Persistence of Transmitted HIV-1 Drug Resistance Mutations Associated with Fitness Costs and Viral Genetic Backgrounds.

Transmission of drug-resistant pathogens presents an almost-universal challenge for fighting infectious diseases. Transmitted drug resistance mutations (TDRM) can persist in the absence of drugs for considerable time. It is generally believed that differential TDRM-persistence is caused, at least partially, by variations in TDRM-fitness-costs. However, in vivo epidemiological evidence for the impact of fitness costs on TDRM-persistence is rare. Here, we studied the persistence of TDRM in HIV-1 using longitudinally-sampled nucleotide sequences from the Swiss-HIV-Cohort-Study (SHCS). All treatment-naïve individuals with TDRM at baseline were included. Persistence of TDRM was quantified via reversion rates (RR) determined with interval-censored survival models. Fitness costs of TDRM were estimated in the genetic background in which they occurred using a previously published and validated machine-learning algorithm (based on in vitro replicative capacities) and were included in the survival models as explanatory variables. In 857 sequential samples from 168 treatment-naïve patients, 17 TDRM were analyzed. RR varied substantially and ranged from 174.0/100-person-years;CI=[51.4, 588.8] (for 184V) to 2.7/100-person-years;[0.7, 10.9] (for 215D). RR increased significantly with fitness cost (increase by 1.6[1.3,2.0] per standard deviation of fitness costs). When subdividing fitness costs into the average fitness cost of a given mutation and the deviation from the average fitness cost of a mutation in a given genetic background, we found that both components were significantly associated with reversion-rates. Our results show that the substantial variations of TDRM persistence in the absence of drugs are associated with fitness-cost differences both among mutations and among different genetic backgrounds for the same mutation.

Points to consider for prioritizing clinical genetic testing services: a European consensus process oriented at accountability for reasonableness.

Given the cost constraints of the European health-care systems, criteria are needed to decide which genetic services to fund from the public budgets, if not all can be covered. To ensure that high-priority services are available equitably within and across the European countries, a shared set of prioritization criteria would be desirable. A decision process following the accountability for reasonableness framework was undertaken, including a multidisciplinary EuroGentest/PPPC-ESHG workshop to develop shared prioritization criteria. Resources are currently too limited to fund all the beneficial genetic testing services available in the next decade. Ethically and economically reflected prioritization criteria are needed. Prioritization should be based on considerations of medical benefit, health need and costs. Medical benefit includes evidence of benefit in terms of clinical benefit, benefit of information for important life decisions, benefit for other people apart from the person tested and the patient-specific likelihood of being affected by the condition tested for. It may be subject to a finite time window. Health need includes the severity of the condition tested for and its progression at the time of testing. Further discussion and better evidence is needed before clearly defined recommendations can be made or a prioritization algorithm proposed. To our knowledge, this is the first time a clinical society has initiated a decision process about health-care prioritization on a European level, following the principles of accountability for reasonableness. We provide points to consider to stimulate this debate across the EU and to serve as a reference for improving patient management.

Diversity and genetic relatedness among genotypes of Vitis spp. using microsatellite molecular markers

The purpose of this research was to study the genetic diversity and genetic relatedness of 60 genotypes of grapevines derived from the Germplasm Bank of Embrapa Semiárido, Juazeiro, BA, Brazil. Seven previously characterized microsatellite markers were used: VVS2, VVMD5, VVMD7, VVMD27, VVMD3, ssrVrZAG79 and ssrVrZAG62. The expected heterozygosity (He) and polymorphic information content (PIC) were calculated, and the cluster analysis were processed to generate a dendrogram using the algorithm UPGMA. The He ranged from 81.8% to 88.1%, with a mean of 84.8%. The loci VrZAG79 and VVMD7 were the most informative, with a PIC of 87 and 86%, respectively, while VrZAG62 was the least informative, with a PIC value of 80%. Cluster analysis by UPGMA method allowed separation of the genotypes according to their genealogy and identification of possible parentage for the cultivars 'Dominga', 'Isaura', 'CG 26916', 'CG28467' and 'Roni Redi'.

