The reasons behind the progression in PISA scores: An education production function approach using semi-parametric techniques

Research Masters

Immune regulation and function of the NOD-like receptors NLRC5 and NLRP3

AbstractThe vertebrate immune system is composed of the innate and the adaptive branches. Innate immune cells represent the first line of defense and detect pathogens through pattern recognition receptors (PRRs), detecting evolutionary conserved pathogen- and danger- associated molecular patterns. Engagement of these receptors initiates the inflammatory response, but also instructs antigen-specific adaptive immune cells. NOD-like receptors (NLRs) are an important group of PRRs, leading to the production of inflammatory mediators and favoring antigen presentation to Τ lymphocytes through the regulation of major histocompatibility complex (MHC) molecules.In this work we focused our attention on selected NOD-like receptors (NLRs) and their role at the interface between innate and adaptive immunity. First, we describe a new regulatory mechanism controlling IL-1 production. Our results indicate that type I interferons (IFNs) block NLRP1 and NLRP3 inflammasome activity and interfere with LPS-driven proIL-Ια and -β induction. As type I IFNs are produced upon viral infections, these anti-inflammatory effects of type I IFN could be relevant in the context of superinfections, but could also help explaining the efficacy of IFN-β in multiple sclerosis treatment.The second project addresses the role of a novel NLR family member, called NLRC5. The function of this NLR is still matter of debate, as it has been proposed as both an inhibitor and an activator of different inflammatory pathways. We found that the expression of this protein is restricted to immune cells and is positively regulated by IFNs. We generated Nlrc5-deficient mice and found that this NLR plays an essential role in Τ, NKT and, NK lymphocytes, in which it drives the expression of MHC class I molecules. Accordingly, we could show that CD8+ Τ cell-mediated killing of target lymphocytes lacking NLRC5 is strongly impaired. Moreover, NLRC5 expression was found to be low in many lymphoid- derived tumor cell lines, a mechanism that could be exploited by tumors to escape immunosurveillance.Finally, we found NLRC5 to be involved in the production of IL-10 by CD4+ Τ cells, as Nlrc5- deficient Τ lymphocytes produced less of this cytokine upon TCR triggering. In line with these observations, Mrc5-deficient CD4+ Τ cells expanded more than control cells when transferred into lymphopenic hosts and led to a more rapid appearance of colitis symptoms. Therefore, our work gives novel insights on the function of NLRC5 by using knockout mice, and strongly supports the idea that NLRs direct not only innate, but also adaptive immune responses.

MHC class I expression dependent on bacterial infection and parental factors in whitefish embryos (Salmonidae).

Ecological conditions can influence not only the expression of a phenotype, but also the heritability of a trait. As such, heritable variation for a trait needs to be studied across environments. We have investigated how pathogen challenge affects the expression of MHC genes in embryos of the lake whitefish Coregonus palaea. In order to experimentally separate paternal (i.e. genetic) from maternal and environmental effects, and determine whether and how stress affects the heritable variation for MHC expression, embryos were produced in full-factorial in vitro fertilizations, reared singly, and exposed at 208 degree days (late-eyed stage) to either one of two strains of Pseudomonas fluorescens that differ in their virulence characteristics (one increased mortality, while both delayed hatching time). Gene expression was assessed 48 h postinoculation, and virulence effects of the bacterial infection were monitored until hatching. We found no evidence of MHC class II expression at this stage of development. MHC class I expression was markedly down-regulated in reaction to both pseudomonads. While MHC expression could not be linked to embryo survival, the less the gene was expressed, the earlier the embryos hatched within each treatment group, possibly due to trade-offs between immune function and developmental rate or further factors that affect both hatching timing and MHC expression. We found significant additive genetic variance for MHC class I expression in some treatments. That is, changes in pathogen pressures could induce rapid evolution in MHC class I expression. However, we found no additive genetic variance in reaction norms in our study population.

