DNA-Directed DNA Polymerases Evolve From Reverse Transcriptase

Scientists have been debating for decades the origin of life on earth. A number of hypotheses were proposed as to what emerged first RNA or DNA; with most scientists are in favour of the "RNA World" hypothesis. Assuming RNA emerged first, it fellow that the RNA polymerases would've appeared before DNA polymerases. Using recombinant DNA technology and bioinformatics we undertook this study to explore the relationship between RNA polymerases, reverse transcriptase and DNA polymerases. The working hypothesis is that DNA polymerases evolved from reverse transcriptase and the latter evolved from RNA polymerases. If this hypothesis is correct then one would expect to find various ancient DNA polymerases with varying level of reverse transcriptase activity. In the first phase of this research project multiple sequence alignments were made on the protein sequence of 32 prokaryotic DNA-directed DNA polymerases originating from 11 prokaryotic families against 3 viral reverse transcriptase. The data from such alignments was not very conclusive. DNA polymerases with higher level of reverse transcriptase activity were non-confined to ancient organisms, as one would've expected. The second phase of this project was focused on conditions that may alter the DNA polymerase activity. Various reaction conditions, such as temperature, using various ions (Ni2+, Mn2+, Mg2+) were tested. Interestingly, it was found that the DNA polymerase from the Thermos aquatics family can be made to copy RNA into DNA (i.e. reverse transcriptase activity). Thus it was shown that under appropriate conditions (ions and reactions temperatures) reverse transcriptase activity can be induced in DNA polymerase. In the third phase of this study recombinant DNA technology was used to generate a chimeric DNA polymerase; in attempts to identify the region(s) of the polymerase responsible for RNA-directed DNA polymerase activity. The two DNA polymerases employed were the Thermus aquatic us and Thermus thermophiles. As in the second phase various reaction conditions were investigated. Data indicated that the newly engineered chimeric DNA polymerase can be induced to copy RNA into DNA. Thus the intrinsic reverse transcriptase activity found in ancient DNA polymerases was localized into a domain and can be induced via appropriate reaction conditions.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

The Impact of Directed Mirror Focus and Technique Cues on Psychological, Cognitive and Emotional Exercise Correlates in an Introductory Weight Training Orientation

The purpose of the study was to investigate whether teaching inactive and low active women to use mirrors for form and technique purposes could lessen the negative impact of mirrors on self-presentational concerns, affect, and self-efficacy. Eligible women (N = 82) underwent a one-on-one weight training orientation with a personal trainer. Participants were randomized into one of four experimental groups, each unique in the type of feedback (general or technique-specific) and the degree of focus on the mirror for technique reinforcement. Questionnaires assessed study outcomes pre- and post-orientation. Results indicated groups did not significantly differ on any post-condition variables, when controlling for pre-condition values (all p’s >.05). All groups showed outcome improvements following the orientation. This suggests that during a complex task, a personal trainer who emphasizes form and technique can facilitate improvements to psychological outcomes in novice exercisers, independent of the presence of mirrors or directional cues provided.

Tests of Conditional Asset Pricing Models in the Brazilian Stock Market

In this paper, we test a version of the conditional CAPM with respect to a local market portfolio, proxied by the Brazilian stock index during the 1976-1992 period. We also test a conditional APT model by using the difference between the 30-day rate (Cdb) and the overnight rate as a second factor in addition to the market portfolio in order to capture the large inflation risk present during this period. The conditional CAPM and APT models are estimated by the Generalized Method of Moments (GMM) and tested on a set of size portfolios created from a total of 25 securities exchanged on the Brazilian markets. The inclusion of this second factor proves to be crucial for the appropriate pricing of the portfolios.

Tests of Conditional Asset Pricing Models in the Brazilian Stock Market

In this paper, we test a version of the conditional CAPM with respect to a local market portfolio, proxied by the Brazilian stock index during the 1976-1992 period. We also test a conditional APT model by using the difference between the 30-day rate (Cdb) and the overnight rate as a second factor in addition to the market portfolio in order to capture the large inflation risk present during this period. the conditional CAPM and APT models are estimated by the Generalized Method of Moments (GMM) and tested on a set of size portfolios created from a total of 25 securities exchanged on the Brazilian markets. the inclusion of this second factor proves to be crucial for the appropriate pricing of the portfolios.

Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form

Conditional heteroskedasticity is an important feature of many macroeconomic and financial time series. Standard residual-based bootstrap procedures for dynamic regression models treat the regression error as i.i.d. These procedures are invalid in the presence of conditional heteroskedasticity. We establish the asymptotic validity of three easy-to-implement alternative bootstrap proposals for stationary autoregressive processes with m.d.s. errors subject to possible conditional heteroskedasticity of unknown form. These proposals are the fixed-design wild bootstrap, the recursive-design wild bootstrap and the pairwise bootstrap. In a simulation study all three procedures tend to be more accurate in small samples than the conventional large-sample approximation based on robust standard errors. In contrast, standard residual-based bootstrap methods for models with i.i.d. errors may be very inaccurate if the i.i.d. assumption is violated. We conclude that in many empirical applications the proposed robust bootstrap procedures should routinely replace conventional bootstrap procedures for autoregressions based on the i.i.d. error assumption.

Directed Technical Change and International Trade

Recent changes in comparative advantage in the largest OECD economies differ significantly from the predictions of Heckscher-Ohlin-Vanek theory. Japan's rising share of OECD machinery exports and the improvement in the comparative advantage of the USA and Germany in heavy industry were accompanied by growing scarcities of the factors used intensively in the favored sector of each country. Here we examine Acemoglu's (1998, 2002) hypothesis that technical change may be directed toward raising the marginal productivity of abundant factors. Testing this hypothesis with 1970-1992 export data from 14 OECD countries, we find evidence that international comparative advantage was reshaped by innovation biased toward the abundant factors in the largest economies.