Analysis of a Plant Complex Resistance Gene Locus Underlying Immune-Related Hybrid Incompatibility and Its Occurrence in Nature

Mechanisms underlying speciation in plants include detrimental (incompatible) genetic interactions between parental alleles that incur a fitness cost in hybrids. We reported on recessive hybrid incompatibility between an Arabidopsis thaliana strain from Poland, Landsberg erecta (Ler), and many Central Asian A. thaliana strains. The incompatible interaction is determined by a polymorphic cluster of Toll/interleukin-1 receptor-nucleotide binding-leucine rich repeat (TNL) RPP1 (Recognition of Peronospora parasitica1)-like genes in Ler and alleles of the receptor-like kinase Strubbelig Receptor Family 3 (SRF3) in Central Asian strains Kas-2 or Kond, causing temperature-dependent autoimmunity and loss of growth and reproductive fitness. Here, we genetically dissected the RPP1-like Ler locus to determine contributions of individual RPP1-like Ler (R1R8) genes to the incompatibility. In a neutral background, expression of most RPP1-like Ler genes, except R3, has no effect on growth or pathogen resistance. Incompatibility involves increased R3 expression and engineered R3 overexpression in a neutral background induces dwarfism and sterility. However, no individual RPP1-like Ler gene is sufficient for incompatibility between Ler and Kas-2 or Kond, suggesting that co-action of at least two RPP1-like members underlies this epistatic interaction. We find that the RPP1-like Ler haplotype is frequent and occurs with other Ler RPP1-like alleles in a local population in Gorzów Wielkopolski (Poland). Only Gorzów individuals carrying the RPP1-like Ler haplotype are incompatible with Kas-2 and Kond, whereas other RPP1-like alleles in the population are compatible. Therefore, the RPP1-like Ler haplotype has been maintained in genetically different individuals at a single site, allowing exploration of forces shaping the evolution of RPP1-like genes at local and regional population scales.

Evaluation of genetic mixtures of sorghum lines for anthracnose resistance management

The main objective of this work was to evaluate the diversification of sorghum (Sorghum bicolor) populations as a way to manage resistance to the sorghum anthracnose fungus Colletotrichum graminicola. A total of 18 three-way hybrids were obtained by crossing six single cross male-sterile F1 hybrids, derived by crossing A (non restorer sterile cytoplasm) and B (non restorer normal cytoplasm) lines, with three fertile R (restorer) lines, previously evaluated for their differential reaction to the pathogen. Variation in the level of resistance was observed, as indicated by the values of the area under the disease progress curve (AUDPC) obtained for each hybrid. Lines contributed differently to the level of resistance of each hybrid. All hybrids in which CMSXS169R was the male progenitor were classified as highly resistant. Some hybrids had a level of resistance superior to the maximum levels of each line component individually.

Design and Control of Permanent Magnet Synchronous Machine for Hybrid Electric Vehicle

This master’s thesis mainly focuses on the design requirements of an Electric drive for Hybrid car application and its control strategy to achieve a wide speed range. It also emphasises how the control and performance requirements are transformed into its design variables. A parallel hybrid topology is considered where an IC engine and an electric drive share a common crank shaft. A permanent magnet synchronous machine (PMSM) is used as an electric drive machine. Performance requirements are converted into Machine design variables using the vector model of PMSM. Main dimensions of the machine are arrived using analytical approach and Finite Element Analysis (FEA) is used to verify the design and performance. Vector control algorithm was used to control the machine. The control algorithm was tested in a low power PMSM using an embedded controller. A prototype of 10 kW PMSM was built according to the design values. The prototype was tested in the laboratory using a high power converter. Tests were carried out to verify different operating modes. The results were in agreement with the calculations.

Novel Methods for Error Modeling and Parameter Identification of a Redundant Serial-Parallel Hybrid Robot