Novel glutathione disulfide transferase function of CC-glutaredoxins involved in disease resistance and flower development

Glutaredoxins are oxidoreductases capable of reducing protein disulfide bridges and glutathione mixed disulfides through the process of deglutathionylation and glutathionylation. Lately, redox-mediated modifications of functional cysteine residues of TGA1 and TGA8 transcription factors have been postulated. Namely, GRX480 and ROXY1 glutaredoxins have been previously shown to interact with TGA proteins and have been suggested to regulate redox state of these proteins. TGA1, together with TGA2, is involved in systemic acquired resistance (SAR) establishment in the plant Arabidopsis thaliana through PR1 (Pathogenesis related 1) gene activation. They both form an enhanceosome complex with the NPR1 protein (non-expressor of pathogenesis related gene 1) which leads to PR1 transcription. Although TGA1 is capable of activating PR1 transcription, the ability of the TGA1 NPR1 enhanceosome complex to assembly is based on the redox status of TGA1. We identified GRX480 as a glutathionylating enzyme that catalyzes the TGA1 glutathione disulfide transferase reaction with a Km of around 20μM GSSG (oxidized glutathione). Out of four cysteine residues found within TGA1, C172 and C266 were found to be glutathionylated by this enzyme. We also confirmed TGA1 glutathionylation in vivo and showed that this modification takes place while TGA1 is associated with the PR1 promoter enzymatically via GRX480. Furthermore, we show that glutathionylation via GRX480 abolishes TGA1's interaction with NPR1 and consequently prevents the TGA1-NPR1 transcription activation of PR1. When glutathionylated, TGA1 is recruited to the PR1 promoter and acts as a repressor. Therefore, glutathionylation is a mechanism that prevents TGA1 NPR1 interaction, allowing TGA1 to function as a repressor of PR1 transcription. Surprisingly, GRX480 was not able to deglutathionylate proteins demonstrating the irreversible nature of the reaction. Moreover, we demonstrate that other members of CC-class glutaredoxins, namely ROXY1 and ROXY2, can also catalyze protein glutathionylation. The TGA8 protein was previously shown to interact with NPR1 analogs, BOP1 and BOP2 proteins. However, unlike the case of TGA1 NPR1 interaction, here we demonstrate that TGA8-BOP1 interaction is not redox regulated and that TGA8 glutathionylation by ROXY1 and ROXY2 enzymes does not abolish this interaction in vitro. However, TGA8 glutathionylation results in TGA8 oligomer disassembly into smaller complexes and monomers. Our results suggest that CC-Grxs are unable to reduce mixed disulfides, instead they efficiently catalyze the opposite reaction which distinguishes them from traditional glutaredoxins. Therefore, they should not be classified as glutaredoxins but as protein glutathione disulfide transferases.

ARMA modeling of time series based on rational approximation of spectral density function

This study is concerned with Autoregressive Moving Average (ARMA) models of time series. ARMA models form a subclass of the class of general linear models which represents stationary time series, a phenomenon encountered most often in practice by engineers, scientists and economists. It is always desirable to employ models which use parameters parsimoniously. Parsimony will be achieved by ARMA models because it has only finite number of parameters. Even though the discussion is primarily concerned with stationary time series, later we will take up the case of homogeneous non stationary time series which can be transformed to stationary time series. Time series models, obtained with the help of the present and past data is used for forecasting future values. Physical science as well as social science take benefits of forecasting models. The role of forecasting cuts across all fields of management-—finance, marketing, production, business economics, as also in signal process, communication engineering, chemical processes, electronics etc. This high applicability of time series is the motivation to this study.

Measure Fields for Function Approximation

The computation of a piecewise smooth function that approximates a finite set of data points may be decomposed into two decoupled tasks: first, the computation of the locally smooth models, and hence, the segmentation of the data into classes that consist on the sets of points best approximated by each model, and second, the computation of the normalized discriminant functions for each induced class. The approximating function may then be computed as the optimal estimator with respect to this measure field. We give an efficient procedure for effecting both computations, and for the determination of the optimal number of components.

