWORKS

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Pedro L. Iglesias

2007-01-01

Full Text Available The Genetic Algorithms (GAs are a technique of optimization used for water distribution networks design. This work has been made with a modified pseudo genetic algorithm (PGA, whose main variation with a classical GA is a change in the codification of the chromosomes, which is made of numerical form instead of the binary codification. This variation entails a series of special characteristics in the codification and in the definition of the operations of mutation and crossover. Initially, the work displays the results of the PGA on a water network studied in the literature. The results show the kindness of the method. Also is made a statistical analysis of the obtained solutions. This analysis allows verifying the values of mutation and crossing probability more suitable for the proposed method. Finally, in the study of the analyzed water supply networks the concept of reliability in introduced. This concept is essential to understand the validity of the obtained results. The second part, starting with values optimized for the probability of crossing and mutation, the influence of the population size is analyzed in the final solutions on the network of Hanoi, widely studied in the bibliography. The aim is to find the most suitable configuration of the problem, so that good solutions are obtained in the less time.

STUDY OF SENSITIVITY OF THE PARAMETERS OF A GENETIC ALGORITHM FOR DESIGN OF WATER DISTRIBUTION NETWORKS

Directory of Open Access Journals (Sweden)

Pedro L. Iglesias

2007-12-01

Full Text Available The Genetic Algorithms (GAs are a technique of optimization used for water distribution networks design. This work has been made with a modified pseudo genetic algorithm (PGA, whose main variation with a classical GA is a change in the codification of the chromosomes, which is made of numerical form instead of the binary codification. This variation entails a series of special characteristics in the codification and in the definition of the operations of mutation and crossover. Initially, the work displays the results of the PGA on a water network studied in the literature. The results show the kindness of the method. Also is made a statistical analysis of the obtained solutions. This analysis allows verifying the values of mutation and crossing probability more suitable for the proposed method. Finally, in the study of the analyzed water supply networks the concept of reliability in introduced. This concept is essential to understand the validity of the obtained results. The second part, starting with values optimized for the probability of crossing and mutation, the influence of the population size is analyzed in the final solutions on the network of Hanoi, widely studied in the bibliography. The aim is to find the most suitable configuration of the problem, so that good solutions are obtained in the less time.

Triglyceride-Rich Lipoproteins and Atherosclerotic Cardiovascular Disease: New Insights From Epidemiology, Genetics, and Biology.

Science.gov (United States)

Nordestgaard, Børge G

2016-02-19

Scientific interest in triglyceride-rich lipoproteins has fluctuated over the past many years, ranging from beliefs that these lipoproteins cause atherosclerotic cardiovascular disease (ASCVD) to being innocent bystanders. Correspondingly, clinical recommendations have fluctuated from a need to reduce levels to no advice on treatment. New insight in epidemiology now suggests that these lipoproteins, marked by high triglycerides, are strong and independent predictors of ASCVD and all-cause mortality, and that their cholesterol content or remnant cholesterol likewise are strong predictors of ASCVD. Of all adults, 27% have triglycerides >2 mmol/L (176 mg/dL), and 21% have remnant cholesterol >1 mmol/L (39 mg/dL). For individuals in the general population with nonfasting triglycerides of 6.6 mmol/L (580 mg/dL) compared with individuals with levels of 0.8 mmol/L (70 mg/dL), the risks were 5.1-fold for myocardial infarction, 3.2-fold for ischemic heart disease, 3.2-fold for ischemic stroke, and 2.2-fold for all-cause mortality. Also, genetic studies using the Mendelian randomization design, an approach that minimizes problems with confounding and reverse causation, now demonstrate that triglyceride-rich lipoproteins are causally associated with ASCVD and all-cause mortality. Finally, genetic evidence also demonstrates that high concentrations of triglyceride-rich lipoproteins are causally associated with low-grade inflammation. This suggests that an important part of inflammation in atherosclerosis and ASCVD is because of triglyceride-rich lipoprotein degradation and uptake into macrophage foam cells in the arterial intima. Taken together, new insights now strongly suggest that elevated triglyceride-rich lipoproteins represent causal risk factors for low-grade inflammation, ASCVD, and all-cause mortality. © 2016 American Heart Association, Inc.

An Adaptive Filtering Algorithm Based on Genetic Algorithm-Backpropagation Network

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Kai Hu

2013-01-01

Full Text Available A new image filtering algorithm is proposed. GA-BPN algorithm uses genetic algorithm (GA to decide weights in a back propagation neural network (BPN. It has better global optimal characteristics than traditional optimal algorithm. In this paper, we used GA-BPN to do image noise filter researching work. Firstly, this paper uses training samples to train GA-BPN as the noise detector. Then, we utilize the well-trained GA-BPN to recognize noise pixels in target image. And at last, an adaptive weighted average algorithm is used to recover noise pixels recognized by GA-BPN. Experiment data shows that this algorithm has better performance than other filters.

PREDICTIVE CONTROL OF A BATCH POLYMERIZATION SYSTEM USING A FEEDFORWARD NEURAL NETWORK WITH ONLINE ADAPTATION BY GENETIC ALGORITHM

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A. Cancelier

Full Text Available Abstract This study used a predictive controller based on an empirical nonlinear model comprising a three-layer feedforward neural network for temperature control of the suspension polymerization process. In addition to the offline training technique, an algorithm was also analyzed for online adaptation of its parameters. For the offline training, the network was statically trained and the genetic algorithm technique was used in combination with the least squares method. For online training, the network was trained on a recurring basis and only the technique of genetic algorithms was used. In this case, only the weights and bias of the output layer neuron were modified, starting from the parameters obtained from the offline training. From the experimental results obtained in a pilot plant, a good performance was observed for the proposed control system, with superior performance for the control algorithm with online adaptation of the model, particularly with respect to the presence of off-set for the case of the fixed parameters model.

TheCellMap.org: A Web-Accessible Database for Visualizing and Mining the Global Yeast Genetic Interaction Network.

Science.gov (United States)

Usaj, Matej; Tan, Yizhao; Wang, Wen; VanderSluis, Benjamin; Zou, Albert; Myers, Chad L; Costanzo, Michael; Andrews, Brenda; Boone, Charles

2017-05-05

Providing access to quantitative genomic data is key to ensure large-scale data validation and promote new discoveries. TheCellMap.org serves as a central repository for storing and analyzing quantitative genetic interaction data produced by genome-scale Synthetic Genetic Array (SGA) experiments with the budding yeast Saccharomyces cerevisiae In particular, TheCellMap.org allows users to easily access, visualize, explore, and functionally annotate genetic interactions, or to extract and reorganize subnetworks, using data-driven network layouts in an intuitive and interactive manner. Copyright © 2017 Usaj et al.

Web tools for molecular epidemiology of tuberculosis.

Science.gov (United States)

Shabbeer, Amina; Ozcaglar, Cagri; Yener, Bülent; Bennett, Kristin P

2012-06-01

In this study we explore publicly available web tools designed to use molecular epidemiological data to extract information that can be employed for the effective tracking and control of tuberculosis (TB). The application of molecular methods for the epidemiology of TB complement traditional approaches used in public health. DNA fingerprinting methods are now routinely employed in TB surveillance programs and are primarily used to detect recent transmissions and in outbreak investigations. Here we present web tools that facilitate systematic analysis of Mycobacterium tuberculosis complex (MTBC) genotype information and provide a view of the genetic diversity in the MTBC population. These tools help answer questions about the characteristics of MTBC strains, such as their pathogenicity, virulence, immunogenicity, transmissibility, drug-resistance profiles and host-pathogen associativity. They provide an integrated platform for researchers to use molecular epidemiological data to address current challenges in the understanding of TB dynamics and the characteristics of MTBC. Copyright © 2011. Published by Elsevier B.V.

Reconstructing Genetic Regulatory Networks Using Two-Step Algorithms with the Differential Equation Models of Neural Networks.

Science.gov (United States)

Chen, Chi-Kan

2017-07-26

The identification of genetic regulatory networks (GRNs) provides insights into complex cellular processes. A class of recurrent neural networks (RNNs) captures the dynamics of GRN. Algorithms combining the RNN and machine learning schemes were proposed to reconstruct small-scale GRNs using gene expression time series. We present new GRN reconstruction methods with neural networks. The RNN is extended to a class of recurrent multilayer perceptrons (RMLPs) with latent nodes. Our methods contain two steps: the edge rank assignment step and the network construction step. The former assigns ranks to all possible edges by a recursive procedure based on the estimated weights of wires of RNN/RMLP (RE RNN /RE RMLP ), and the latter constructs a network consisting of top-ranked edges under which the optimized RNN simulates the gene expression time series. The particle swarm optimization (PSO) is applied to optimize the parameters of RNNs and RMLPs in a two-step algorithm. The proposed RE RNN -RNN and RE RMLP -RNN algorithms are tested on synthetic and experimental gene expression time series of small GRNs of about 10 genes. The experimental time series are from the studies of yeast cell cycle regulated genes and E. coli DNA repair genes. The unstable estimation of RNN using experimental time series having limited data points can lead to fairly arbitrary predicted GRNs. Our methods incorporate RNN and RMLP into a two-step structure learning procedure. Results show that the RE RMLP using the RMLP with a suitable number of latent nodes to reduce the parameter dimension often result in more accurate edge ranks than the RE RNN using the regularized RNN on short simulated time series. Combining by a weighted majority voting rule the networks derived by the RE RMLP -RNN using different numbers of latent nodes in step one to infer the GRN, the method performs consistently and outperforms published algorithms for GRN reconstruction on most benchmark time series. The framework of two

Comparison between genetic algorithm and self organizing map to detect botnet network traffic

Science.gov (United States)

Yugandhara Prabhakar, Shinde; Parganiha, Pratishtha; Madhu Viswanatham, V.; Nirmala, M.

2017-11-01

In Cyber Security world the botnet attacks are increasing. To detect botnet is a challenging task. Botnet is a group of computers connected in a coordinated fashion to do malicious activities. Many techniques have been developed and used to detect and prevent botnet traffic and the attacks. In this paper, a comparative study is done on Genetic Algorithm (GA) and Self Organizing Map (SOM) to detect the botnet network traffic. Both are soft computing techniques and used in this paper as data analytics system. GA is based on natural evolution process and SOM is an Artificial Neural Network type, uses unsupervised learning techniques. SOM uses neurons and classifies the data according to the neurons. Sample of KDD99 dataset is used as input to GA and SOM.

Predicting and controlling infectious disease epidemics using temporal networks.

Science.gov (United States)

Masuda, Naoki; Holme, Petter

2013-01-01

Infectious diseases can be considered to spread over social networks of people or animals. Mainly owing to the development of data recording and analysis techniques, an increasing amount of social contact data with time stamps has been collected in the last decade. Such temporal data capture the dynamics of social networks on a timescale relevant to epidemic spreading and can potentially lead to better ways to analyze, forecast, and prevent epidemics. However, they also call for extended analysis tools for network epidemiology, which has, to date, mostly viewed networks as static entities. We review recent results of network epidemiology for such temporal network data and discuss future developments.

Seeded Bayesian Networks: Constructing genetic networks from microarray data

Directory of Open Access Journals (Sweden)

Quackenbush John

2008-07-01

Full Text Available Abstract Background DNA microarrays and other genomics-inspired technologies provide large datasets that often include hidden patterns of correlation between genes reflecting the complex processes that underlie cellular metabolism and physiology. The challenge in analyzing large-scale expression data has been to extract biologically meaningful inferences regarding these processes – often represented as networks – in an environment where the datasets are often imperfect and biological noise can obscure the actual signal. Although many techniques have been developed in an attempt to address these issues, to date their ability to extract meaningful and predictive network relationships has been limited. Here we describe a method that draws on prior information about gene-gene interactions to infer biologically relevant pathways from microarray data. Our approach consists of using preliminary networks derived from the literature and/or protein-protein interaction data as seeds for a Bayesian network analysis of microarray results. Results Through a bootstrap analysis of gene expression data derived from a number of leukemia studies, we demonstrate that seeded Bayesian Networks have the ability to identify high-confidence gene-gene interactions which can then be validated by comparison to other sources of pathway data. Conclusion The use of network seeds greatly improves the ability of Bayesian Network analysis to learn gene interaction networks from gene expression data. We demonstrate that the use of seeds derived from the biomedical literature or high-throughput protein-protein interaction data, or the combination, provides improvement over a standard Bayesian Network analysis, allowing networks involving dynamic processes to be deduced from the static snapshots of biological systems that represent the most common source of microarray data. Software implementing these methods has been included in the widely used TM4 microarray analysis package.

Expression quantitative trait loci and genetic regulatory network analysis reveals that Gabra2 is involved in stress responses in the mouse.