To obtain the desirable accuracy of a robot, there are two techniques available. The first option would be to make the robot match the nominal mathematic model. In other words, the manufacturing and assembling tolerances of every part would be extremely tight so that all of the various parameters would match the “design” or “nominal” values as closely as possible. This method can satisfy most of the accuracy requirements， but the cost would increase dramatically as the accuracy requirement increases. Alternatively, a more cost-effective solution is to build a manipulator with relaxed manufacturing and assembling tolerances. By modifying the mathematical model in the controller, the actual errors of the robot can be compensated. This is the essence of robot calibration. Simply put, robot calibration is the process of defining an appropriate error model and then identifying the various parameter errors that make the error model match the robot as closely as possible. This work focuses on kinematic calibration of a 10 degree-of-freedom (DOF) redundant serial-parallel hybrid robot. The robot consists of a 4-DOF serial mechanism and a 6-DOF hexapod parallel manipulator. The redundant 4-DOF serial structure is used to enlarge workspace and the 6-DOF hexapod manipulator is used to provide high load capabilities and stiffness for the whole structure. The main objective of the study is to develop a suitable calibration method to improve the accuracy of the redundant serial-parallel hybrid robot. To this end, a Denavit–Hartenberg (DH) hybrid error model and a Product-of-Exponential (POE) error model are developed for error modeling of the proposed robot. Furthermore, two kinds of global optimization methods, i.e. the differential-evolution (DE) algorithm and the Markov Chain Monte Carlo (MCMC) algorithm, are employed to identify the parameter errors of the derived error model. A measurement method based on a 3-2-1 wire-based pose estimation system is proposed and implemented in a Solidworks environment to simulate the real experimental validations. Numerical simulations and Solidworks prototype-model validations are carried out on the hybrid robot to verify the effectiveness, accuracy and robustness of the calibration algorithms.

Genetic programming for the RoboCup Rescue Simulation System

The Robocup Rescue Simulation System (RCRSS) is a dynamic system of multi-agent interaction, simulating a large-scale urban disaster scenario. Teams of rescue agents are charged with the tasks of minimizing civilian casualties and infrastructure damage while competing against limitations on time, communication, and awareness. This thesis provides the first known attempt of applying Genetic Programming (GP) to the development of behaviours necessary to perform well in the RCRSS. Specifically, this thesis studies the suitability of GP to evolve the operational behaviours required of each type of rescue agent in the RCRSS. The system developed is evaluated in terms of the consistency with which expected solutions are the target of convergence as well as by comparison to previous competition results. The results indicate that GP is capable of converging to some forms of expected behaviour, but that additional evolution in strategizing behaviours must be performed in order to become competitive. An enhancement to the standard GP algorithm is proposed which is shown to simplify the initial search space allowing evolution to occur much quicker. In addition, two forms of population are employed and compared in terms of their apparent effects on the evolution of control structures for intelligent rescue agents. The first is a single population in which each individual is comprised of three distinct trees for the respective control of three types of agents, the second is a set of three co-evolving subpopulations one for each type of agent. Multiple populations of cooperating individuals appear to achieve higher proficiencies in training, but testing on unseen instances raises the issue of overfitting.

Genetic Programming for Non-Photorealistic Rendering

This thesis focuses on developing an evolutionary art system using genetic programming. The main goal is to produce new forms of evolutionary art that filter existing images into new non-photorealistic (NPR) styles, by obtaining images that look like traditional media such as watercolor or pencil, as well as brand new effects. The approach permits GP to generate creative forms of NPR results. The GP language is extended with different techniques and methods inspired from NPR research such as colour mixing expressions, image processing filters and painting algorithm. Colour mixing is a major new contribution, as it enables many familiar and innovative NPR effects to arise. Another major innovation is that many GP functions process the canvas (rendered image), while is dynamically changing. Automatic fitness scoring uses aesthetic evaluation models and statistical analysis, and multi-objective fitness evaluation is used. Results showed a variety of NPR effects, as well as new, creative possibilities.

Alternative strategies for deciphering the genetic architecture of childhood Pre-B acute lymphoblastic leukemia

