Monooxygenase Diversity and Oxygen Activation within Polyketide Antibiotic Biosynthesis

Molecular oxygen (O2) is a key component in cellular respiration and aerobic life. Through the redox potential of O2, the amount of free energy available to organisms that utilize it is greatly increased. Yet, due to the nature of the O2 electron configuration, it is non-reactive to most organic molecules in the ground state. For O2 to react with most organic compounds it must be activated. By activating O2, oxygenases can catalyze reactions involving oxygen incorporation into organic compounds. The oxygen activation mechanisms employed by many oxygenases to have been studied, and they often include transition metals and selected organic compounds. Despite the diversity of mechanisms for O2 activation explored in this thesis, all of the monooxygenases studied in the experimental part activate O2 through a transient carbanion intermediate. One of these enzymes is the small cofactorless monooxygenase SnoaB. Cofactorless monooxygenases are unusual oxygenases that require neither transition metals nor cofactors to activate oxygen. Based on our biochemical characterization and the crystal structure of this enzyme, the mechanism most likely employed by SnoaB relies on a carbanion intermediate to activate oxygen, which is consistent with the proposed substrate-assisted mechanism for this family of enzymes. From the studies conducted on the two-component system AlnT and AlnH, both the functions of the NADH-dependent flavin reductase, AlnH, and the reduced flavin dependent monooxygenase, AlnT, were confirmed. The unusual regiochemistry proposed for AlnT was also confirmed on the basis of the structure of a reaction product. The mechanism of AlnT, as with other flavin-dependent monooxygenases, is likely to involve a caged radical pair consisting of a superoxide anion and a neutral flavin radical formed from an initial carbanion intermediate. In the studies concerning the engineering of the S-adenosyl-L-methionine (SAM) dependent 4-O-methylase DnrK and the homologous atypical 10-hydroxylase RdmB, our data suggest that an initial decarboxylation of the substrate is catalyzed by both of these enzymes, which results in the generation of a carbanion intermediate. This intermediate is not essential for the 4-O-methylation reaction, but it is important for the 10-hydroxylation reaction, since it enables substrate-assisted activation of molecular oxygen involving a single electron transfer to O2 from a carbanion intermediate. The only role for SAM in the hydroxylation reaction is likely to be stabilization of the carbanion through the positive charge of the cofactor. Based on the DnrK variant crystal structure and the characterizations of several DnrK variants, the insertion of a single amino acid in DnrK (S297) is sufficient for gaining a hydroxylation function, which is likely caused by carbanion stabilization through active site solvent restriction. Despite large differences in the three-dimensional structures of the oxygenases and the potential for multiple oxygen activation mechanisms, all the enzymes in my studies rely on carbanion intermediates to activate oxygen from either flavins or their substrates. This thesis provides interesting examples of divergent evolution and the prevalence of carbanion intermediates within polyketide biosynthesis. This mechanism appears to be recurrent in aromatic polyketide biosynthesis and may reflect the acidic nature of these compounds, propensity towards hydrogen bonding and their ability to delocalize π-electrons.

Spreading my wings : how can I improve my practice and contribute to the professional knowledge base through narrative-autobiographical self-study?

"How can I improve my practice and contribute to the professional knowledge base through narrative-autobiographical self-study?" Through the use of Whitehead's (1989) living educational theory and examination of my stories, I identify the values and critical events that have helped me come to know my own learning and shape my professional self. Building on the premise that educational knowledge/theory is created, recreated, and lived through educational inquiry; I strive to make meaning of this data archive, collected over 7 years of teaching. I chart my journey to reexamine my beliefs and practices, to find a balance between traditional and progressive practices and to align my theory and practice. I retell, and, thus, in some way relive, my own "living contradictions." A reconceptualization of the KNOW, DO, BE model (Drake & Burns, 2004) is used to develop strategies to align my practice, including a six-step model of curriculum design that combines the backwards design process of Wiggins and McTighe (1998), the KNOW, DO, BE model (Drake & Burns) and Curry and Samara's (1995) differentiation planner.