Science.gov (United States)

Dai, Jiajuan; Wang, Xusheng; Chen, Ying; Wang, Xiaodong; Zhu, Jun; Lu, Lu

2009-11-01

Previous studies have revealed that the subunit alpha 2 (Gabra2) of the gamma-aminobutyric acid receptor plays a critical role in the stress response. However, little is known about the gentetic regulatory network for Gabra2 and the stress response. We combined gene expression microarray analysis and quantitative trait loci (QTL) mapping to characterize the genetic regulatory network for Gabra2 expression in the hippocampus of BXD recombinant inbred (RI) mice. Our analysis found that the expression level of Gabra2 exhibited much variation in the hippocampus across the BXD RI strains and between the parental strains, C57BL/6J, and DBA/2J. Expression QTL (eQTL) mapping showed three microarray probe sets of Gabra2 to have highly significant linkage likelihood ratio statistic (LRS) scores. Gene co-regulatory network analysis showed that 10 genes, including Gria3, Chka, Drd3, Homer1, Grik2, Odz4, Prkag2, Grm5, Gabrb1, and Nlgn1 are directly or indirectly associated with stress responses. Eleven genes were implicated as Gabra2 downstream genes through mapping joint modulation. The genetical genomics approach demonstrates the importance and the potential power of the eQTL studies in identifying genetic regulatory networks that contribute to complex traits, such as stress responses.

Reconfiguration of distribution networks to minimize loss and disruption costs using genetic algorithms

Energy Technology Data Exchange (ETDEWEB)

Cebrian, Juan Carlos; Kagan, Nelson [Department of Electrical Engineering, University of Sao Paulo, Escola Politecnica, Av. Prof. Luciano Gualberto, travessa 3 n 380 - CEP - 05508-970 - Sao Paulo (Brazil)

2010-01-15

In this paper a computational implementation of an evolutionary algorithm (EA) is shown in order to tackle the problem of reconfiguring radial distribution systems. The developed module considers power quality indices such as long duration interruptions and customer process disruptions due to voltage sags, by using the Monte Carlo simulation method. Power quality costs are modeled into the mathematical problem formulation, which are added to the cost of network losses. As for the EA codification proposed, a decimal representation is used. The EA operators, namely selection, recombination and mutation, which are considered for the reconfiguration algorithm, are herein analyzed. A number of selection procedures are analyzed, namely tournament, elitism and a mixed technique using both elitism and tournament. The recombination operator was developed by considering a chromosome structure representation that maps the network branches and system radiality, and another structure that takes into account the network topology and feasibility of network operation to exchange genetic material. The topologies regarding the initial population are randomly produced so as radial configurations are produced through the Prim and Kruskal algorithms that rapidly build minimum spanning trees. (author)

A drug-sensitive genetic network masks fungi from the immune system.

Directory of Open Access Journals (Sweden)

Robert T Wheeler

2006-04-01

Full Text Available Fungal pathogens can be recognized by the immune system via their beta-glucan, a potent proinflammatory molecule that is present at high levels but is predominantly buried beneath a mannoprotein coat and invisible to the host. To investigate the nature and significance of "masking" this molecule, we characterized the mechanism of masking and consequences of unmasking for immune recognition. We found that the underlying beta-glucan in the cell wall of Candida albicans is unmasked by subinhibitory doses of the antifungal drug caspofungin, causing the exposed fungi to elicit a stronger immune response. Using a library of bakers' yeast (Saccharomyces cerevisiae mutants, we uncovered a conserved genetic network that is required for concealing beta-glucan from the immune system and limiting the host response. Perturbation of parts of this network in the pathogen C. albicans caused unmasking of its beta-glucan, leading to increased beta-glucan receptor-dependent elicitation of key proinflammatory cytokines from primary mouse macrophages. By creating an anti-inflammatory barrier to mask beta-glucan, opportunistic fungi may promote commensal colonization and have an increased propensity for causing disease. Targeting the widely conserved gene network required for creating and maintaining this barrier may lead to novel broad-spectrum antimycotics.

A genetic epidemiological mega analysis of smoking initiation in adolescents

NARCIS (Netherlands)

Maes, H.H.; Prom-Wormley, E.; Eaves, L.J.; Rhee, S.H.; Hewitt, J.K.; Young, S.; Corley, R.; McGue, M.K.; Iacono, W.G.; Legrand, L.; Samek, D.; Murrelle, E.L.; Silberg, J.L.; Miles, D.; Schieken, R.M.; Beunen, G.P.; Thomis, M.; Rose, R.J.; Dick, D.M.; Boomsma, D.I.; Bartels, M.; Vink, J.M.; Lichtenstein, P.; White, V.; Kaprio, J.; Neale, M.C.

2017-01-01

Introduction. Previous studies in adolescents were not adequately powered to accurately disentangle genetic and environmental influences on smoking initiation across adolescence. Methods. Mega-analysis of pooled genetically informative data on smoking initiation was performed, with structural

Molecular genetics

International Nuclear Information System (INIS)

Parkinson, D.R.; Krontiris, T.G.

1986-01-01

In this chapter the authors review new findings concerning the molecular genetics of malignant melanoma in the context of other information obtained from clinical, epidemiologic, and cytogenetic studies in this malignancy. These new molecular approaches promise to provide a more complete understanding of the mechanisms involved in the development of melanoma, thereby suggesting new methods for its treatment and prevention

Genetic disorders from an endogamous population

African Journals Online (AJOL)

Abdulbari Bener

b Dept. of Evidence for Population Health Unit, School of Epidemiology and Health Sciences, University of Manchester, Manchester, UK ... genetics counseling and screening for the hereditary diseases programme. Results: The ..... Elementary.

Online Learning of Genetic Network Programming and its Application to Prisoner’s Dilemma Game

Science.gov (United States)

Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu; Murata, Junichi

A new evolutionary model with the network structure named Genetic Network Programming (GNP) has been proposed recently. GNP, that is, an expansion of GA and GP, represents solutions as a network structure and evolves it by using “offline learning (selection, mutation, crossover)”. GNP can memorize the past action sequences in the network flow, so it can deal with Partially Observable Markov Decision Process (POMDP) well. In this paper, in order to improve the ability of GNP, Q learning (an off-policy TD control algorithm) that is one of the famous online methods is introduced for online learning of GNP. Q learning is suitable for GNP because (1) in reinforcement learning, the rewards an agent will get in the future can be estimated, (2) TD control doesn’t need much memory and can learn quickly, and (3) off-policy is suitable in order to search for an optimal solution independently of the policy. Finally, in the simulations, online learning of GNP is applied to a player for “Prisoner’s dilemma game” and its ability for online adaptation is confirmed.

[Eco-epidemiology: towards epidemiology of complexity].

Science.gov (United States)

Bizouarn, Philippe

2016-05-01

In order to solve public health problems posed by the epidemiology of risk factors centered on the individual and neglecting the causal processes linking the risk factors with the health outcomes, Mervyn Susser proposed a multilevel epidemiology called eco-epidemiology, addressing the interdependence of individuals and their connection with molecular, individual, societal, environmental levels of organization participating in the causal disease processes. The aim of this epidemiology is to integrate more than a level of organization in design, analysis and interpretation of health problems. After presenting the main criticisms of risk-factor epidemiology focused on the individual, we will try to show how eco-epidemiology and its development could help to understand the need for a broader and integrative epidemiology, in which studies designed to identify risk factors would be balanced by studies designed to answer other questions equally vital to public health. © 2016 médecine/sciences – Inserm.

[Coronary heart disease: epidemiologic-genetic aspects].

Science.gov (United States)

Epstein, F H

1985-01-01

Coronary heart disease and the risk factors which predispose to it aggregate in families. How much of this clustering of disease is "explained" by the familial resemblance in predisposing factors? The published reports which bear on this question fall into six distinct study designs: prospective studies, persons at high or low risk or persons with and without a positive family history as points of departure, case-control studies, studies of patients who had a coronary angiogram and studies in different ethnic groups. The findings of the 16 investigations reviewed suggest that there are as yet unidentified factors - genetic, environmental or both - which are responsible for familial clustering of coronary heart disease, apart from the three main risk factors (serum lipids, blood pressure, smoking) and diabetes. Future research must put greater emphasis on studies of families rather than individuals and on closer collaboration between epidemiologists and geneticists, in order to fill these gaps in knowledge. It is likely that the individual predisposition to coronary heart disease is due in part to genetic influences which remain to be discovered in the course of such studies. They would help in identifying susceptible person in the population with greater precision than is now possible. The "high-risk strategy" of coronary heart disease prevention will become more efficient as more specific and sensitive tests of disease prediction are developed. In the meantime, preventive programmes must be put into action on the basis of what is already known, on the level of both the high-risk and the community-wide mass strategy.

The Current Status of the Disease Caused by Enterovirus 71 Infections: Epidemiology, Pathogenesis, Molecular Epidemiology, and Vaccine Development.

Science.gov (United States)

Chang, Ping-Chin; Chen, Shou-Chien; Chen, Kow-Tong

2016-09-09

Enterovirus 71 (EV71) infections have a major public health impact in the Asia-Pacific region. We reviewed the epidemiology, pathogenesis, and molecular epidemiology of EV71 infection as well as EV71 vaccine development. Previous studies were found using the search terms "enterovirus 71" and "epidemiology" or "pathogenesis" or "molecular epidemiology" or "vaccine" in Medline and PubMed. Articles that were not published in the English language, manuscripts without an abstract, and opinion articles were excluded from the review. The reported epidemiology of cases caused by EV71 infection varied from country to country; seasonal variations in incidence were observed. Most cases of EV71 infection that resulted in hospitalization for complications occurred in children less than five years old. The brainstem was the most likely major target of EV71 infection. The emergence of the EV71 epidemic in the Asia-Pacific region has been associated with the circulation of different genetic lineages (genotypes B3, B4, C1, C2, and C4) that appear to be undergoing rapid evolutionary changes. The relationship between the gene structure of the EV71 virus and the factors that ensure its survival, circulation, and evasion of immunity is still unknown. EV71 infection has emerged as an important global public health problem. Vaccine development, including the development of inactivated whole-virus live attenuated, subviral particles, and DNA vaccines, has been progressing.

Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks

Science.gov (United States)

García-Calvo, Raúl; Guisado, JL; Diaz-del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

2018-01-01

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes—master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)—is carried out for this problem. Several procedures that optimize the use of the GPU’s resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent

Graphics Processing Unit-Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks.

Science.gov (United States)

García-Calvo, Raúl; Guisado, J L; Diaz-Del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

2018-01-01

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes-master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)-is carried out for this problem. Several procedures that optimize the use of the GPU's resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent

Do-it-yourself networks: a novel method of generating weighted networks.

Science.gov (United States)

Shanafelt, D W; Salau, K R; Baggio, J A

2017-11-01

Network theory is finding applications in the life and social sciences for ecology, epidemiology, finance and social-ecological systems. While there are methods to generate specific types of networks, the broad literature is focused on generating unweighted networks. In this paper, we present a framework for generating weighted networks that satisfy user-defined criteria. Each criterion hierarchically defines a feature of the network and, in doing so, complements existing algorithms in the literature. We use a general example of ecological species dispersal to illustrate the method and provide open-source code for academic purposes.

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.

Molecular epidemiology and evolutionary genetics of Mycobacterium tuberculosis in Taipei.

Science.gov (United States)

Dou, Horng-Yunn; Tseng, Fan-Chen; Lin, Chih-Wei; Chang, Jia-Ru; Sun, Jun-Ren; Tsai, Wen-Shing; Lee, Shi-Yi; Su, Ih-Jen; Lu, Jang-Jih

2008-12-22

The control of tuberculosis in densely populated cities is complicated by close human-to-human contacts and potential transmission of pathogens from multiple sources. We conducted a molecular epidemiologic analysis of 356 Mycobacterium tuberculosis (MTB) isolates from patients presenting pulmonary tuberculosis in metropolitan Taipei. Classical antibiogram studies and genetic characterization, using mycobacterial interspersed repetitive-unit-variable-number tandem-repeat (MIRU-VNTR) typing and spoligotyping, were applied after culture. A total of 356 isolates were genotyped by standard spoligotyping and the strains were compared with in the international spoligotyping database (SpolDB4). All isolates were also categorized using the 15 loci MIRU-VNTR typing method and combin with NTF locus and RD deletion analyses. Of 356 isolates spoligotyped, 290 (81.4%) displayed known spoligotypes and 66 were not identified in the database. Major spoligotypes found were Beijing lineages (52.5%), followed by Haarlem lineages (13.5%) and EAI plus EAI-like lineages (11%). When MIRU-VNTR was employed, 140 patterns were identified, including 36 clusters by 252 isolates and 104 unique patterns, and the largest cluster comprised 95 isolates from the Beijing family. The combination of spoligotyping and MIRU-VNTR revealed that 236 (67%) of the 356 isolates were clustered in 43 genotypes. Strains of the Beijing family was more likely to be of modern strain and a higher percentage of multiple drug resistance than other families combined (P = 0.08). Patients infected with Beijing strains were younger than those with other strains (mean 58.7 vs. 64.2, p = 0.02). Moreover, 85.3% of infected persons younger than 25 years had Beijing modern strain, suggesting a possible recent spread in the young population by this family of TB strain in Taipei. Our data on MTB genotype in Taipei suggest that MTB infection has not been optimally controlled. Control efforts should be reinforced in view of the

Leveraging Social Networking Sites for an Autoimmune Hepatitis Genetic Repository: Pilot Study to Evaluate Feasibility.