La leucémie lymphoblastique aigüe (LLA) est une maladie génétique complexe. Malgré que cette maladie hématologique soit le cancer pédiatrique le plus fréquent, ses causes demeurent inconnues. Des études antérieures ont démontrées que le risque à la LLA chez l’enfant pourrait être influencé par des gènes agissant dans le métabolisme des xénobiotiques, dans le maintient de l’intégrité génomique et dans la réponse au stress oxydatif, ainsi que par des facteurs environnementaux. Au cours de mes études doctorales, j’ai tenté de disséquer davantage les bases génétiques de la LLA de l’enfant en postulant que la susceptibilité à cette maladie serait modulée, au moins en partie, par des variants génétiques agissant dans deux voies biologiques fondamentales : le point de contrôle G1/S du cycle cellulaire et la réparation des cassures double-brin de l’ADN. En utilisant une approche unique reposant sur l’analyse d’une cohorte cas-contrôles jumelée à une cohorte de trios enfants-parents, j’ai effectué une étude d’association de type gènes/voies biologiques candidats. Ainsi, j’ai évaluer le rôle de variants provenant de la séquence promotrice de 12 gènes du cycle cellulaire et de 7 gènes de la voie de réparation de l’ADN, dans la susceptibilité à la LLA. De tels polymorphismes dans la région promotrice (pSNPs) pourraient perturber la liaison de facteurs de transcription et mener à des différences dans les niveaux d’expression des gènes pouvant influencer le risque à la maladie. En combinant différentes méthodes analytiques, j’ai évalué le rôle de différents mécanismes génétiques dans le développement de la LLA chez l’enfant. J’ai tout d’abord étudié les associations avec gènes/variants indépendants, et des essaies fonctionnels ont été effectués afin d’évaluer l’impact des pSNPs sur la liaison de facteurs de transcription et l’activité promotrice allèle-spécifique. Ces analyses ont mené à quatre publications. Il est peu probable que ces gènes de susceptibilité agissent seuls; j’ai donc utilisé une approche intégrative afin d’explorer la possibilité que plusieurs variants d’une même voie biologique ou de voies connexes puissent moduler le risque de la maladie; ces travaux ont été soumis pour publication. En outre, le développement précoce de la LLA, voir même in utero, suggère que les parents, et plus particulièrement la mère, pourraient jouer un rôle important dans le développement de cette maladie chez l’enfant. Dans une étude par simulations, j’ai évalué la performance des méthodes d’analyse existantes de détecter des effets fœto-maternels sous un design hybride trios/cas-contrôles. J’ai également investigué l’impact des effets génétiques agissant via la mère sur la susceptibilité à la LLA. Cette étude, récemment publiée, fût la première à démontrer que le risque de la leucémie chez l’enfant peut être modulé par le génotype de sa mère. En conclusions, mes études doctorales ont permis d’identifier des nouveaux gènes de susceptibilité pour la LLA pédiatrique et de mettre en évidence le rôle du cycle cellulaire et de la voie de la réparation de l’ADN dans la leucémogenèse. À terme, ces travaux permettront de mieux comprendre les bases génétiques de la LLA, et conduiront au développement d’outils cliniques qui amélioreront la détection, le diagnostique et le traitement de la leucémie chez l’enfant.

A Novel Fast Hybrid Cryptographic System: MARS4

A novel and fast technique for cryptographic applications is designed and developed using the symmetric key algorithm “MAJE4” and the popular asymmetric key algorithm “RSA”. The MAJE4 algorithm is used for encryption / decryption of files since it is much faster and occupies less memory than RSA. The RSA algorithm is used to solve the problem of key exchange as well as to accomplish scalability and message authentication. The focus is to develop a new hybrid system called MARS4 by combining the two cryptographic methods with an aim to get the advantages of both. The performance evaluation of MARS4 is done in comparison with MAJE4 and RSA.

Genetic Programming meets Model-Driven Development

Genetic programming is known to provide good solutions for many problems like the evolution of network protocols and distributed algorithms. In such cases it is most likely a hardwired module of a design framework that assists the engineer to optimize specific aspects of the system to be developed. It provides its results in a fixed format through an internal interface. In this paper we show how the utility of genetic programming can be increased remarkably by isolating it as a component and integrating it into the model-driven software development process. Our genetic programming framework produces XMI-encoded UML models that can easily be loaded into widely available modeling tools which in turn posses code generation as well as additional analysis and test capabilities. We use the evolution of a distributed election algorithm as an example to illustrate how genetic programming can be combined with model-driven development. This example clearly illustrates the advantages of our approach – the generation of source code in different programming languages.