Modelling, control and supervision for a class of hybrid systems

The aim of this thesis is to narrow the gap between two different control techniques: the continuous control and the discrete event control techniques DES. This gap can be reduced by the study of Hybrid systems, and by interpreting as Hybrid systems the majority of large-scale systems. In particular, when looking deeply into a process, it is often possible to identify interaction between discrete and continuous signals. Hybrid systems are systems that have both continuous, and discrete signals. Continuous signals are generally supposed continuous and differentiable in time, since discrete signals are neither continuous nor differentiable in time due to their abrupt changes in time. Continuous signals often represent the measure of natural physical magnitudes such as temperature, pressure etc. The discrete signals are normally artificial signals, operated by human artefacts as current, voltage, light etc. Typical processes modelled as Hybrid systems are production systems, chemical process, or continuos production when time and continuous measures interacts with the transport, and stock inventory system. Complex systems as manufacturing lines are hybrid in a global sense. They can be decomposed into several subsystems, and their links. Another motivation for the study of Hybrid systems is the tools developed by other research domains. These tools benefit from the use of temporal logic for the analysis of several properties of Hybrid systems model, and use it to design systems and controllers, which satisfies physical or imposed restrictions. This thesis is focused in particular types of systems with discrete and continuous signals in interaction. That can be modelled hard non-linealities, such as hysteresis, jumps in the state, limit cycles, etc. and their possible non-deterministic future behaviour expressed by an interpretable model description. The Hybrid systems treated in this work are systems with several discrete states, always less than thirty states (it can arrive to NP hard problem), and continuous dynamics evolving with expression: with Ki ¡ Rn constant vectors or matrices for X components vector. In several states the continuous evolution can be several of them Ki = 0. In this formulation, the mathematics can express Time invariant linear system. By the use of this expression for a local part, the combination of several local linear models is possible to represent non-linear systems. And with the interaction with discrete events of the system the model can compose non-linear Hybrid systems. Especially multistage processes with high continuous dynamics are well represented by the proposed methodology. Sate vectors with more than two components, as third order models or higher is well approximated by the proposed approximation. Flexible belt transmission, chemical reactions with initial start-up and mobile robots with important friction are several physical systems, which profits from the benefits of proposed methodology (accuracy). The motivation of this thesis is to obtain a solution that can control and drive the Hybrid systems from the origin or starting point to the goal. How to obtain this solution, and which is the best solution in terms of one cost function subject to the physical restrictions and control actions is analysed. Hybrid systems that have several possible states, different ways to drive the system to the goal and different continuous control signals are problems that motivate this research. The requirements of the system on which we work is: a model that can represent the behaviour of the non-linear systems, and that possibilities the prediction of possible future behaviour for the model, in order to apply an supervisor which decides the optimal and secure action to drive the system toward the goal. Specific problems can be determined by the use of this kind of hybrid models are: - The unity of order. - Control the system along a reachable path. - Control the system in a safe path. - Optimise the cost function. - Modularity of control The proposed model solves the specified problems in the switching models problem, the initial condition calculus and the unity of the order models. Continuous and discrete phenomena are represented in Linear hybrid models, defined with defined eighth-tuple parameters to model different types of hybrid phenomena. Applying a transformation over the state vector : for LTI system we obtain from a two-dimensional SS a single parameter, alpha, which still maintains the dynamical information. Combining this parameter with the system output, a complete description of the system is obtained in a form of a graph in polar representation. Using Tagaki-Sugeno type III is a fuzzy model which include linear time invariant LTI models for each local model, the fuzzyfication of different LTI local model gives as a result a non-linear time invariant model. In our case the output and the alpha measure govern the membership function. Hybrid systems control is a huge task, the processes need to be guided from the Starting point to the desired End point, passing a through of different specific states and points in the trajectory. The system can be structured in different levels of abstraction and the control in three layers for the Hybrid systems from planning the process to produce the actions, these are the planning, the process and control layer. In this case the algorithms will be applied to robotics ¡V a domain where improvements are well accepted ¡V it is expected to find a simple repetitive processes for which the extra effort in complexity can be compensated by some cost reductions. It may be also interesting to implement some control optimisation to processes such as fuel injection, DC-DC converters etc. In order to apply the RW theory of discrete event systems on a Hybrid system, we must abstract the continuous signals and to project the events generated for these signals, to obtain new sets of observable and controllable events. Ramadge & Wonham¡¦s theory along with the TCT software give a Controllable Sublanguage of the legal language generated for a Discrete Event System (DES). Continuous abstraction transforms predicates over continuous variables into controllable or uncontrollable events, and modifies the set of uncontrollable, controllable observable and unobservable events. Continuous signals produce into the system virtual events, when this crosses the bound limits. If this event is deterministic, they can be projected. It is necessary to determine the controllability of this event, in order to assign this to the corresponding set, , controllable, uncontrollable, observable and unobservable set of events. Find optimal trajectories in order to minimise some cost function is the goal of the modelling procedure. Mathematical model for the system allows the user to apply mathematical techniques over this expression. These possibilities are, to minimise a specific cost function, to obtain optimal controllers and to approximate a specific trajectory. The combination of the Dynamic Programming with Bellman Principle of optimality, give us the procedure to solve the minimum time trajectory for Hybrid systems. The problem is greater when there exists interaction between adjacent states. In Hybrid systems the problem is to determine the partial set points to be applied at the local models. Optimal controller can be implemented in each local model in order to assure the minimisation of the local costs. The solution of this problem needs to give us the trajectory to follow the system. Trajectory marked by a set of set points to force the system to passing over them. Several ways are possible to drive the system from the Starting point Xi to the End point Xf. Different ways are interesting in: dynamic sense, minimum states, approximation at set points, etc. These ways need to be safe and viable and RchW. And only one of them must to be applied, normally the best, which minimises the proposed cost function. A Reachable Way, this means the controllable way and safe, will be evaluated in order to obtain which one minimises the cost function. Contribution of this work is a complete framework to work with the majority Hybrid systems, the procedures to model, control and supervise are defined and explained and its use is demonstrated. Also explained is the procedure to model the systems to be analysed for automatic verification. Great improvements were obtained by using this methodology in comparison to using other piecewise linear approximations. It is demonstrated in particular cases this methodology can provide best approximation. The most important contribution of this work, is the Alpha approximation for non-linear systems with high dynamics While this kind of process is not typical, but in this case the Alpha approximation is the best linear approximation to use, and give a compact representation.