Science.gov (United States)

Comerford, Megan; Fogel, Rachel; Bailey, James Robert; Chilukuri, Prianka; Chalasani, Naga; Lammert, Craig Steven

2018-01-18

Conventional approaches to participant recruitment are often inadequate in rare disease investigation. Social networking sites such as Facebook may provide a vehicle to circumvent common research limitations and pitfalls. We report our preliminary experience with Facebook-based methodology for participant recruitment and participation into an ongoing study of autoimmune hepatitis (AIH). The goal of our research was to conduct a pilot study to assess whether a Facebook-based methodology is capable of recruiting geographically widespread participants into AIH patient-oriented research and obtaining quality phenotypic data. We established a Facebook community, the Autoimmune Hepatitis Research Network (AHRN), in 2014 to provide a secure and reputable distillation of current literature and AIH research opportunities. Quarterly advertisements for our ongoing observational AIH study were posted on the AHRN over 2 years. Interested and self-reported AIH participants were subsequently enrolled after review of study materials and completion of an informed consent by our study coordinator. Participants returned completed study materials, including epidemiologic questionnaires and genetic material, to our facility via mail. Outside medical records were obtained and reviewed by a study physician. We successfully obtained all study materials from 29 participants with self-reported AIH within 2 years from 20 different states. Liver biopsy results were available for 90% (26/29) of participants, of which 81% (21/29) had findings consistent with AIH, 15% (4/29) were suggestive of AIH with features of primary biliary cholangitis (PBC), and 4% (1/29) had PBC alone. A total of 83% (24/29) had at least 2 of 3 proposed criteria: positive autoimmune markers, consistent histologic findings of AIH on liver biopsy, and reported treatment with immunosuppressant medications. Self-reported and physician records were discrepant for immunosuppressant medications or for AIH/PBC diagnoses in 4

A service-oriented architecture for integrating the modeling and formal verification of genetic regulatory networks

Directory of Open Access Journals (Sweden)

Page Michel

2009-12-01

Full Text Available Abstract Background The study of biological networks has led to the development of increasingly large and detailed models. Computer tools are essential for the simulation of the dynamical behavior of the networks from the model. However, as the size of the models grows, it becomes infeasible to manually verify the predictions against experimental data or identify interesting features in a large number of simulation traces. Formal verification based on temporal logic and model checking provides promising methods to automate and scale the analysis of the models. However, a framework that tightly integrates modeling and simulation tools with model checkers is currently missing, on both the conceptual and the implementational level. Results We have developed a generic and modular web service, based on a service-oriented architecture, for integrating the modeling and formal verification of genetic regulatory networks. The architecture has been implemented in the context of the qualitative modeling and simulation tool GNA and the model checkers NUSMV and CADP. GNA has been extended with a verification module for the specification and checking of biological properties. The verification module also allows the display and visual inspection of the verification results. Conclusions The practical use of the proposed web service is illustrated by means of a scenario involving the analysis of a qualitative model of the carbon starvation response in E. coli. The service-oriented architecture allows modelers to define the model and proceed with the specification and formal verification of the biological properties by means of a unified graphical user interface. This guarantees a transparent access to formal verification technology for modelers of genetic regulatory networks.

Exploring the genetics underlying autoimmune diseases with network analysis and link prediction

KAUST Repository

Alanis Lobato, Gregorio; Cannistraci, Carlo; Ravasi, Timothy

2014-01-01

Ever since the first Genome Wide Association Study (GWAS) was carried out we have seen an important number of discoveries of biological and clinical relevance. However, there are some scientists that consider that these research outcomes and their utility are far from what was expected from this experimental design. We instead believe that the thousands of genetic variants associated with complex disorders by means of GWASs are an extremely valuable source of information that needs to be mined in a different way. Based on this philosophy, we followed a holistic perspective to analyze GWAS data and explored the structural properties of the network representation of one of these datasets with the aim to advance our understanding of the genetic intricacies underlying autoimmune human diseases. The simplicity, computational efficiency and precision of the tools proposed in this paper represent a new means to address GWAS data and contribute to the better exploitation of these rich sources of information. © 2014 IEEE.

Exploring the genetics underlying autoimmune diseases with network analysis and link prediction

KAUST Repository

Alanis Lobato, Gregorio

2014-02-01

Ever since the first Genome Wide Association Study (GWAS) was carried out we have seen an important number of discoveries of biological and clinical relevance. However, there are some scientists that consider that these research outcomes and their utility are far from what was expected from this experimental design. We instead believe that the thousands of genetic variants associated with complex disorders by means of GWASs are an extremely valuable source of information that needs to be mined in a different way. Based on this philosophy, we followed a holistic perspective to analyze GWAS data and explored the structural properties of the network representation of one of these datasets with the aim to advance our understanding of the genetic intricacies underlying autoimmune human diseases. The simplicity, computational efficiency and precision of the tools proposed in this paper represent a new means to address GWAS data and contribute to the better exploitation of these rich sources of information. © 2014 IEEE.

PREDICTIVE CONTROL OF A BATCH POLYMERIZATION SYSTEM USING A FEEDFORWARD NEURAL NETWORK WITH ONLINE ADAPTATION BY GENETIC ALGORITHM

OpenAIRE

Cancelier, A.; Claumann, C. A.; Bolzan, A.; Machado, R. A. F.

2016-01-01

Abstract This study used a predictive controller based on an empirical nonlinear model comprising a three-layer feedforward neural network for temperature control of the suspension polymerization process. In addition to the offline training technique, an algorithm was also analyzed for online adaptation of its parameters. For the offline training, the network was statically trained and the genetic algorithm technique was used in combination with the least squares method. For online training, ...

Phelan-McDermid syndrome data network: Integrating patient reported outcomes with clinical notes and curated genetic reports.

Science.gov (United States)

Kothari, Cartik; Wack, Maxime; Hassen-Khodja, Claire; Finan, Sean; Savova, Guergana; O'Boyle, Megan; Bliss, Geraldine; Cornell, Andria; Horn, Elizabeth J; Davis, Rebecca; Jacobs, Jacquelyn; Kohane, Isaac; Avillach, Paul

2017-09-01

The heterogeneity of patient phenotype data are an impediment to the research into the origins and progression of neuropsychiatric disorders. This difficulty is compounded in the case of rare disorders such as Phelan-McDermid Syndrome (PMS) by the paucity of patient clinical data. PMS is a rare syndromic genetic cause of autism and intellectual deficiency. In this paper, we describe the Phelan-McDermid Syndrome Data Network (PMS_DN), a platform that facilitates research into phenotype-genotype correlation and progression of PMS by: a) integrating knowledge of patient phenotypes extracted from Patient Reported Outcomes (PRO) data and clinical notes-two heterogeneous, underutilized sources of knowledge about patient phenotypes-with curated genetic information from the same patient cohort and b) making this integrated knowledge, along with a suite of statistical tools, available free of charge to authorized investigators on a Web portal https://pmsdn.hms.harvard.edu. PMS_DN is a Patient Centric Outcomes Research Initiative (PCORI) where patients and their families are involved in all aspects of the management of patient data in driving research into PMS. To foster collaborative research, PMS_DN also makes patient aggregates from this knowledge available to authorized investigators using distributed research networks such as the PCORnet PopMedNet. PMS_DN is hosted on a scalable cloud based environment and complies with all patient data privacy regulations. As of October 31, 2016, PMS_DN integrates high-quality knowledge extracted from the clinical notes of 112 patients and curated genetic reports of 176 patients with preprocessed PRO data from 415 patients. © 2017 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

Genetic Epidemiology of Spontaneous Subarachnoid Hemorrhage

DEFF Research Database (Denmark)

Korja, Miikka; Silventoinen, Karri; McCarron, Peter

2010-01-01

and 1 opposite sex) and 492 discordant twin pairs for SAH. The concordance for SAH in monozygotic twins was 3.1% compared with 0.27% in dizygotic twins, suggesting at most a modest role for genetic factors in the etiology of SAH. The population-based probability estimate for SAH in dizygotic siblings...... of a patient with SAH is 0.54%, and only 1 of 185 full siblings experience familial SAH. The corresponding risk of SAH in monozygotic twins is 5.9%. Model-fitting, which was based on the comparison of the few monozygotic and dizygotic pairs, suggested that the estimated heritability of SAH is 41%. CONCLUSIONS...

Rabies in southeast Brazil: a change in the epidemiological pattern.

Science.gov (United States)

Queiroz, Luzia Helena; Favoretto, Silvana Regina; Cunha, Elenice Maria S; Campos, Angélica Cristine A; Lopes, Marissol Cardoso; de Carvalho, Cristiano; Iamamoto, Keila; Araújo, Danielle Bastos; Venditti, Leandro Lima R; Ribeiro, Erica S; Pedro, Wagner André; Durigon, Edison Luiz

2012-01-01

This epidemiological study was conducted using antigenic and genetic characterisation of rabies virus isolates obtained from different animal species in the southeast of Brazil from 1993 to 2007. An alteration in the epidemiological profile was observed. One hundred two samples were tested using a panel of eight monoclonal antibodies, and 94 were genetically characterised by sequencing the nucleoprotein gene. From 1993 to 1997, antigenic variant 2 (AgV-2), related to a rabies virus maintained in dog populations, was responsible for rabies cases in dogs, cats, cattle and horses. Antigenic variant 3 (AgV-3), associated with Desmodus rotundus, was detected in a few cattle samples from rural areas. From 1998 to 2007, rabies virus was detected in bats and urban pets, and four distinct variants were identified. A nucleotide similarity analysis resulted in two primary groups comprising the dog and bat antigenic variants and showing the distinct endemic cycles maintained in the different animal species in this region.

Genetic and systems level analysis of Drosophila sticky/citron kinase and dFmr1 mutants reveals common regulation of genetic networks

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Zarnescu Daniela C

2008-11-01

Full Text Available Abstract Background In Drosophila, the genes sticky and dFmr1 have both been shown to regulate cytoskeletal dynamics and chromatin structure. These genes also genetically interact with Argonaute family microRNA regulators. Furthermore, in mammalian systems, both genes have been implicated in neuronal development. Given these genetic and functional similarities, we tested Drosophila sticky and dFmr1 for a genetic interaction and measured whole genome expression in both mutants to assess similarities in gene regulation. Results We found that sticky mutations can dominantly suppress a dFmr1 gain-of-function phenotype in the developing eye, while phenotypes produced by RNAi knock-down of sticky were enhanced by dFmr1 RNAi and a dFmr1 loss-of-function mutation. We also identified a large number of transcripts that were misexpressed in both mutants suggesting that sticky and dFmr1 gene products similarly regulate gene expression. By integrating gene expression data with a protein-protein interaction network, we found that mutations in sticky and dFmr1 resulted in misexpression of common gene networks, and consequently predicted additional specific phenotypes previously not known to be associated with either gene. Further phenotypic analyses validated these predictions. Conclusion These findings establish a functional link between two previously unrelated genes. Microarray analysis indicates that sticky and dFmr1 are both required for regulation of many developmental genes in a variety of cell types. The diversity of transcripts regulated by these two genes suggests a clear cause of the pleiotropy that sticky and dFmr1 mutants display and provides many novel, testable hypotheses about the functions of these genes. As both of these genes are implicated in the development and function of the mammalian brain, these results have relevance to human health as well as to understanding more general biological processes.

Stability analysis of delayed genetic regulatory networks with stochastic disturbances

Energy Technology Data Exchange (ETDEWEB)

Zhou Qi, E-mail: zhouqilhy@yahoo.com.c [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China); Xu Shengyuan [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China); Chen Bing [Institute of Complexity Science, Qingdao University, Qingdao 266071, Shandong (China); Li Hongyi [Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China); Chu Yuming [Department of Mathematics, Huzhou Teacher' s College, Huzhou 313000, Zhejiang (China)

2009-10-05

This Letter considers the problem of stability analysis of a class of delayed genetic regulatory networks with stochastic disturbances. The delays are assumed to be time-varying and bounded. By utilizing Ito's differential formula and Lyapunov-Krasovskii functionals, delay-range-dependent and rate-dependent (rate-independent) stability criteria are proposed in terms of linear matrices inequalities. An important feature of the proposed results is that all the stability conditions are dependent on the upper and lower bounds of the delays. Another important feature is that the obtained stability conditions are less conservative than certain existing ones in the literature due to introducing some appropriate free-weighting matrices. A simulation example is employed to illustrate the applicability and effectiveness of the proposed methods.

