Heterogeneity of photosystem II as it occurs in domain specific regions of the thylakoid membrane of spinach (spinacia oleracia L.)

Thylakoid membrane fractions were prepared from specific regions of thylakoid membranes of spinach (Spinacia oleracea). These fractions, which include grana (83), stroma (T3), grana core (8S), margins (Ma) and purified stroma (Y100) were prepared using a non-detergent method including a mild sonication and aqueous two-phase partitioning. The significance of PSlla and PSII~ centres have been described extensively in the literature. Previous work has characterized two types of PSII centres which are proposed to exist in different regions of the thylakoid membrane. a-centres are suggested to aggregate in stacked regions of grana whereas ~-centres are located in unstacked regions of stroma lamellae. The goal of this study is to characterize photosystem II from the isolated membrane vesicles representing different regions of the higher plant thylakoid membrane. The low temperature absorption spectra have been deconvoluted via Gaussian decomposition to estimate the relative sub-components that contribute to each fractions signature absorption spectrum. The relative sizes of the functional PSII antenna and the fluorescence induction kinetics were measured and used to determine the relative contributions of PSlla and PSII~ to each fraction. Picosecond chlorophyll fluorescence decay kinetics were collected for each fraction to characterize and gain insight into excitation energy transfer and primary electron transport in PSlla and PSII~ centres. The results presented here clearly illustrate the widely held notions of PSII/PS·I and PSlIa/PSII~ spatial separation. This study suggests that chlorophyll fluorescence decay lifetimes of PSII~ centres are shorter than those of PSlIa centres and, at FM, the longer lived of the two PSII components renders a larger yield in PSlIa-rich fractions, but smaller in PSIlr3-rich fractions.

Winâ Foy : functional object-oriented programming language

This thesis will introduce a new strongly typed programming language utilizing Self types, named Win--*Foy, along with a suitable user interface designed specifically to highlight language features. The need for such a programming language is based on deficiencies found in programming languages that support both Self types and subtyping. Subtyping is a concept that is taken for granted by most software engineers programming in object-oriented languages. Subtyping supports subsumption but it does not support the inheritance of binary methods. Binary methods contain an argument of type Self, the same type as the object itself, in a contravariant position, i.e. as a parameter. There are several arguments in favour of introducing Self types into a programming language (11. This rationale led to the development of a relation that has become known as matching [4, 5). The matching relation does not support subsumption, however, it does support the inheritance of binary methods. Two forms of matching have been proposed (lJ. Specifically, these relations are known as higher-order matching and I-bound matching. Previous research on these relations indicates that the higher-order matching relation is both reflexive and transitive whereas the f-bound matching is reflexive but not transitive (7]. The higher-order matching relation provides significant flexibility regarding inheritance of methods that utilize or return values of the same type. This flexibility, in certain situations, can restrict the programmer from defining specific classes and methods which are based on constant values [21J. For this reason, the type This is used as a second reference to the type of the object that cannot, contrary to Self, be specialized in subclasses. F-bound matching allows a programmer to define a function that will work for all types of A', a subtype of an upper bound function of type A, with the result type being dependent on A'. The use of parametric polymorphism in f-bound matching provides a connection to subtyping in object-oriented languages. This thesis will contain two main sections. Firstly, significant details concerning deficiencies of the subtype relation and the need to introduce higher-order and f-bound matching relations into programming languages will be explored. Secondly, a new programming language named Win--*Foy Functional Object-Oriented Programming Language has been created, along with a suitable user interface, in order to facilitate experimentation by programmers regarding the matching relation. The construction of the programming language and the user interface will be explained in detail.