Are class II phosphoinositide 3-kinases potential targets for anticancer therapies?

Of the three classes of true phosphoinositide (PI) 3-kinases, the class II subdivision, which consists of three isoforms, PI3K-C2alpha, PI3K-C2beta and PI3K-C2gamma, is the least well understood. There are a number of reasons for this. This class of PI 3-kinase was identified exclusively by PCR and homology cloning approaches and not on the basis of cellular function. Like class I PI 3-kinases, class II PI 3-kinases are activated by diverse receptor types. To complicate the elucidation of class II PI 3-kinase function further, their in vitro substrate specificity is intermediate between the receptor activated class I PI 3-kinases and the housekeeping class III PI 3-kinase. The class II PI 3-kinases are inhibited by the two commonly used PI 3-kinase family selective inhibitors, wortmannin and LY294002, and there are no widely available, specific inhibitors for the individual classes or isoforms. Here the current state of understanding of class II PI 3-kinase function is reviewed, followed by an appraisal as to whether there is enough evidence to suggest that pharmaceutical companies, who are currently targeting the class I PI 3-kinases in an attempt to generate anticancer agents, should also consider targeting the class II PI 3-kinases.

Improvement of classification accuracy for stochastic discrimination - Multi-class classification

In this paper, an improved stochastic discrimination (SD) is introduced to reduce the error rate of the standard SD in the context of multi-class classification problem. The learning procedure of the improved SD consists of two stages. In the first stage, a standard SD, but with shorter learning period is carried out to identify an important space where all the misclassified samples are located. In the second stage, the standard SD is modified by (i) restricting sampling in the important space; and (ii) introducing a new discriminant function for samples in the important space. It is shown by mathematical derivation that the new discriminant function has the same mean, but smaller variance than that of standard SD for samples in the important space. It is also analyzed that the smaller the variance of the discriminant function, the lower the error rate of the classifier. Consequently, the proposed improved SD improves standard SD by its capability of achieving higher classification accuracy. Illustrative examples axe provided to demonstrate the effectiveness of the proposed improved SD.