Genetic Algorithms vs. Artificial Neural Networks in Economic Forecasting Process

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Nicolae Morariu

2008-01-01

Full Text Available This paper aims to describe the implementa-tion of a neural network and a genetic algorithm system in order to forecast certain economic indicators of a free market economy. In a free market economy forecasting process precedes the economic planning (a management function, providing important information for the result of the last process. Forecasting represents a starting point in setting of target for a firm, an organization or even a branch of the economy. Thus, the forecasting method used can influence in a significant mode the evolution of an entity. In the following we will describe the forecasting of an economic indicator using two intelligent systems. The difference between the results obtained by this two systems are described in chapter IV.

Reactor Network Synthesis Using Coupled Genetic Algorithm with the Quasi-linear Programming Method

OpenAIRE

Soltani, H.; Shafiei, S.; Edraki, J.

2016-01-01

This research is an attempt to develop a new procedure for the synthesis of reactor networks (RNs) using a genetic algorithm (GA) coupled with the quasi-linear programming (LP) method. The GA is used to produce structural configuration, whereas continuous variables are handled using a quasi-LP formulation for finding the best objective function. Quasi-LP consists of LP together with a search loop to find the best reactor conversions (xi), as well as split and recycle ratios (yi). Quasi-LP rep...

Networks in Later Life: An Examination of Race Differences in Social Support Networks.

Science.gov (United States)

Peek, M. Kristen; O'Neill, Gregory S.

2001-01-01

Considers race differences in the determinants of social support network characteristics using data from Established Populations for Epidemiological Studies of the Elderly. Focuses on the extent to which race differences in network dimensions are present and whether variations can be attributed to social structural positions held. Results indicate…

Genetic Diversity in Natural Populations of New World Leishmania

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Cupolillo Elisa

1998-01-01

Full Text Available Our results have shown the wide diversity of parasites within New World Leishmania. Biochemical and molecular characterization of species within the genus has revealed that much of the population heterogeneity has a genetic basis. The source of genetic diversity among Leishmania appears to arise from predominantly asexual, clonal reproduction, although occasional bouts of sexual reproduction can not be ruled out. Genetic variation is extensive with some clones widely distributed and others seemingly unique and localized to a particular endemic focus. Epidemiological studies of leishmaniasis has been directed to the ecology and dynamics of transmission of Leishmania species/variants, particularly in localized areas. Future research using molecular techniques should aim to identify and follow Leishmania types in nature and correlate genetic typing with important clinical characteristics such as virulence, pathogenicity, drug resistance and antigenic variation. The epidemiological significance of such variation not only has important implications for the control of the leishmaniases, but would also help to elucidate the evolutionary biology of the causative agents.

Epidemiology, molecular epidemiology, and risk factors for renal cell carcinoma

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Chiara Paglino

2011-12-01

Full Text Available Despite only accounting for approximately 2% of all new primary cancer cases, renal cell carcinoma (RCC incidence has dramatically increased over time. Incidence rates vary greatly according to geographic areas, so that it is extremely likely that exogenous risk factors could play an important role in the development of this cancer. Several risk factors have been linked with RCC, including cigarette smoking, obesity, hypertension (and antihypertensive drugs, chronic kidney diseases (also dialysis and transplantation, as well as the use of certain analgesics. Furthermore, although RCC has not generally been considered an occupational cancer, several types of occupationally-derived exposures have been implicated in its pathogenesis. These include exposure to asbestos, chlorinated solvents, gasoline, diesel exhaust fumes, polycyclic aromatic hydrocarbons, printing inks and dyes, cadmium and lead. Finally, families with a predisposition to the development of renal neoplasms were identified and the genes involved discovered and characterized. Therefore, there are now four well-characterized, genetically determined syndromes associated with an increased incidence of kidney tumors, i.e., Von Hippel Lindau (VHL, Hereditary Papillary Renal Carcinoma (HPRC, Birt-Hogg-Dubé Syndrome (BHD, and Hereditary Leiomyomatosis and Renal Cell Cancer (HLRCC. This review will address present knowledge about the epidemiology, molecular epidemiology and risk factors of RCC.

A network security situation prediction model based on wavelet neural network with optimized parameters

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Haibo Zhang

2016-08-01

Full Text Available The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN with optimized parameters by the Improved Niche Genetic Algorithm (INGA. The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN and WNN.

Genetic data from avian influenza and avian paramyxoviruses generated by the European network of excellence (EPIZONE) between 2006 and 2011—Review and recommendations for surveillance

DEFF Research Database (Denmark)

Dundon, William G.; Heidari, Alireza; Fusaro, Alice

2012-01-01

Since 2006, the members of the molecular epidemiological working group of the European “EPIZONE” network of excellence have been generating sequence data on avian influenza and avian paramyxoviruses from both European and African sources in an attempt to more fully understand the circulation...... and impact of these viruses. This review presents a timely update on the epidemiological situation of these viruses based on sequence data generated during the lifetime of this project in addition to data produced by other groups during the same period. Based on this information and putting it all...

Quantum Genetics in terms of Quantum Reversible Automata and Quantum Computation of Genetic Codes and Reverse Transcription

CERN Document Server

Baianu,I C

2004-01-01

The concepts of quantum automata and quantum computation are studied in the context of quantum genetics and genetic networks with nonlinear dynamics. In previous publications (Baianu,1971a, b) the formal concept of quantum automaton and quantum computation, respectively, were introduced and their possible implications for genetic processes and metabolic activities in living cells and organisms were considered. This was followed by a report on quantum and abstract, symbolic computation based on the theory of categories, functors and natural transformations (Baianu,1971b; 1977; 1987; 2004; Baianu et al, 2004). The notions of topological semigroup, quantum automaton, or quantum computer, were then suggested with a view to their potential applications to the analogous simulation of biological systems, and especially genetic activities and nonlinear dynamics in genetic networks. Further, detailed studies of nonlinear dynamics in genetic networks were carried out in categories of n-valued, Lukasiewicz Logic Algebra...

Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

Science.gov (United States)

Peck, Charles C.; Dhawan, Atam P.; Meyer, Claudia M.

1991-01-01

A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.

Detecting Cyber-Attacks on Wireless Mobile Networks Using Multicriterion Fuzzy Classifier with Genetic Attribute Selection

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El-Sayed M. El-Alfy

2015-01-01

Full Text Available With the proliferation of wireless and mobile network infrastructures and capabilities, a wide range of exploitable vulnerabilities emerges due to the use of multivendor and multidomain cross-network services for signaling and transport of Internet- and wireless-based data. Consequently, the rates and types of cyber-attacks have grown considerably and current security countermeasures for protecting information and communication may be no longer sufficient. In this paper, we investigate a novel methodology based on multicriterion decision making and fuzzy classification that can provide a viable second-line of defense for mitigating cyber-attacks. The proposed approach has the advantage of dealing with various types and sizes of attributes related to network traffic such as basic packet headers, content, and time. To increase the effectiveness and construct optimal models, we augmented the proposed approach with a genetic attribute selection strategy. This allows efficient and simpler models which can be replicated at various network components to cooperatively detect and report malicious behaviors. Using three datasets covering a variety of network attacks, the performance enhancements due to the proposed approach are manifested in terms of detection errors and model construction times.

A comparison between genetic algorithms and neural networks for optimizing fuel recharges in BWR

International Nuclear Information System (INIS)

Ortiz J, J.; Requena, I.

2002-01-01

In this work the results of a genetic algorithm (AG) and a neural recurrent multi state network (RNRME) for optimizing the fuel reload of 5 cycles of the Laguna Verde nuclear power plant (CNLV) are presented. The fuel reload obtained by both methods are compared and it was observed that the RNRME creates better fuel distributions that the AG. Moreover a comparison of the utility for using one or another one techniques is make. (Author)

System network planning expansion using mathematical programming, genetic algorithms and tabu search

International Nuclear Information System (INIS)

Sadegheih, A.; Drake, P.R.

2008-01-01

In this paper, system network planning expansion is formulated for mixed integer programming, a genetic algorithm (GA) and tabu search (TS). Compared with other optimization methods, GAs are suitable for traversing large search spaces, since they can do this relatively rapidly and because the use of mutation diverts the method away from local minima, which will tend to become more common as the search space increases in size. GA's give an excellent trade off between solution quality and computing time and flexibility for taking into account specific constraints in real situations. TS has emerged as a new, highly efficient, search paradigm for finding quality solutions to combinatorial problems. It is characterized by gathering knowledge during the search and subsequently profiting from this knowledge. The attractiveness of the technique comes from its ability to escape local optimality. The cost function of this problem consists of the capital investment cost in discrete form, the cost of transmission losses and the power generation costs. The DC load flow equations for the network are embedded in the constraints of the mathematical model to avoid sub-optimal solutions that can arise if the enforcement of such constraints is done in an indirect way. The solution of the model gives the best line additions and also provides information regarding the optimal generation at each generation point. This method of solution is demonstrated on the expansion of a 10 bus bar system to 18 bus bars. Finally, a steady-state genetic algorithm is employed rather than generational replacement, also uniform crossover is used

Profesi Epidemiologi

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Buchari Lapau

2011-01-01

Full Text Available Makalah ini pertama kali menjelaskan perlu adanya profesi kesehatan masyarakat dalam rangka pembangunan kesehatan. Lalu dijelaskan apa profesi itu dan standar keberadaan profesi, atas dasar mana dapat ditetapkan bahwa pelayanan epidemiologi merupakan salah satu profesi. Dalam rangka pembinaan profesi kesehatan masyarakat, IAKMI dan APTKMI telah membentuk Majelis Kolegium Kesehatan Masyarakat Indonesia (MKKMI yang terdiri atas 8 kolegium antara lain Kolegium Epidemiologi, yang telah menyusun Standar Profesi Epidemiologi yang terdiri atas beberapa standar. Masing-masing standar dijelaskan mulai dari kurikulum, standar pelayanan epidmiologi, profil epidemiolog kesehatan, peran epidemiolog kesehatan, fungsi epidemiolog kesehatan, standar kompetensi epidemiologi, dan standar pendidikan profesi epidemiologi.

A national study of the molecular epidemiology of HIV-1 in Australia 2005-2012.

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Alison Castley

Full Text Available Rates of new HIV-1 diagnoses are increasing in Australia, with evidence of an increasing proportion of non-B HIV-1 subtypes reflecting a growing impact of migration and travel. The present study aims to define HIV-1 subtype diversity patterns and investigate possible HIV-1 transmission networks within Australia.The Australian Molecular Epidemiology Network (AMEN HIV collaborating sites in Western Australia, South Australia, Victoria, Queensland and western Sydney (New South Wales, provided baseline HIV-1 partial pol sequence, age and gender information for 4,873 patients who had genotypes performed during 2005-2012. HIV-1 phylogenetic analyses utilised MEGA V6, with a stringent classification of transmission pairs or clusters (bootstrap ≥98%, genetic distance ≤1.5% from at least one other sequence in the cluster.HIV-1 subtype B represented 74.5% of the 4,873 sequences (WA 59%, SA 68.4%, w-Syd 73.8%, Vic 75.6%, Qld 82.1%, with similar proportion of transmission pairs and clusters found in the B and non-B cohorts (23% vs 24.5% of sequences, p = 0.3. Significantly more subtype B clusters were comprised of ≥3 sequences compared with non-B clusters (45.0% vs 24.0%, p = 0.021 and significantly more subtype B pairs and clusters were male-only (88% compared to 53% CRF01_AE and 17% subtype C clusters. Factors associated with being in a cluster of any size included; being sequenced in a more recent time period (p3 was associated with being sequenced in a more recent time period (p = 0.05 and being male (p = 0.008.This nationwide HIV-1 study of 4,873 patient sequences highlights the increased diversity of HIV-1 subtypes within the Australian epidemic, as well as differences in transmission networks associated with these HIV-1 subtypes. These findings provide epidemiological insights not readily available using standard surveillance methods and can inform the development of effective public health strategies in the current paradigm of HIV prevention

A national study of the molecular epidemiology of HIV-1 in Australia 2005-2012.