Predictors of shy children's coping with a social conflict : mediators and moderators

Shy children are at risk for later maladjustment due to ineffective coping with social conflicts through reliance on avoidance, rather than approach-focused, coping. The purpose of the present study was to explore whether the relation between shyness and children's coping was mediated by attributions and moderated by personality selftheories and gender. Participants included a classroom-based sample of 175 children (93 boys), aged 9-13 years (M = 10.11 years, SD = 0.92). Children completed self-report measures assessing shyness, attributions, personality self-theories and coping strategies. Results showed that negative attribution biases partially mediated the negative relations between shyness and social support seeking, as well as problem-solving, and the positive association between shyness and externalizing. Moreover, self-theories moderated the relation between shyness and internalizing coping at the trend level, such that the positive relation was exacerbated among entity-oriented children to a greater degree than incrementally-oriented children. In terms of gender differences, shyness was related to lower use of social support and problem-solving among incrementally-oriented boys and entity-oriented girls. Thus, shy children's perceptions of social conflicts as the outcome of an enduring trait (e.g., social incompetence) may partially explain why they do not act assertively and aggress as a means of social coping. Furthermore, entity-oriented beliefs may exacerbate shy children's reliance on internalizing actions, such as crying. Although an incrementally-oriented stance may enhance shy girls' reliance on approach strategies, it does not appear to serve the same protective role for shy boys. Therefore, copingoriented interventions may need to focus on restructuring shy children's social cognitions and implementing gender-specific programming for their personality biases.

A System for Models of First Order Theories

If you want to know whether a property is true or not in a specific algebraic structure,you need to test that property on the given structure. This can be done by hand, which can be cumbersome and erroneous. In addition, the time consumed in testing depends on the size of the structure where the property is applied. We present an implementation of a system for finding counterexamples and testing properties of models of first-order theories. This system is supposed to provide a convenient and paperless environment for researchers and students investigating or studying such models and algebraic structures in particular. To implement a first-order theory in the system, a suitable first-order language.( and some axioms are required. The components of a language are given by a collection of variables, a set of predicate symbols, and a set of operation symbols. Variables and operation symbols are used to build terms. Terms, predicate symbols, and the usual logical connectives are used to build formulas. A first-order theory now consists of a language together with a set of closed formulas, i.e. formulas without free occurrences of variables. The set of formulas is also called the axioms of the theory. The system uses several different formats to allow the user to specify languages, to define axioms and theories and to create models. Besides the obvious operations and tests on these structures, we have introduced the notion of a functor between classes of models in order to generate more co~plex models from given ones automatically. As an example, we will use the system to create several lattices structures starting from a model of the theory of pre-orders.

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.

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.

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.

A domain-general perspective on medial frontal brain activity during performance monitoring

Activity of the medial frontal cortex (MFC) has been implicated in attention regulation and performance monitoring. The MFC is thought to generate several event-related potential (ERPs) components, known as medial frontal negativities (MFNs), that are elicited when a behavioural response becomes difficult to control (e.g., following an error or shifting from a frequently executed response). The functional significance of MFNs has traditionally been interpreted in the context of the paradigm used to elicit a specific response, such as errors. In a series of studies, we consider the functional similarity of multiple MFC brain responses by designing novel performance monitoring tasks and exploiting advanced methods for electroencephalography (EEG) signal processing and robust estimation statistics for hypothesis testing. In study 1, we designed a response cueing task and used Independent Component Analysis (ICA) to show that the latent factors describing a MFN to stimuli that cued the potential need to inhibit a response on upcoming trials also accounted for medial frontal brain responses that occurred when individuals made a mistake or inhibited an incorrect response. It was also found that increases in theta occurred to each of these task events, and that the effects were evident at the group level and in single cases. In study 2, we replicated our method of classifying MFC activity to cues in our response task and showed again, using additional tasks, that error commission, response inhibition, and, to a lesser extent, the processing of performance feedback all elicited similar changes across MFNs and theta power. In the final study, we converted our response cueing paradigm into a saccade cueing task in order to examine the oscillatory dynamics of response preparation. We found that, compared to easy pro-saccades, successfully preparing a difficult anti-saccadic response was characterized by an increase in MFC theta and the suppression of posterior alpha power prior to executing the eye movement. These findings align with a large body of literature on performance monitoring and ERPs, and indicate that MFNs, along with their signature in theta power, reflects the general process of controlling attention and adapting behaviour without the need to induce error commission, the inhibition of responses, or the presentation of negative feedback.