Evolving Distributed Algorithms with Genetic Programming

Distributed systems are one of the most vital components of the economy. The most prominent example is probably the internet, a constituent element of our knowledge society. During the recent years, the number of novel network types has steadily increased. Amongst others, sensor networks, distributed systems composed of tiny computational devices with scarce resources, have emerged. The further development and heterogeneous connection of such systems imposes new requirements on the software development process. Mobile and wireless networks, for instance, have to organize themselves autonomously and must be able to react to changes in the environment and to failing nodes alike. Researching new approaches for the design of distributed algorithms may lead to methods with which these requirements can be met efficiently. In this thesis, one such method is developed, tested, and discussed in respect of its practical utility. Our new design approach for distributed algorithms is based on Genetic Programming, a member of the family of evolutionary algorithms. Evolutionary algorithms are metaheuristic optimization methods which copy principles from natural evolution. They use a population of solution candidates which they try to refine step by step in order to attain optimal values for predefined objective functions. The synthesis of an algorithm with our approach starts with an analysis step in which the wanted global behavior of the distributed system is specified. From this specification, objective functions are derived which steer a Genetic Programming process where the solution candidates are distributed programs. The objective functions rate how close these programs approximate the goal behavior in multiple randomized network simulations. The evolutionary process step by step selects the most promising solution candidates and modifies and combines them with mutation and crossover operators. This way, a description of the global behavior of a distributed system is translated automatically to programs which, if executed locally on the nodes of the system, exhibit this behavior. In our work, we test six different ways for representing distributed programs, comprising adaptations and extensions of well-known Genetic Programming methods (SGP, eSGP, and LGP), one bio-inspired approach (Fraglets), and two new program representations called Rule-based Genetic Programming (RBGP, eRBGP) designed by us. We breed programs in these representations for three well-known example problems in distributed systems: election algorithms, the distributed mutual exclusion at a critical section, and the distributed computation of the greatest common divisor of a set of numbers. Synthesizing distributed programs the evolutionary way does not necessarily lead to the envisaged results. In a detailed analysis, we discuss the problematic features which make this form of Genetic Programming particularly hard. The two Rule-based Genetic Programming approaches have been developed especially in order to mitigate these difficulties. In our experiments, at least one of them (eRBGP) turned out to be a very efficient approach and in most cases, was superior to the other representations.

Evaluation of two hybrid metric-conceptual models, for simulating phosphorus transfer from agricultural land in the river enborne, a lowland UK catchment

Across Europe, elevated phosphorus (P) concentrations in lowland rivers have made them particularly susceptible to eutrophication. This is compounded in southern and central UK by increasing pressures on water resources, which may be further enhanced by the potential effects of climate change. The EU Water Framework Directive requires an integrated approach to water resources management at the catchment scale and highlights the need for modelling tools that can distinguish relative contributions from multiple nutrient sources and are consistent with the information content of the available data. Two such models are introduced and evaluated within a stochastic framework using daily flow and total phosphorus concentrations recorded in a clay catchment typical of many areas of the lowland UK. Both models disaggregate empirical annual load estimates, derived from land use data, as a function of surface/near surface runoff, generated using a simple conceptual rainfall-runoff model. Estimates of the daily load from agricultural land, together with those from baseflow and point sources, feed into an in-stream routing algorithm. The first model assumes constant concentrations in runoff via surface/near surface pathways and incorporates an additional P store in the river-bed sediments, depleted above a critical discharge, to explicitly simulate resuspension. The second model, which is simpler, simulates P concentrations as a function of surface/near surface runoff, thus emphasising the influence of non-point source loads during flow peaks and mixing of baseflow and point sources during low flows. The temporal consistency of parameter estimates and thus the suitability of each approach is assessed dynamically following a new approach based on Monte-Carlo analysis. (c) 2004 Elsevier B.V. All rights reserved.

Generic systolic array for genetic algorithms

The authors present a systolic design for a simple GA mechanism which provides high throughput and unidirectional pipelining by exploiting the inherent parallelism in the genetic operators. The design computes in O(N+G) time steps using O(N2) cells where N is the population size and G is the chromosome length. The area of the device is independent of the chromosome length and so can be easily scaled by replicating the arrays or by employing fine-grain migration. The array is generic in the sense that it does not rely on the fitness function and can be used as an accelerator for any GA application using uniform crossover between pairs of chromosomes. The design can also be used in hybrid systems as an add-on to complement existing designs and methods for fitness function acceleration and island-style population management