Science.gov (United States)

Castley, Alison; Sawleshwarkar, Shailendra; Varma, Rick; Herring, Belinda; Thapa, Kiran; Dwyer, Dominic; Chibo, Doris; Nguyen, Nam; Hawke, Karen; Ratcliff, Rodney; Garsia, Roger; Kelleher, Anthony; Nolan, David

2017-01-01

Rates of new HIV-1 diagnoses are increasing in Australia, with evidence of an increasing proportion of non-B HIV-1 subtypes reflecting a growing impact of migration and travel. The present study aims to define HIV-1 subtype diversity patterns and investigate possible HIV-1 transmission networks within Australia. The Australian Molecular Epidemiology Network (AMEN) HIV collaborating sites in Western Australia, South Australia, Victoria, Queensland and western Sydney (New South Wales), provided baseline HIV-1 partial pol sequence, age and gender information for 4,873 patients who had genotypes performed during 2005-2012. HIV-1 phylogenetic analyses utilised MEGA V6, with a stringent classification of transmission pairs or clusters (bootstrap ≥98%, genetic distance ≤1.5% from at least one other sequence in the cluster). HIV-1 subtype B represented 74.5% of the 4,873 sequences (WA 59%, SA 68.4%, w-Syd 73.8%, Vic 75.6%, Qld 82.1%), with similar proportion of transmission pairs and clusters found in the B and non-B cohorts (23% vs 24.5% of sequences, p = 0.3). Significantly more subtype B clusters were comprised of ≥3 sequences compared with non-B clusters (45.0% vs 24.0%, p = 0.021) and significantly more subtype B pairs and clusters were male-only (88% compared to 53% CRF01_AE and 17% subtype C clusters). Factors associated with being in a cluster of any size included; being sequenced in a more recent time period (p3) was associated with being sequenced in a more recent time period (p = 0.05) and being male (p = 0.008). This nationwide HIV-1 study of 4,873 patient sequences highlights the increased diversity of HIV-1 subtypes within the Australian epidemic, as well as differences in transmission networks associated with these HIV-1 subtypes. These findings provide epidemiological insights not readily available using standard surveillance methods and can inform the development of effective public health strategies in the current paradigm of HIV prevention in

Genetic diversity and striatal gene networks: focus on the heterogeneous stock-collaborative cross (HS-CC mouse

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Belknap John

2010-10-01

Full Text Available Abstract Background The current study focused on the extent genetic diversity within a species (Mus musculus affects gene co-expression network structure. To examine this issue, we have created a new mouse resource, a heterogeneous stock (HS formed from the same eight inbred strains that have been used to create the collaborative cross (CC. The eight inbred strains capture > 90% of the genetic diversity available within the species. For contrast with the HS-CC, a C57BL/6J (B6 × DBA/2J (D2 F2 intercross and the HS4, derived from crossing the B6, D2, BALB/cJ and LP/J strains, were used. Brain (striatum gene expression data were obtained using the Illumina Mouse WG 6.1 array, and the data sets were interrogated using a weighted gene co-expression network analysis (WGCNA. Results Genes reliably detected as expressed were similar in all three data sets as was the variability of expression. As measured by the WGCNA, the modular structure of the transcriptome networks was also preserved both on the basis of module assignment and from the perspective of the topological overlap maps. Details of the HS-CC gene modules are provided; essentially identical results were obtained for the HS4 and F2 modules. Gene ontology annotation of the modules revealed a significant overrepresentation in some modules for neuronal processes, e.g., central nervous system development. Integration with known protein-protein interactions data indicated significant enrichment among co-expressed genes. We also noted significant overlap with markers of central nervous system cell types (neurons, oligodendrocytes and astrocytes. Using the Allen Brain Atlas, we found evidence of spatial co-localization within the striatum for several modules. Finally, for some modules it was possible to detect an enrichment of transcription binding sites. The binding site for Wt1, which is associated with neurodegeneration, was the most significantly overrepresented. Conclusions Despite the marked

Optimal Parameter for the Training of Multilayer Perceptron Neural Networks by Using Hierarchical Genetic Algorithm

International Nuclear Information System (INIS)

Orozco-Monteagudo, Maykel; Taboada-Crispi, Alberto; Gutierrez-Hernandez, Liliana

2008-01-01

This paper deals with the controversial topic of the selection of the parameters of a genetic algorithm, in this case hierarchical, used for training of multilayer perceptron neural networks for the binary classification. The parameters to select are the crossover and mutation probabilities of the control and parametric genes and the permanency percent. The results can be considered as a guide for using this kind of algorithm.

Linking healthcare associated norovirus outbreaks: a molecular epidemiologic method for investigating transmission

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Andrews Nick

2006-07-01

Full Text Available Abstract Background Noroviruses are highly infectious pathogens that cause gastroenteritis in the community and in semi-closed institutions such as hospitals. During outbreaks, multiple units within a hospital are often affected, and a major question for control programs is: are the affected units part of the same outbreak or are they unrelated transmission events? In practice, investigators often assume a transmission link based on epidemiological observations, rather than a systematic approach to tracing transmission. Here, we present a combined molecular and statistical method for assessing: 1 whether observed clusters provide evidence of local transmission and 2 the probability that anecdotally|linked outbreaks truly shared a transmission event. Methods 76 healthcare associated outbreaks were observed in an active and prospective surveillance scheme of 15 hospitals in the county of Avon, England from April 2002 to March 2003. Viral RNA from 64 out of 76 specimens from distinct outbreaks was amplified by reverse transcription-PCR and was sequenced in the polymerase (ORF 1 and capsid (ORF 2 regions. The genetic diversity, at the nucleotide level, was analysed in relation to the epidemiological patterns. Results Two out of four genetic and epidemiological clusters of outbreaks were unlikely to have occurred by chance alone, thus suggesting local transmission. There was anecdotal epidemiological evidence of a transmission link among 5 outbreaks pairs. By combining this epidemiological observation with viral sequence data, the evidence of a link remained convincing in 3 of these pairs. These results are sensitive to prior beliefs of the strength of epidemiological evidence especially when the outbreak strains are common in the background population. Conclusion The evidence suggests that transmission between hospitals units does occur. Using the proposed criteria, certain hypothesized transmission links between outbreaks were supported while

Practical mine ventilation optimization based on genetic algorithms for free splitting networks

Energy Technology Data Exchange (ETDEWEB)

Acuna, E.; Maynard, R.; Hall, S. [Laurentian Univ., Sudbury, ON (Canada). Mirarco Mining Innovation; Hardcastle, S.G.; Li, G. [Natural Resources Canada, Sudbury, ON (Canada). CANMET Mining and Mineral Sciences Laboratories; Lowndes, I.S. [Nottingham Univ., Nottingham (United Kingdom). Process and Environmental Research Division; Tonnos, A. [Bestech, Sudbury, ON (Canada)

2010-07-01

The method used to optimize the design and operation of mine ventilation has generally been based on case studies and expert knowledge. It has yet to benefit from optimization techniques used and proven in other fields of engineering. Currently, optimization of mine ventilation systems is a manual based decision process performed by an experienced mine ventilation specialist assisted by commercial ventilation distribution solvers. These analysis tools are widely used in the mining industry to evaluate the practical and economic viability of alternative ventilation system configurations. The scenario which is usually selected is the one that reports the lowest energy consumption while delivering the required airflow distribution. Since most commercial solvers do not have an integrated optimization algorithm network, the process of generating a series of potential ventilation solutions using the conventional iterative design strategy can be time consuming. For that reason, a genetic algorithm (GA) optimization routine was developed in combination with a ventilation solver to determine the potential optimal solutions of a primary mine ventilation system based on a free splitting network. The optimization method was used in a small size mine ventilation network. The technique was shown to have the capacity to generate good feasible solutions and improve upon the manual results obtained by mine ventilation specialists. 9 refs., 7 tabs., 3 figs.

Application of a Genetic Algorithm and a Neural Network for the Discovery and Optimization of New Solid Catalytic Materials

Czech Academy of Sciences Publication Activity Database

Rodemerck, U.; Baerns, M.; Holeňa, Martin; Wolf, D.

2004-01-01

Roč. 223, - (2004), s. 168-174 ISSN 0169-4332 Institutional research plan: CEZ:AV0Z1030915 Keywords : genetic algorithm * neural network * catalytic materials Subject RIV: BA - General Mathematics Impact factor: 1.497, year: 2004

A new optimization framework using genetic algorithm and artificial neural network to reduce uncertainties in petroleum reservoir models

Science.gov (United States)

Maschio, Célio; José Schiozer, Denis

2015-01-01

In this article, a new optimization framework to reduce uncertainties in petroleum reservoir attributes using artificial intelligence techniques (neural network and genetic algorithm) is proposed. Instead of using the deterministic values of the reservoir properties, as in a conventional process, the parameters of the probability density function of each uncertain attribute are set as design variables in an optimization process using a genetic algorithm. The objective function (OF) is based on the misfit of a set of models, sampled from the probability density function, and a symmetry factor (which represents the distribution of curves around the history) is used as weight in the OF. Artificial neural networks are trained to represent the production curves of each well and the proxy models generated are used to evaluate the OF in the optimization process. The proposed method was applied to a reservoir with 16 uncertain attributes and promising results were obtained.

Intra-species diversity and epidemiology varies among coagulase-negative Staphylococcus species causing bovine intramammary infections.

Science.gov (United States)

Piessens, V; De Vliegher, S; Verbist, B; Braem, G; Van Nuffel, A; De Vuyst, L; Heyndrickx, M; Van Coillie, E

2012-02-24

Although many studies report coagulase-negative staphylococci (CNS) as the predominant cause of subclinical bovine mastitis, their epidemiology is poorly understood. In the current study, the genetic diversity within four CNS species frequently associated with bovine intramammary infections, Staphylococcus haemolyticus, S. simulans, S. chromogenes, and S. epidermidis, was determined. For epidemiological purposes, CNS genotypes recovered from bovine milk collected on six Flemish dairy farms were compared with those from the farm environment, and their distribution within the farms was investigated. Genetic diversity was assessed by two molecular typing techniques, amplification fragment length polymorphism (AFLP) and random amplification of polymorphic DNA (RAPD) analysis. Subtyping revealed the highest genetic heterogeneity among S. haemolyticus isolates. A large variety of genotypes was found among environmental isolates, of which several could be linked with intramammary infection, indicating that the environment could act as a potential source for infection. For S. simulans, various genotypes were found in the environment, but a link with IMI was less obvious. For S. epidermidis and S. chromogenes, genetic heterogeneity was limited and the sporadic isolates from environment displayed largely the same genotypes as those from milk. The higher clonality of the S. epidermidis and S. chromogenes isolates from milk suggests that specific genotypes probably disseminate within herds and are more udder-adapted. Environmental sources and cow-to-cow transmission both seem to be involved in the epidemiology of CNS, although their relative importance might substantially vary between species. Copyright © 2011 Elsevier B.V. All rights reserved.

Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations.

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Xiaodong Cai

Full Text Available Integrating genetic perturbations with gene expression data not only improves accuracy of regulatory network topology inference, but also enables learning of causal regulatory relations between genes. Although a number of methods have been developed to integrate both types of data, the desiderata of efficient and powerful algorithms still remains. In this paper, sparse structural equation models (SEMs are employed to integrate both gene expression data and cis-expression quantitative trait loci (cis-eQTL, for modeling gene regulatory networks in accordance with biological evidence about genes regulating or being regulated by a small number of genes. A systematic inference method named sparsity-aware maximum likelihood (SML is developed for SEM estimation. Using simulated directed acyclic or cyclic networks, the SML performance is compared with that of two state-of-the-art algorithms: the adaptive Lasso (AL based scheme, and the QTL-directed dependency graph (QDG method. Computer simulations demonstrate that the novel SML algorithm offers significantly better performance than the AL-based and QDG algorithms across all sample sizes from 100 to 1,000, in terms of detection power and false discovery rate, in all the cases tested that include acyclic or cyclic networks of 10, 30 and 300 genes. The SML method is further applied to infer a network of 39 human genes that are related to the immune function and are chosen to have a reliable eQTL per gene. The resulting network consists of 9 genes and 13 edges. Most of the edges represent interactions reasonably expected from experimental evidence, while the remaining may just indicate the emergence of new interactions. The sparse SEM and efficient SML algorithm provide an effective means of exploiting both gene expression and perturbation data to infer gene regulatory networks. An open-source computer program implementing the SML algorithm is freely available upon request.

The epidemiological characteristics and genetic diversity of dengue virus during the third largest historical outbreak of dengue in Guangdong, China, in 2014.