Muscle strength and activation characteristics of power- trained and non-athlete boys and men

During maturation, muscle strength is enhanced through muscle growth, although neuro-muscular factors are also believed to be involved. In adults, training for power sports has been shown to enhance muscle strength and activation. The purpose of this study was to examine muscle strength and activation in power-trained athletes (POW) compared with non-athletes (CON), in boys and in adults. After familiarization subjects performed ten 5-s explosive maximal voluntary contractions for elbow and knee flexion and extension. The adults were stronger then the boys and the adult POW were stronger then the adult CON, even after correction for muscle size. Normalized rate of torque development was higher in the adults then in the boys and higher in the POW then CON boys. The rate of muscle activation was higher in the adults and POW groups. The results suggest that maturation and power-training have an additive effect on muscle activation.

Automatic Structure Generation using Genetic Programming and Fractal Geometry

Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages o ffered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and e ffectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the fi eld of computational aesthetics and automated structure design.

Automatic evolution of conceptual building architectures

This thesis describes research in which genetic programming is used to automatically evolve shape grammars that construct three dimensional models of possible external building architectures. A completely automated fitness function is used, which evaluates the three dimensional building models according to different geometric properties such as surface normals, height, building footprint, and more. In order to evaluate the buildings on the different criteria, a multi-objective fitness function is used. The results obtained from the automated system were successful in satisfying the multiple objective criteria as well as creating interesting and unique designs that a human-aided system might not discover. In this study of evolutionary design, the architectures created are not meant to be fully functional and structurally sound blueprints for constructing a building, but are meant to be inspirational ideas for possible architectural designs. The evolved models are applicable for today's architectural industries as well as in the video game and movie industries. Many new avenues for future work have also been discovered and highlighted.

Hot cognition and activation of the medial prefrontal cortex: Self-regulating in contexts involving motivational pressures

The medial prefrontal cortex (mPFC) is involved in performance-monitoring and has been implicated in the generation of several electrocortical responses associated with self-regulation. The error-related negativity (ERN), the inhibitory Nogo N2 (N2), and the feedback-related negativity (FRN) are event-related potential (ERP) components which reflect mPFC activity associated with feedback to behavioural (ERN, N2) and environmental (FRN) consequences. Our main goal was to determine whether or not rnPFC activation varies as a function of motivational context (e.g., those involving performance-related incentives) or the use of internally versus externally generated feedback signals (i.e., errors). Additionally, we assessed medial prefrontal activity in relation to individual differences in personality and temperament. Participants completed a combination of tasks in which performance-related incentives were associated with task performance and feedback generated from internal versus external responses. MPFC activity was indexed using both ERP scalp voltage peaks and intracerebral current source density (CSD) of dorsal and ventral regions. Additionally, participants completed several questionnaires assessing personality and temperament styles. Given previous studies have shown that enhanced mPFC activity to loss (or negative) feedback, we expected that activity in the mPFC would generally be greater during the Loss condition relative to the Win condition for both the ERN and N2. Also, due to the evidence that the (vmPFC) is engaged in arousing contexts, we hypothesized that activity in the ventromedial prefrontal cortex (vmPFC) would be greater than activity in the dorsomedial prefrontal cortex (dmPFC), especially in the Loss condition of the GoNogo task (ERN). Similarly, loss feedback in the BART (FRN) was expected to engage the vmPFC more than the dmPFC. Finally, we predicted that persons rating themselves as more willing to engage in approach-related behaviours or to exhibit rigid cognitive styles would show reduced activity of the mPFC. Overall, our results emphasize the role of affective evaluations of behavioural and environmental consequences when self-regulating. Although there were no effects of context on brain activity, our data indicate that, during the time of the ERN and N2 on the MW Go-Nogo task and the FRN on the BART, the vrnPFC was more active compared to the dmPFC. Moreover, regional recruitment in the mPFC was similar across internally (ERN) and externally (FRN) generated errors signals associated with loss feedback, as reflected by relatively greater activity in the vmPFC than the dmPFC. Our data also suggest that greater activity in the mPFC is associated with better inhibitory control, as reflected by both scalp and CSD measures. Additionally, deactivation of the subgenual anterior cingulate cortex (sgACC) and lower levels of self-reported positive affect were both related to increased voluntary risk-taking on the BART. Finally, persons reporting higher levels of approach-related behaviour or cognitive rigidity showed reduced activity of the mPFC. These results are in line with previous research emphasizing that affect/motivation is central to the processes reflected by mediofrontal negativities (MFNs), that the vmPFC is involved in regulating demands on motivational/affective systems, and that the underlying mechanisms driving these functions vary across both individuals and contexts.

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.