Directed evolution of human dihydrofolate reductase: towards a better understanding of binding at the active site

La dihydrofolate réductase humaine (DHFRh) est une enzyme essentielle à la prolifération cellulaire, ce qui en fait une cible de choix pour le traitement de différents cancers. À cet effet, plusieurs inhibiteurs spécifiques de la DHFRh, les antifolates, ont été mis au point : le méthotrexate (MTX) et le pemetrexed (PMTX) en sont de bons exemples. Malgré l’efficacité clinique certaine de ces antifolates, le développement de nouveaux traitements s’avère nécessaire afin de réduire les effets secondaires liés à leur utilisation. Enfin, dans l’optique d’orienter la synthèse de nouveaux composés inhibiteurs des DHFRh, une meilleure connaissance des interactions entre les antifolates et leur enzyme cible est primordiale. À l’aide de l’évolution dirigée, il a été possible d’identifier des mutants de la DHFRh pour lesquels l’affinité envers des antifolates cliniquement actifs se voyait modifiée. La mutagenèse dite ¬¬de saturation a été utilisée afin de générer des banques de mutants présentant une diversité génétique au niveau des résidus du site actif de l’enzyme d’intérêt. De plus, une nouvelle méthode de criblage a été mise au point, laquelle s’est avérée efficace pour départager les mutations ayant entrainé une résistance aux antifolates et/ou un maintient de l’activité enzymatique envers son substrat natif, soient les phénotypes d’activité. La méthode de criblage consiste dans un premier temps en une sélection bactérienne à haut débit, puis dans un second temps en un criblage sur plaques permettant d’identifier les meilleurs candidats. Plusieurs mutants actifs de la DHFRh, résistants aux antifolates, ont ainsi pu être identifiés et caractérisés lors d’études de cinétique enzymatique (kcat et IC50). Sur la base de ces résultats cinétiques, de la modélisation moléculaire et des données structurales de la littérature, une étude structure-activité a été effectuée. En regardant quelles mutations ont les effets les plus significatif sur la liaison, nous avons commencé à construire un carte moléculaire des contacts impliqués dans la liaison des ligands. Enfin, des connaissances supplémentaires sur les propriétés spécifiques de liaison ont put être acquises en variant l’inhibiteur testé, permettant ainsi une meilleure compréhension du phénomène de discrimination du ligand.

Conditional Expected Utility

Let 'epsilon' be a class of event. Conditionally Expected Utility decision makers are decision makers whose conditional preferences ≿E, E є 'epsilon', satisfy the axioms of Subjective Expected Utility theory (SEU). We extend the notion of unconditional preference that is conditionally EU to unconditional preferences that are not necessarily SEU. We give a representation theorem for a class of such preferences, and show that they are Invariant Bi-separable in the sense of Ghirardato et al.[7]. Then, we consider the special case where the unconditional preference is itself SEU, and compare our results with those of Fishburn [6].

Distributed conditional computation

L'objectif de cette thèse est de présenter différentes applications du programme de recherche de calcul conditionnel distribué. On espère que ces applications, ainsi que la théorie présentée ici, mènera à une solution générale du problème d'intelligence artificielle, en particulier en ce qui a trait à la nécessité d'efficience. La vision du calcul conditionnel distribué consiste à accélérer l'évaluation et l'entraînement de modèles profonds, ce qui est très différent de l'objectif usuel d'améliorer sa capacité de généralisation et d'optimisation. Le travail présenté ici a des liens étroits avec les modèles de type mélange d'experts. Dans le chapitre 2, nous présentons un nouvel algorithme d'apprentissage profond qui utilise une forme simple d'apprentissage par renforcement sur un modèle d'arbre de décisions à base de réseau de neurones. Nous démontrons la nécessité d'une contrainte d'équilibre pour maintenir la distribution d'exemples aux experts uniforme et empêcher les monopoles. Pour rendre le calcul efficient, l'entrainement et l'évaluation sont contraints à être éparse en utilisant un routeur échantillonnant des experts d'une distribution multinomiale étant donné un exemple. Dans le chapitre 3, nous présentons un nouveau modèle profond constitué d'une représentation éparse divisée en segments d'experts. Un modèle de langue à base de réseau de neurones est construit à partir des transformations éparses entre ces segments. L'opération éparse par bloc est implémentée pour utilisation sur des cartes graphiques. Sa vitesse est comparée à deux opérations denses du même calibre pour démontrer le gain réel de calcul qui peut être obtenu. Un modèle profond utilisant des opérations éparses contrôlées par un routeur distinct des experts est entraîné sur un ensemble de données d'un milliard de mots. Un nouvel algorithme de partitionnement de données est appliqué sur un ensemble de mots pour hiérarchiser la couche de sortie d'un modèle de langage, la rendant ainsi beaucoup plus efficiente. Le travail présenté dans cette thèse est au centre de la vision de calcul conditionnel distribué émis par Yoshua Bengio. Elle tente d'appliquer la recherche dans le domaine des mélanges d'experts aux modèles profonds pour améliorer leur vitesse ainsi que leur capacité d'optimisation. Nous croyons que la théorie et les expériences de cette thèse sont une étape importante sur la voie du calcul conditionnel distribué car elle cadre bien le problème, surtout en ce qui concerne la compétitivité des systèmes d'experts.

A Note On Some Domination Parameters in Graph Products

In this paper, we study the domination number, the global dom ination number, the cographic domination number, the global co graphic domination number and the independent domination number of all the graph products which are non-complete extended p-sums (NEPS) of two graphs.