Science.gov (United States)

Sun, Jiufeng; Wu, De; Zhou, Huiqiong; Zhang, Huan; Guan, Dawei; He, Xiang; Cai, Songwu; Ke, Changwen; Lin, Jinyan

2016-01-01

The third largest historical outbreak of dengue occurred during July to December 2014, in 20 of 21 cities of Guangdong, China. The epidemiological and molecular characteristics of the introduction, expansion and phylogeny of the DENV isolates involved in this outbreak were investigated. A combination analyses of epidemiological characteristics and genetic diversity of dengue virus was performed in this study. In total, 45,236 cases and 6 fatalities were reported. Unemployed individuals, retirees and retailers were the most affected populations. A total of 6024 cases were verified to have DENV infections by nucleic acid detection, of which 5947, 74 and 3 were confirmed to have DENV-1, -2, and -3 infections, respectively. Phylogenetic analyses of DENV-1 isolates were assigned into three genotypes (I, IV, and V). Genotype V was the predominant genotype that likely originated from Singapore. The DENV-2 isolates were assigned to the Cosmopolitan and Asian I genotypes. A unique DENV-3 isolate (genotype III) shared high similarity with isolates obtained from Guangdong in 2013. A combination analyses demonstrated the multiple geographical origins of this outbreak, and highlight the importance of early detection, the case management and vector surveillance for preventing further dengue epidemics in Guangdong. Copyright © 2015 The British Infection Association. Published by Elsevier Ltd. All rights reserved.

Invited commentary: genetic variants and individual- and societal-level risk factors.

Science.gov (United States)

Coughlin, Steven S

2010-01-01

Over the past decade, leading epidemiologists have noted the importance of social factors in studying and understanding the distribution and determinants of disease in human populations; but to what extent are epidemiologic studies integrating genetic information and other biologic variables with information about individual-level risk factors and group-level or societal factors related to the broader residential, behavioral, or cultural context? There remains a need to consider ways to integrate genetic information with social and contextual information in epidemiologic studies, partly to combat the overemphasis on the importance of genetic factors as determinants of disease in human populations. Even in genome-wide association studies of coronary heart disease and other common complex diseases, only a small proportion of heritability is explained by the genetic variants identified to date. It is possible that familial clustering due to genetic factors has been overestimated and that important environmental or social influences (acting alone or in combination with genetic variants) have been overlooked. The accompanying article by Bressler et al. (Am J Epidemiol. 2010;171(1):14-23) highlights some of these important issues.

Role of genomic typing in taxonomy, evolutionary genetics, and microbial epidemiology.

NARCIS (Netherlands)

A.F. van Belkum (Alex); M. Struelens; A. de Visser (Arjan); H.A. Verbrugh (Henri); M. Tibayrench

2001-01-01

textabstractCurrently, genetic typing of microorganisms is widely used in several major fields of microbiological research. Taxonomy, research aimed at elucidation of evolutionary dynamics or phylogenetic relationships, population genetics of microorganisms, and

An interaction map of circulating metabolites, immune gene networks, and their genetic regulation.

Science.gov (United States)

Nath, Artika P; Ritchie, Scott C; Byars, Sean G; Fearnley, Liam G; Havulinna, Aki S; Joensuu, Anni; Kangas, Antti J; Soininen, Pasi; Wennerström, Annika; Milani, Lili; Metspalu, Andres; Männistö, Satu; Würtz, Peter; Kettunen, Johannes; Raitoharju, Emma; Kähönen, Mika; Juonala, Markus; Palotie, Aarno; Ala-Korpela, Mika; Ripatti, Samuli; Lehtimäki, Terho; Abraham, Gad; Raitakari, Olli; Salomaa, Veikko; Perola, Markus; Inouye, Michael

2017-08-01

Immunometabolism plays a central role in many cardiometabolic diseases. However, a robust map of immune-related gene networks in circulating human cells, their interactions with metabolites, and their genetic control is still lacking. Here, we integrate blood transcriptomic, metabolomic, and genomic profiles from two population-based cohorts (total N = 2168), including a subset of individuals with matched multi-omic data at 7-year follow-up. We identify topologically replicable gene networks enriched for diverse immune functions including cytotoxicity, viral response, B cell, platelet, neutrophil, and mast cell/basophil activity. These immune gene modules show complex patterns of association with 158 circulating metabolites, including lipoprotein subclasses, lipids, fatty acids, amino acids, small molecules, and CRP. Genome-wide scans for module expression quantitative trait loci (mQTLs) reveal five modules with mQTLs that have both cis and trans effects. The strongest mQTL is in ARHGEF3 (rs1354034) and affects a module enriched for platelet function, independent of platelet counts. Modules of mast cell/basophil and neutrophil function show temporally stable metabolite associations over 7-year follow-up, providing evidence that these modules and their constituent gene products may play central roles in metabolic inflammation. Furthermore, the strongest mQTL in ARHGEF3 also displays clear temporal stability, supporting widespread trans effects at this locus. This study provides a detailed map of natural variation at the blood immunometabolic interface and its genetic basis, and may facilitate subsequent studies to explain inter-individual variation in cardiometabolic disease.

INTEGRATING GENETIC AND STRUCTURAL DATA ON HUMAN PROTEIN KINOME IN NETWORK-BASED MODELING OF KINASE SENSITIVITIES AND RESISTANCE TO TARGETED AND PERSONALIZED ANTICANCER DRUGS.

Science.gov (United States)

Verkhivker, Gennady M

2016-01-01

The human protein kinome presents one of the largest protein families that orchestrate functional processes in complex cellular networks, and when perturbed, can cause various cancers. The abundance and diversity of genetic, structural, and biochemical data underlies the complexity of mechanisms by which targeted and personalized drugs can combat mutational profiles in protein kinases. Coupled with the evolution of system biology approaches, genomic and proteomic technologies are rapidly identifying and charactering novel resistance mechanisms with the goal to inform rationale design of personalized kinase drugs. Integration of experimental and computational approaches can help to bring these data into a unified conceptual framework and develop robust models for predicting the clinical drug resistance. In the current study, we employ a battery of synergistic computational approaches that integrate genetic, evolutionary, biochemical, and structural data to characterize the effect of cancer mutations in protein kinases. We provide a detailed structural classification and analysis of genetic signatures associated with oncogenic mutations. By integrating genetic and structural data, we employ network modeling to dissect mechanisms of kinase drug sensitivities to oncogenic EGFR mutations. Using biophysical simulations and analysis of protein structure networks, we show that conformational-specific drug binding of Lapatinib may elicit resistant mutations in the EGFR kinase that are linked with the ligand-mediated changes in the residue interaction networks and global network properties of key residues that are responsible for structural stability of specific functional states. A strong network dependency on high centrality residues in the conformation-specific Lapatinib-EGFR complex may explain vulnerability of drug binding to a broad spectrum of mutations and the emergence of drug resistance. Our study offers a systems-based perspective on drug design by unravelling

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

Directory of Open Access Journals (Sweden)

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.

Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits.

Science.gov (United States)

Shi, Huwenbo; Mancuso, Nicholas; Spendlove, Sarah; Pasaniuc, Bogdan

2017-11-02

Although genetic correlations between complex traits provide valuable insights into epidemiological and etiological studies, a precise quantification of which genomic regions disproportionately contribute to the genome-wide correlation is currently lacking. Here, we introduce ρ-HESS, a technique to quantify the correlation between pairs of traits due to genetic variation at a small region in the genome. Our approach requires GWAS summary data only and makes no distributional assumption on the causal variant effect sizes while accounting for linkage disequilibrium (LD) and overlapping GWAS samples. We analyzed large-scale GWAS summary data across 36 quantitative traits, and identified 25 genomic regions that contribute significantly to the genetic correlation among these traits. Notably, we find 6 genomic regions that contribute to the genetic correlation of 10 pairs of traits that show negligible genome-wide correlation, further showcasing the power of local genetic correlation analyses. Finally, we report the distribution of local genetic correlations across the genome for 55 pairs of traits that show putative causal relationships. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

A genetic-epidemiologic study of Alzheimer’s disease

NARCIS (Netherlands)

A. Arias-Vásquez (Alejandro)

2006-01-01

textabstractAlzheimer's disease (AD) is the most frequent cause of dementia and thus is a major public-health problem. Age and genetic predisposition to the disease are the most important risk factors. In 2001 more than 24 million people in the western world had dementia. This number is expected to

A proposal to establish an international network in molecular microbiology and genetic engineering for scientific cooperation and prevention of misuse of biological sciences in the framework of science for peace

International Nuclear Information System (INIS)

Becker, Y.

1998-01-01

The conference on 'Science and Technology for Construction of Peace' which was organized by the Landau Network Coordination Center and A. Volta Center for Scientific Culture dealt with conversion of military and technological capacities into sustainable civilian application. The ideas regarding the conversion of nuclear warheads into nuclear energy for civilian-use led to the idea that the extension of this trend of thought to molecular biology and genetic engineering, will be a useful contribution to Science for Peace. This idea of developing a Cooperation Network in Molecular Biology and Genetic Engineering that will function parallel to and with the Landau Network Coordination in the 'A. Volta' Center was discussed in the Second International Symposium on Science for Peace, Jerusalem, January 1997. It is the reason for the inclusion of the biological aspects in the deliberations of our Forum. It is hoped that the establishment of an international network in molecular biology and genetic engineering, similar to the Landau Network in physics, will support and achieve the decommissioning of biological weapons. Such a network in microbiology and genetic engineering will contribute to the elimination of biological weapons and to contributions to Science for Peace and to Culture of Peace activities of UNESCO. (author)

Beam-column joint shear prediction using hybridized deep learning neural network with genetic algorithm

Science.gov (United States)

Mundher Yaseen, Zaher; Abdulmohsin Afan, Haitham; Tran, Minh-Tung

2018-04-01

Scientifically evidenced that beam-column joints are a critical point in the reinforced concrete (RC) structure under the fluctuation loads effects. In this novel hybrid data-intelligence model developed to predict the joint shear behavior of exterior beam-column structure frame. The hybrid data-intelligence model is called genetic algorithm integrated with deep learning neural network model (GA-DLNN). The genetic algorithm is used as prior modelling phase for the input approximation whereas the DLNN predictive model is used for the prediction phase. To demonstrate this structural problem, experimental data is collected from the literature that defined the dimensional and specimens’ properties. The attained findings evidenced the efficitveness of the hybrid GA-DLNN in modelling beam-column joint shear problem. In addition, the accurate prediction achived with less input variables owing to the feasibility of the evolutionary phase.

A national study of the molecular epidemiology of HIV-1 in Australia 2005–2012

Science.gov (United States)

Castley, Alison; Sawleshwarkar, Shailendra; Varma, Rick; Herring, Belinda; Thapa, Kiran; Dwyer, Dominic; Chibo, Doris; Nguyen, Nam; Hawke, Karen; Ratcliff, Rodney; Garsia, Roger; Kelleher, Anthony; Nolan, David

2017-01-01

Introduction Rates of new HIV-1 diagnoses are increasing in Australia, with evidence of an increasing proportion of non-B HIV-1 subtypes reflecting a growing impact of migration and travel. The present study aims to define HIV-1 subtype diversity patterns and investigate possible HIV-1 transmission networks within Australia. Methods The Australian Molecular Epidemiology Network (AMEN) HIV collaborating sites in Western Australia, South Australia, Victoria, Queensland and western Sydney (New South Wales), provided baseline HIV-1 partial pol sequence, age and gender information for 4,873 patients who had genotypes performed during 2005–2012. HIV-1 phylogenetic analyses utilised MEGA V6, with a stringent classification of transmission pairs or clusters (bootstrap ≥98%, genetic distance ≤1.5% from at least one other sequence in the cluster). Results HIV-1 subtype B represented 74.5% of the 4,873 sequences (WA 59%, SA 68.4%, w-Syd 73.8%, Vic 75.6%, Qld 82.1%), with similar proportion of transmission pairs and clusters found in the B and non-B cohorts (23% vs 24.5% of sequences, p = 0.3). Significantly more subtype B clusters were comprised of ≥3 sequences compared with non-B clusters (45.0% vs 24.0%, p = 0.021) and significantly more subtype B pairs and clusters were male-only (88% compared to 53% CRF01_AE and 17% subtype C clusters). Factors associated with being in a cluster of any size included; being sequenced in a more recent time period (p3) was associated with being sequenced in a more recent time period (p = 0.05) and being male (p = 0.008). Conclusion This nationwide HIV-1 study of 4,873 patient sequences highlights the increased diversity of HIV-1 subtypes within the Australian epidemic, as well as differences in transmission networks associated with these HIV-1 subtypes. These findings provide epidemiological insights not readily available using standard surveillance methods and can inform the development of effective public health strategies in the

[The genetics of addictions].