The electromyographic threshold in children: Differences in neuromuscular activation during progressive exercise between boys and men

The electromyographic threshold (EMGTh), defined as an upward inflexion in the rising EMG signal during progressive exercise, is thought to reflect the onset of increased type-II MU recruitment. The study’s objective was to compare the relative exercise intensity at which the EMGTh occurs in boys vs. men. Participants included 21 men (23.4±4.1 yrs) and 23 boys (11.1±1.1 yrs). Ramped cycle-ergometry was conducted to volitional exhaustion with surface EMG recorded from the vastus lateralis muscles. The EMGTh was mathematically determined using a composite of both legs. EMGTh was detected in 95.2% of the men and in 78.3% of the boys (χ2(1, n=44) =2.69, p =.10). The boys’ EMGTh was significantly higher than the men’s (86.4±9.6 vs. 79.7±10.0% of peak power-output at exhaustion; p <.05). These findings suggest that boys activate their type-II MUs to a lesser extent than men during progressive exercise and support the hypothesis of differential child–adult MU activation.

Top-down and Bottom-up influences on ACC activation: Evaluation of a proposed model of the feedback-related negativity

The Feedback-Related Negativity (FRN) is thought to reflect the dopaminergic prediction error signal from the subcortical areas to the ACC (i.e., a bottom-up signal). Two studies were conducted in order to test a new model of FRN generation, which includes direct modulating influences of medial PFC (i.e., top-down signals) on the ACC at the time of the FRN. Study 1 examined the effects of one’s sense of control (top-down) and of informative cues (bottom-up) on the FRN measures. In Study 2, sense of control and instruction-based (top-down) and probability-based expectations (bottom-up) were manipulated to test the proposed model. The results suggest that any influences of medial PFC on the activity of the ACC that occur in the context of incentive tasks are not direct. The FRN was shown to be sensitive to salient stimulus characteristics. The results of this dissertation partially support the reinforcement learning theory, in that the FRN is a marker for prediction error signal from subcortical areas. However, the pattern of results outlined here suggests that prediction errors are based on salient stimulus characteristics and are not reward specific. A second goal of this dissertation was to examine whether ACC activity, measured through the FRN, is altered in individuals at-risk for problem-gambling behaviour (PG). Individuals in this group were more sensitive to the valence of the outcome in a gambling task compared to not at-risk individuals, suggesting that gambling contexts increase the sensitivity of the reward system to valence of the outcome in individuals at risk for PG. Furthermore, at-risk participants showed an increased sensitivity to reward characteristics and a decreased response to loss outcomes. This contrasts with those not at risk whose FRNs were sensitive to losses. As the results did not replicate previous research showing attenuated FRNs in pathological gamblers, it is likely that the size and time of the FRN does not change gradually with increasing risk of maladaptive behaviour. Instead, changes in ACC activity reflected by the FRN in general can be observed only after behaviour becomes clinically maladaptive or through comparison between different types of gain/loss outcomes.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

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.

The time-course of lexical activation in Japanese morphographic word recognition: Evidence for a character-driven processing model

This lexical decision study with eye tracking of Japanese two-kanji-character words investigated the order in which a whole two-character word and its morphographic constituents are activated in the course of lexical access, the relative contributions of the left and the right characters in lexical decision, the depth to which semantic radicals are processed, and how nonlinguistic factors affect lexical processes. Mixed-effects regression analyses of response times and subgaze durations (i.e., first-pass fixation time spent on each of the two characters) revealed joint contributions of morphographic units at all levels of the linguistic structure with the magnitude and the direction of the lexical effects modulated by readers’ locus of attention in a left-to-right preferred processing path. During the early time frame, character effects were larger in magnitude and more robust than radical and whole-word effects, regardless of the font size and the type of nonwords. Extending previous radical-based and character-based models, we propose a task/decision-sensitive character-driven processing model with a level-skipping assumption: Connections from the feature level bypass the lower radical level and link up directly to the higher character level.

Automatic Preparation of ETD Material from the Internet Archive for the DSpace Repository Platform

A big challenge associated with getting an institutional repository off the ground is getting content into it. This article will look at how to use digitization services at the Internet Archive alongside software utilities that the author developed to automate the harvesting of scanned dissertations and associated Dublin Core XML files to create an ETD Portal using the DSpace platform. The end result is a metadata-rich, full-text collection of theses that can be constructed for little out of pocket cost.

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.