Grey-box radial basis function modelling: the art of incorporating prior knowledge

A basic principle in data modelling is to incorporate available a priori information regarding the underlying data generating mechanism into the modelling process. We adopt this principle and consider grey-box radial basis function (RBF) modelling capable of incorporating prior knowledge. Specifically, we show how to explicitly incorporate the two types of prior knowledge: the underlying data generating mechanism exhibits known symmetric property and the underlying process obeys a set of given boundary value constraints. The class of orthogonal least squares regression algorithms can readily be applied to construct parsimonious grey-box RBF models with enhanced generalisation capability.

Fully complex-valued radial basis function networks: orthogonal least squares regression and classification

We consider a fully complex-valued radial basis function (RBF) network for regression and classification applications. For regression problems, the locally regularised orthogonal least squares (LROLS) algorithm aided with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF models, is extended to the fully complex-valued RBF (CVRBF) network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully CVRBF network. The proposed fully CVRBF network is also applied to four-class classification problems that are typically encountered in communication systems. A complex-valued orthogonal forward selection algorithm based on the multi-class Fisher ratio of class separability measure is derived for constructing sparse CVRBF classifiers that generalise well. The effectiveness of the proposed algorithm is demonstrated using the example of nonlinear beamforming for multiple-antenna aided communication systems that employ complex-valued quadrature phase shift keying modulation scheme. (C) 2007 Elsevier B.V. All rights reserved.

Construction of tunable radial basis function networks using orthogonal forward selection

An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines an RBF node, namely, its center vector and diagonal covariance matrix, by minimizing the LOO statistics. For regression application, the LOO criterion is chosen to be the LOO mean-square error, while the LOO misclassification rate is adopted in two-class classification application. This OFS-LOO algorithm is computationally efficient, and it is capable of constructing parsimonious RBF networks that generalize well. Moreover, the proposed algorithm is fully automatic, and the user does not need to specify a termination criterion for the construction process. The effectiveness of the proposed RBF network construction procedure is demonstrated using examples taken from both regression and classification applications.

Kernel classifier construction using orthogonal forward selection and boosting with Fisher ratio class separability measure

A greedy technique is proposed to construct parsimonious kernel classifiers using the orthogonal forward selection method and boosting based on Fisher ratio for class separability measure. Unlike most kernel classification methods, which restrict kernel means to the training input data and use a fixed common variance for all the kernel terms, the proposed technique can tune both the mean vector and diagonal covariance matrix of individual kernel by incrementally maximizing Fisher ratio for class separability measure. An efficient weighted optimization method is developed based on boosting to append kernels one by one in an orthogonal forward selection procedure. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing sparse Gaussian radial basis function network classifiers. that generalize well.

Global exponential periodicity of a class of bidirectional associative memory networks with finite distributed delays

In this paper, we study the periodic oscillatory behavior of a class of bidirectional associative memory (BAM) networks with finite distributed delays. A set of criteria are proposed for determining global exponential periodicity of the proposed BAM networks, which assume neither differentiability nor monotonicity of the activation function of each neuron. In addition, our criteria are easily checkable. (c) 2005 Elsevier Inc. All rights reserved.

Global exponential periodicity of a class of neural networks with recent-history distributed delays

In this paper, we propose to study a class of neural networks with recent-history distributed delays. A sufficient condition is derived for the global exponential periodicity of the proposed neural networks, which has the advantage that it assumes neither the differentiability nor monotonicity of the activation function of each neuron nor the symmetry of the feedback matrix or delayed feedback matrix. Our criterion is shown to be valid by applying it to an illustrative system. (c) 2005 Elsevier Ltd. All rights reserved.