Science.gov (United States)

Ibañez Cuadrado, Angela

2008-01-01

The addictions are common chronic psychiatric diseases which represent a serious worldwide public-health problem. They have a high prevalence and negative effects at individual, family and societal level, with a high sanitary cost. Epidemiological genetic research has revealed that addictions are moderately to highly heritable. Also the investigation has evidenced that environmental and genetic factors contribute to individual differences in vulnerability to addictions. Advances in the neurobiology of addiction joined to the development of new molecular genetic technologies, have led to the identification of a variety of underlying genes and pathways in addiction process, leading to the description of common molecular mechanisms in substance and behaviour dependencies. Identifying gene-environment interactions is a crucial issue in future research. Other major goal in genetic research is the identification of new therapeutic targets for treatment and prevention.

Genetic prediction of type 2 diabetes using deep neural network.

Science.gov (United States)

Kim, J; Kim, J; Kwak, M J; Bajaj, M

2018-04-01

Type 2 diabetes (T2DM) has strong heritability but genetic models to explain heritability have been challenging. We tested deep neural network (DNN) to predict T2DM using the nested case-control study of Nurses' Health Study (3326 females, 45.6% T2DM) and Health Professionals Follow-up Study (2502 males, 46.5% T2DM). We selected 96, 214, 399, and 678 single-nucleotide polymorphism (SNPs) through Fisher's exact test and L1-penalized logistic regression. We split each dataset randomly in 4:1 to train prediction models and test their performance. DNN and logistic regressions showed better area under the curve (AUC) of ROC curves than the clinical model when 399 or more SNPs included. DNN was superior than logistic regressions in AUC with 399 or more SNPs in male and 678 SNPs in female. Addition of clinical factors consistently increased AUC of DNN but failed to improve logistic regressions with 214 or more SNPs. In conclusion, we show that DNN can be a versatile tool to predict T2DM incorporating large numbers of SNPs and clinical information. Limitations include a relatively small number of the subjects mostly of European ethnicity. Further studies are warranted to confirm and improve performance of genetic prediction models using DNN in different ethnic groups. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Event-based cluster synchronization of coupled genetic regulatory networks

Science.gov (United States)

Yue, Dandan; Guan, Zhi-Hong; Li, Tao; Liao, Rui-Quan; Liu, Feng; Lai, Qiang

2017-09-01

In this paper, the cluster synchronization of coupled genetic regulatory networks with a directed topology is studied by using the event-based strategy and pinning control. An event-triggered condition with a threshold consisting of the neighbors' discrete states at their own event time instants and a state-independent exponential decay function is proposed. The intra-cluster states information and extra-cluster states information are involved in the threshold in different ways. By using the Lyapunov function approach and the theories of matrices and inequalities, we establish the cluster synchronization criterion. It is shown that both the avoidance of continuous transmission of information and the exclusion of the Zeno behavior are ensured under the presented triggering condition. Explicit conditions on the parameters in the threshold are obtained for synchronization. The stability criterion of a single GRN is also given under the reduced triggering condition. Numerical examples are provided to validate the theoretical results.

Investigation of genetic diversity and epidemiological characteristics of Pasteurella multocida isolates from poultry in southwest China by population structure, multi-locus sequence typing and virulence-associated gene profile analysis.

Science.gov (United States)

Li, Zhangcheng; Cheng, Fangjun; Lan, Shimei; Guo, Jianhua; Liu, Wei; Li, Xiaoyan; Luo, Zeli; Zhang, Manli; Wu, Juan; Shi, Yang

2018-04-25

Fowl cholera caused by Pasteurella multocida has always been a disease of global importance for poultry production. The aim of this study was to obtain more information about the epidemiology of avian P. multocida infection in southwest China and the genetic characteristics of clinical isolates. P. multocida isolates were characterized by biochemical and molecular-biological methods. The distributions of the capsular serogroups, the phenotypic antimicrobial resistance profiles, lipopolysaccharide (LPS) genotyping and the presence of 19 virulence genes were investigated in 45 isolates of P. multocida that were associated with clinical disease in poultry. The genetic diversity of P. multocida strains was performed by 16S rRNA and rpoB gene sequence analysis as well as multilocus sequence typing (MLST). The results showed that most (80.0%) of the P. multocida isolates in this study represented special P. multocida subspecies, and 71.1% of the isolates showed multiple-drug resistance. 45 isolates belonged to capsular types: A (100%) and two LPS genotypes: L1 (95.6%) and L3 (4.4%). MLST revealed two new alleles (pmi77 and gdh57) and one new sequence type (ST342). ST129 types dominated in 45 P. multocida isolates. Isolates belonging to ST129 were with the genes ompH+plpB+ptfA+tonB, whereas ST342 included isolates with fur+hgbA+tonB genes. Population genetic analysis and the MLST results revealed that at least one new ST genotype was present in the avian P. multocida in China. These findings provide novel insights into the epidemiological characteristics of avian P. multocida isolates in southwest China.

Global epidemiology of phytoplasma diseases of economic importance in Southeast Europe

OpenAIRE

Mitrev, Sasa

2007-01-01

The network ‘Global epidemiology of phytoplasma diseases of economic importance in Southeast Europe’ will coordinate the efforts of plant pathologists, microbiologists and entomologists of Southeast European countries to better monitor phytoplasma strains propagation through nurseries and insect vectors, at the European scale. This will be investigated both in plants and insects using up to date molecular typing tools and real-time PCR detection technology. In addition, the network will initi...

Virophages, polintons, and transpovirons: a complex evolutionary network of diverse selfish genetic elements with different reproduction strategies.

Science.gov (United States)

Yutin, Natalya; Raoult, Didier; Koonin, Eugene V

2013-05-23

Recent advances of genomics and metagenomics reveal remarkable diversity of viruses and other selfish genetic elements. In particular, giant viruses have been shown to possess their own mobilomes that include virophages, small viruses that parasitize on giant viruses of the Mimiviridae family, and transpovirons, distinct linear plasmids. One of the virophages known as the Mavirus, a parasite of the giant Cafeteria roenbergensis virus, shares several genes with large eukaryotic self-replicating transposon of the Polinton (Maverick) family, and it has been proposed that the polintons evolved from a Mavirus-like ancestor. We performed a comprehensive phylogenomic analysis of the available genomes of virophages and traced the evolutionary connections between the virophages and other selfish genetic elements. The comparison of the gene composition and genome organization of the virophages reveals 6 conserved, core genes that are organized in partially conserved arrays. Phylogenetic analysis of those core virophage genes, for which a sufficient diversity of homologs outside the virophages was detected, including the maturation protease and the packaging ATPase, supports the monophyly of the virophages. The results of this analysis appear incompatible with the origin of polintons from a Mavirus-like agent but rather suggest that Mavirus evolved through recombination between a polinton and an unknown virus. Altogether, virophages, polintons, a distinct Tetrahymena transposable element Tlr1, transpovirons, adenoviruses, and some bacteriophages form a network of evolutionary relationships that is held together by overlapping sets of shared genes and appears to represent a distinct module in the vast total network of viruses and mobile elements. The results of the phylogenomic analysis of the virophages and related genetic elements are compatible with the concept of network-like evolution of the virus world and emphasize multiple evolutionary connections between bona fide

Global epidemiology of hyperthyroidism and hypothyroidism.

Science.gov (United States)

Taylor, Peter N; Albrecht, Diana; Scholz, Anna; Gutierrez-Buey, Gala; Lazarus, John H; Dayan, Colin M; Okosieme, Onyebuchi E

2018-05-01

Thyroid hormones are essential for growth, neuronal development, reproduction and regulation of energy metabolism. Hypothyroidism and hyperthyroidism are common conditions with potentially devastating health consequences that affect all populations worldwide. Iodine nutrition is a key determinant of thyroid disease risk; however, other factors, such as ageing, smoking status, genetic susceptibility, ethnicity, endocrine disruptors and the advent of novel therapeutics, including immune checkpoint inhibitors, also influence thyroid disease epidemiology. In the developed world, the prevalence of undiagnosed thyroid disease is likely falling owing to widespread thyroid function testing and relatively low thresholds for treatment initiation. However, continued vigilance against iodine deficiency remains essential in developed countries, particularly in Europe. In this report, we review the global incidence and prevalence of hyperthyroidism and hypothyroidism, highlighting geographical differences and the effect of environmental factors, such as iodine supplementation, on these data. We also highlight the pressing need for detailed epidemiological surveys of thyroid dysfunction and iodine status in developing countries.

Design and feasibility of an international study assessing the prevalence of contact allergy to fragrances in the general population: the European Dermato-Epidemiology Network Fragrance Study

OpenAIRE

Rossi, M; Coenraads, PJ; Diepgen, T; Svensson, A; Elsner, P; Gonçalo, Margarida; Bruze, M; Naldi, L

2010-01-01

Background/Aims: Data on contact allergy to fragrances in the general population are limited. Data from allergological services suggest that the frequency of contact allergy to fragrances is increasing. The European Dermato-Epidemiology Network (EDEN) Fragrance Study aims to obtain reliable data on the prevalence of contact allergy to fragrances and other sensitizers of the European baseline series, in the general population of different geographical areas of Europe. We report the methodology...

Epidemiologic research program: Selected bibliography

International Nuclear Information System (INIS)

1993-05-01

This bibliography is a current listing of scientific reports from epidemiologic and related activities sponsored by the Department of Energy. The Office of Epidemiology and Health Surveillance now is the departmental focal point for these activities and any others relating to the study of human health effects. The Office's mission is evolving to encompass the new role of the Department in environmental restoration, weapons dismantlement and nuclear material storage, and development of new energy technologies. Publications in these areas will be included in future editions of the bibliography. The present edition brings the listing up to date, and should facilitate access to specific reports. The program has been divided into several general areas of activity: the Radiation Effects Research Foundation, which supports studies of survivors of the atomic weapons in Hiroshima and Nagasaki; mortality and morbidity studies of DOE workers; studies on internally deposited alpha emitters; medical/histologic studies; studies on the genetic aspects of radiation damage; community health surveillance studies; and the development of computational techniques and of databases to make the results as widely useful as possible

Classification of Atrial Septal Defect and Ventricular Septal Defect with Documented Hemodynamic Parameters via Cardiac Catheterization by Genetic Algorithms and Multi-Layered Artificial Neural Network

Directory of Open Access Journals (Sweden)

Mustafa Yıldız

2012-08-01

Full Text Available Introduction: We aimed to develop a classification method to discriminate ventricular septal defect and atrial septal defect by using severalhemodynamic parameters.Patients and Methods: Forty three patients (30 atrial septal defect, 13 ventricular septal defect; 26 female, 17 male with documentedhemodynamic parameters via cardiac catheterization are included to study. Such parameters as blood pressure values of different areas,gender, age and Qp/Qs ratios are used for classification. Parameters, we used in classification are determined by divergence analysismethod. Those parameters are; i pulmonary artery diastolic pressure, ii Qp/Qs ratio, iii right atrium pressure, iv age, v pulmonary arterysystolic pressure, vi left ventricular sistolic pressure, vii aorta mean pressure, viii left ventricular diastolic pressure, ix aorta diastolicpressure, x aorta systolic pressure. Those parameters detected from our study population, are uploaded to multi-layered artificial neuralnetwork and the network was trained by genetic algorithm.Results: Trained cluster consists of 14 factors (7 atrial septal defect and 7 ventricular septal defect. Overall success ratio is 79.2%, andwith a proper instruction of artificial neural network this ratio increases up to 89%.Conclusion: Parameters, belonging to artificial neural network, which are needed to be detected by the investigator in classical methods,can easily be detected with the help of genetic algorithms. During the instruction of artificial neural network by genetic algorithms, boththe topology of network and factors of network can be determined. During the test stage, elements, not included in instruction cluster, areassumed as in test cluster, and as a result of this study, we observed that multi-layered artificial neural network can be instructed properly,and neural network is a successful method for aimed classification.

Optimization of China Crude Oil Transportation Network with Genetic Ant Colony Algorithm

Directory of Open Access Journals (Sweden)

Yao Wang

2015-08-01

Full Text Available Taking into consideration both shipping and pipeline transport, this paper first analysed the risk factors for different modes of crude oil import transportation. Then, based on the minimum of both transportation cost and overall risk, a multi-objective programming model was established to optimize the transportation network of crude oil import, and the genetic algorithm and ant colony algorithm were employed to solve the problem. The optimized result shows that VLCC (Very Large Crude Carrier is superior in long distance sea transportation, whereas pipeline transport is more secure than sea transport. Finally, this paper provides related safeguard suggestions on crude oil import transportation.

Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system

Energy Technology Data Exchange (ETDEWEB)

Li, Kangji [Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027 (China); School of Electricity Information Engineering, Jiangsu University, Zhenjiang 212013 (China); Su, Hongye [Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027 (China)

2010-11-15

There are several ways to forecast building energy consumption, varying from simple regression to models based on physical principles. In this paper, a new method, namely, the hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system (GA-HANFIS) model is developed. In this model, hierarchical structure decreases the rule base dimension. Both clustering and rule base parameters are optimized by GAs and neural networks (NNs). The model is applied to predict a hotel's daily air conditioning consumption for a period over 3 months. The results obtained by the proposed model are presented and compared with regular method of NNs, which indicates that GA-HANFIS model possesses better performance than NNs in terms of their forecasting accuracy. (author)

Safety assessment, detection and traceability, and societal aspects of genetically modified foods. European Network on Safety Assessment of Genetically Modified Food Crops (ENTRANSFOOD). Concluding remarks.

Science.gov (United States)

Kuiper, H A; König, A; Kleter, G A; Hammes, W P; Knudsen, I

2004-07-01

The most important results from the EU-sponsored ENTRANSFOOD Thematic Network project are reviewed, including the design of a detailed step-wise procedure for the risk assessment of foods derived from genetically modified crops based on the latest scientific developments, evaluation of topical risk assessment issues, and the formulation of proposals for improved risk management and public involvement in the risk analysis process. Copyright 2004 Elsevier Ltd.

Some aspects of cancer epidemiology

International Nuclear Information System (INIS)

Lilienfeld, A.M.

1982-01-01

Epidemiolgic studies have strongly suggested that a vast majority (80-90%) of cancers are caused by radiation, chemical and biologic agents; the remainder result from endogenous or genetic factors. Biologically, cancer is most probably the end result of a complex multistage process and therefore may be due to a sequence of exposures to different agents at each of these stages. This emphasizes the need to stress the study of interactions in epidemiologic studies to a greater extent than has been done thus far. Examples of the importance of interactions in several types of cancer are presented

HIV-TRACE (Transmission Cluster Engine): a tool for large scale molecular epidemiology of HIV-1 and other rapidly evolving pathogens.

Science.gov (United States)

Kosakovsky Pond, Sergei L; Weaver, Steven; Leigh Brown, Andrew J; Wertheim, Joel O

2018-01-31

In modern applications of molecular epidemiology, genetic sequence data are routinely used to identify clusters of transmission in rapidly evolving pathogens, most notably HIV-1. Traditional 'shoeleather' epidemiology infers transmission clusters by tracing chains of partners sharing epidemiological connections (e.g., sexual contact). Here, we present a computational tool for identifying a molecular transmission analog of such clusters: HIV-TRACE (TRAnsmission Cluster Engine). HIV-TRACE implements an approach inspired by traditional epidemiology, by identifying chains of partners whose viral genetic relatedness imply direct or indirect epidemiological connections. Molecular transmission clusters are constructed using codon-aware pairwise alignment to a reference sequence followed by pairwise genetic distance estimation among all sequences. This approach is computationally tractable and is capable of identifying HIV-1 transmission clusters in large surveillance databases comprising tens or hundreds of thousands of sequences in near real time, i.e., on the order of minutes to hours. HIV-TRACE is available at www.hivtrace.org and from github.com/veg/hivtrace, along with the accompanying result visualization module from github.com/veg/hivtrace-viz. Importantly, the approach underlying HIV-TRACE is not limited to the study of HIV-1 and can be applied to study outbreaks and epidemics of other rapidly evolving pathogens. © The Author 2018. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Genome-wide meta-analyses identify multiple loci associated with smoking behavior

NARCIS (Netherlands)

H. Furberg (Helena); Y. Kim (Yunjung); J. Dackor (Jennifer); E.A. Boerwinkle (Eric); N. Franceschini (Nora); D. Ardissino (Diego); L. Bernardinelli (Luisa); P.M. Mannucci (Pier); F. Mauri (Francesco); P.A. Merlini (Piera); D. Absher (Devin); T.L. Assimes (Themistocles); S.P. Fortmann (Stephen); C. Iribarren (Carlos); J.W. Knowles (Joshua); T. Quertermous (Thomas); L. Ferrucci (Luigi); T. Tanaka (Toshiko); J.C. Bis (Joshua); T. Haritunians (Talin); B. McKnight (Barbara); B.M. Psaty (Bruce); K.D. Taylor (Kent); E.L. Thacker (Evan); P. Almgren (Peter); L. Groop (Leif); C. Ladenvall (Claes); M. Boehnke (Michael); A.U. Jackson (Anne); K.L. Mohlke (Karen); H.M. Stringham (Heather); J. Tuomilehto (Jaakko); E.J. Benjamin (Emelia); S.J. Hwang; D. Levy (Daniel); S.R. Preis; R.S. Vasan (Ramachandran Srini); J. Duan (Jubao); P.V. Gejman (Pablo); D.F. Levinson (Douglas); A.R. Sanders (Alan); J. Shi (Jianxin); E.H. Lips (Esther); J.D. McKay (James); A. Agudo (Antonio); L. Barzan (Luigi); V. Bencko (Vladimir); S. Benhamou (Simone); X. Castellsagué (Xavier); C. Canova (Cristina); D.I. Conway (David); E. Fabianova (Eleonora); L. Foretova (Lenka); V. Janout (Vladimir); C.M. Healy (Claire); I. Holcátová (Ivana); K. Kjaerheim (Kristina); P. Lagiou; J. Lissowska (Jolanta); R. Lowry (Ray); T.V. MacFarlane (Tatiana); D. Mates (Dana); L. Richiardi (Lorenzo); P. Rudnai (Peter); N. Szeszenia-Dabrowska (Neonilia); D. Zaridze; A. Znaor (Ariana); M. Lathrop (Mark); P. Brennan (Paul); S. Bandinelli (Stefania); T.M. Frayling (Timothy); J.M. Guralnik (Jack); Y. Milaneschi (Yuri); J.R.B. Perry (John); D. Altshuler (David); R. Elosua (Roberto); S. Kathiresan (Sekar); G. Lucas (Gavin); O. Melander (Olle); V. Salomaa (Veikko); S.M. Schwartz (Stephen); B.F. Voight (Benjamin); B.W.J.H. Penninx (Brenda); J.H. Smit (Johannes); N. Vogelzangs (Nicole); D.I. Boomsma (Dorret); E.J.C. de Geus (Eco); J.M. Vink (Jacqueline); G.A.H.M. Willemsen (Gonneke); S.J. Chanock (Stephen); F. Gu (Fangyi); S.E. Hankinson (Susan); D. Hunter (David); A. Hofman (Albert); H.W. Tiemeier (Henning); A.G. Uitterlinden (André); P. Tikka-Kleemola (Päivi); S. Walter (Stefan); D.I. Chasman (Daniel); B.M. Everett (Brendan); G. Pare (Guillaume); P.M. Ridker (Paul); M.D. Li (Ming); H.H. Maes (Hermine); J. Audrain-Mcgovern (Janet); D. Posthuma (Danielle); L.M. Thornton (Laura); C. Lerman (Caryn); J. Kaprio (Jaakko); J.E. Rose (Jed); J.P.A. Ioannidis (John); P. Kraft (Peter); D.Y. Lin (Dan); P.F. Sullivan (Patrick); C.J. O'Donnell (Christopher)

2010-01-01

textabstractConsistent but indirect evidence has implicated genetic factors in smoking behavior. We report meta-analyses of several smoking phenotypes within cohorts of the Tobacco and Genetics Consortium (n = 74,053). We also partnered with the European Network of Genetic and Genomic Epidemiology

Molecular epidemiology of Plum pox virus in Japan.

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Maejima, Kensaku; Himeno, Misako; Komatsu, Ken; Takinami, Yusuke; Hashimoto, Masayoshi; Takahashi, Shuichiro; Yamaji, Yasuyuki; Oshima, Kenro; Namba, Shigetou

2011-05-01

For a molecular epidemiological study based on complete genome sequences, 37 Plum pox virus (PPV) isolates were collected from the Kanto region in Japan. Pair-wise analyses revealed that all 37 Japanese isolates belong to the PPV-D strain, with low genetic diversity (less than 0.8%). In phylogenetic analysis of the PPV-D strain based on complete nucleotide sequences, the relationships of the PPV-D strain were reconstructed with high resolution: at the global level, the American, Canadian, and Japanese isolates formed their own distinct monophyletic clusters, suggesting that the routes of viral entry into these countries were independent; at the local level, the actual transmission histories of PPV were precisely reconstructed with high bootstrap support. This is the first description of the molecular epidemiology of PPV based on complete genome sequences.

Genetics and caries: prospects

Directory of Open Access Journals (Sweden)

Alexandre Rezende Vieira

2012-01-01

Full Text Available Caries remains the most prevalent non-contagious infectious disease in humans. It is clear that the current approaches to decrease the prevalence of caries in human populations, including water fluoridation and school-based programs, are not enough to protect everyone. The scientific community has suggested the need for innovative work in a number of areas in cariology, encompassing disease etiology, epidemiology, definition, prevention, and treatment. We have pioneered the work on genetic studies to identify genes and genetic markers of diagnostic, prognostic, and therapeutic value. This paper summarizes a presentation that elaborated on these initial findings.

NEURAL NETWORKS, FUZZY LOGIC AND GENETIC ALGORITHMS: APPLICATIONS AND POSSIBILITIES IN FINANCE AND ACCOUNTING

Directory of Open Access Journals (Sweden)

José Alonso Borba

2010-04-01

Full Text Available There are problems in Finance and Accounting that can not be easily solved by means of traditional techniques (e.g. bankruptcy prediction and strategies for investing in common stock. In these situations, it is possible to use methods of Artificial Intelligence. This paper analyzes empirical works published in international journals between 2000 and 2007 that present studies about the application of Neural Networks, Fuzzy Logic and Genetic Algorithms to problems in Finance and Accounting. The objective is to identify and quantify the relationships established between the available techniques and the problems studied by the researchers. Analyzing 258 papers, it was noticed that the most used technique is the Artificial Neural Network. The most researched applications are from the field of Finance, especially those related to stock exchanges (forecasting of common stock and indices prices.

AN ARTIFICIAL NEURAL NETWORK EVALUATION OF TUBERCULOSIS USING GENETIC AND PHYSIOLOGICAL PATIENT DATA

International Nuclear Information System (INIS)

Griffin, William O.; Darsey, Jerry A.; Hanna, Josh; Razorilova, Svetlana; Kitaev, Mikhael; Alisherov, Avtandiil; Tarasenko, Olga

2010-01-01

When doctors see more cases of patients with tell-tale symptoms of a disease, it is hoped that they will be able to recognize an infection administer treatment appropriately, thereby speeding up recovery for sick patients. We hope that our studies can aid in the detection of tuberculosis by using a computer model called an artificial neural network. Our model looks at patients with and without tuberculosis (TB). The data that the neural network examined came from the following: patient' age, gender, place, of birth, blood type, Rhesus (Rh) factor, and genes of the human Leukocyte Antigens (HLA) system (9q34.1) present in the Major Histocompatibility Complex. With availability in genetic data and good research, we hope to give them an advantage in the detection of tuberculosis. We try to mimic the doctor's experience with a computer test, which will learn from patient data the factors that contribute to TB.

The epidemiology of endometriosis.

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Cramer, Daniel W; Missmer, Stacey A

2002-03-01

Advances in understanding the epidemiology of endometriosis have lagged behind other diseases because of methodologic problems related to disease definition and control selection. Nevertheless, a better picture of the epidemiology of endometriosis has emerged over the past few decades. Prevalence estimates of the disease in clinic populations vary from about a 4% occurrence of largely asymptomatic endometriosis found in women undergoing tubal ligation to 50% of teenagers with intractable dysmenorrhea. General population incidence during the 1970s in this country has been suggested to be 1.6 per 1000 white females aged 15-49, while a more current study based upon hospital discharges finds endometriosis as a first listed diagnosis in 1.3 per 1000 discharges in women aged 15-44. There is a clinical impression that blacks have lower rates of endometriosis and Orientals have higher rates than whites. A variety of personal risk factors for endometriosis have also been described. Women with endometriosis may be taller and thinner. Menstrual factors reported to increase risk include dysmenorrhea, early menarche, and shorter cycle lengths. There is support for the idea that lifestyle exposures that might raise or lower estrogen levels could affect risk, including a decreased risk associated with smoking and exercise and an increased risk associated with caffeine or alcohol use. These risk factors appear to be compatible with the central importance of retrograde menstruation influenced by outflow obstruction that might affect its amount, immune factors that might affect its ability to be cleared, or hormonal stimuli that might affect its growth. In this model, dysmenorrhea could be either a disease symptom or a manifestation of outflow obstruction. Nulliparity could be either a consequence of disease or a cause since nulliparous women would not have the benefit of cervical dilation associated with labor and delivery. Since there is evidence that family history is a risk

Molecular Epidemiology of Yellow Fever in Bolivia from 1999 to 2008

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