Effective talent development environments: bridging the theory-practice gap within a UK context

Performance sport has become a high priority for many Nations. For example, the UK distributes approximately £25 million per year through their World Class Performance programmes in order to aid effective identification, development and performance of our best athletes. Not surprisingly, in line with a more professional and scientific approach, the standards of sporting performance at an elite level are constantly improving. In order to remain competitive on the international stage, the processes and support mechanisms within our talent development environments (TDEs) must be effective in order to maintain a consistent stream of talent, capable of success at the highest level.Unfortunately, the structure and evidence base for talent development (TD) processes within the UK is weak and lacking in evidence -based guidance for those working `on the ground'. This is compounded by an apparent procedural bias towards the identification of talent as opposed to its development, a focus which is contrary to much of the research in this area. Against this backdrop, the objectives of this thesis are as follows:1) To identify the `needs' within current TD practice and provide clear direction and methodological guidance for the required programme of research,2) To identify guidelines through a triangulation of evidence that enables the application of effective TD procedures,3) To develop a tool to help bridge the theory -practice divide and enable practitioners and researchers to examine TDEs within applied settings, and4) To provide preliminary validation of the tool to assess the extent to which it has discriminant function

Protein-protein Interactions of Bacterial Topoisomerase I

Protein-protein interactions (PPIs) are essential features of cellular processes including DNA replication, transcription, translation, recombination, and repair. In my study, the protein interactions of bacterial DNA topoisomerase I, an essential enzyme, were investigated. The topoisomerase I in bacteria relaxes excess negative supercoiling on DNA and maintains genomic stability. Investigating the PPI network of DNA topoisomerase I can further our understanding of the various functional roles of this enzyme. My study is focused on topoisomerase I of Escherichia coli and Mycobacterium smegmatis. Firstly, we have explored the biochemical mechanisms for an interaction between RNA Polymerase, and topoisomerase I in E. coli. Molecular docking and molecular dynamic simulations have predicted that the interactions are mediated through electrostatic, and hydrogen bonding. The predicted Lysine residues (K627, K664) of topoisomerase I that are involved in the electrostatic interactions were mutated to Alanine, and its effect on the binding efficiency with RNA polymerase was reported. In a separate study, PPI partners of topoisomerase I in mycobacteria were identified. Knowledge gained from the study can provide valuable insights into the physiological functions of a validated drug target, DNA topoisomerase I, in pathogenic mycobacteria. Co-immunoprecipitation and pull-down assays were coupled to mass spectrometry for identification of the protein partners of mycobacterial topoisomerase I. The study has identified RNA polymerase, and putative helicases (DEAD/DEAH BOX helicases) as potential protein partners of mycobacterial topoisomerase I. My results indicated that the tail region of the CTD-topoisomerase I was required for direct physical interaction with the RNAP beta’ subunit. My studies have also verified the physiological relevance of the topoisomerase I - RNA polymerase interactions for survival under antibiotic, and oxidative stress. Lastly, I report a direct physical interaction between E. coli topoisomerase I and RecA by pull-down assays. Previous studies have shown that RecA, a DNA repair protein, can stimulate the relaxation activity of E. coli topoisomerase I. Our new results showed that the stimulatory effect can be attributed to the physical interaction of topoisomerase I with RecA

Integrated Machine Learning and Bioinformatics Approaches for Prediction of Cancer-Driving Gene Mutations

Cancer arises from the accumulation of somatic mutations and genetic alterations in cell division checkpoints and apoptosis, this often leads to abnormal tumor proliferation. Proper classification of cancer-linked driver mutations will considerably help our understanding of the molecular dynamics of cancer. In this study, we compared several cancer-specific predictive models for prediction of driver mutations in cancer-linked genes that were validated on canonical data sets of functionally validated mutations and applied to a raw cancer genomics data. By analyzing pathogenicity prediction and conservation scores, we have shown that evolutionary conservation scores play a pivotal role in the classification of cancer drivers and were the most informative features in the driver mutation classification. Through extensive comparative analysis with structure-functional experiments and multicenter mutational calling data from PanCancer Atlas studies, we have demonstrated the robustness of our models and addressed the validity of computational predictions. We evaluated the performance of our models using the standard diagnostic metrics such as sensitivity, specificity, area under the curve and F-measure. To address the interpretability of cancer-specific classification models and obtain novel insights about molecular signatures of driver mutations, we have complemented machine learning predictions with structure-functional analysis of cancer driver mutations in several key tumor suppressor genes and oncogenes. Through the experiments carried out in this study, we found that evolutionary-based features have the strongest signal in the machine learning classification VII of driver mutations and provide orthogonal information to the ensembled-based scores that are prominent in the ranking of feature importance

31th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems.The amount and complexity of information itself, the number of abstractionlevels of information, and the size of databases and knowledge bases arecontinuously growing. Conceptual modelling is one of the sub-areas ofinformation modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

Computer Graphics Learning Materials

Selles lõputöös on antud ülevaade Tartu Ülikooli aine Arvutigraafika (MTAT.03.015) jaoks koostatud õppematerjalist ja õppekeskkonnast. Kirjeldatud on aine modulaarset ülesehitust, mis rakendab kombineeritud ülevalt-alla (ing. k. top-down) ja alt-üles (ing. k. bottom-up) lähenemisi. Loodud õppematerjal sisaldab endas interaktiivseid näiteid, mis vastavad hõivatuse taksonoomia 4ndale tasemele. Õppekeskkonna CGLearn spetsifikatsioon ja implementatsiooni detailid on kirjeldatud. Töö lõpus on kursusel osalenud õpilaste hulgas läbi viidud tagasiside küsitluse tulemuste analüüsiga. Lisa fail on lingina kätesaadav serveri probleemide tõttu aadresil : http://comserv.cs.ut.ee/forms/ati_report/files/ComputerGraphicsLearningMaterialsAppendix.zipThis thesis provides an overview of the learning material and a custom learning environment created for the Computer Graphics (MTAT.03.015) course in the University of Tartu. It describes a modular layout, that mixes a top-down and bottom-up approaches, in which the course was organized. The created material also includes interactive examples that satisfy engagement level 4 requirements. The specification and implementation details of the custom learning environment called CGLearn are given. Thesis concludes with the analysis of the feedback questionnaire answered by the students participating in the course and using the material. Due to server problems extras file is in here : http://comserv.cs.ut.ee/forms/ati_report/files/ComputerGraphicsLearningMaterialsAppendix.zi

Nonlinear Effects of Macroeconomic Shocks

This thesis investigates the nonlinear macroeconomic effects of fiscal and uncertainty shocks. It comprises three contained chapters, each one of them being self-contained. In each chapter, theoretical predictions coming from theoretical models are presented and discussed. Such predictions are then tested using state-of-the-art econometric techniques. The first chapter is titled “News in State-Dependent Fiscal Multipliers: The Role of Confidence”. This study scrutinizes the role of consumer confidence in determining the real effects that unanticipated (news) government spending shocks have on output in recessions and expansions by estimating a Smooth-Transition VAR model. To account for fiscal foresight, I employ a measure of anticipated fiscal shocks defined as the sums of expectations’ revisions over future fiscal spending. This variable is shown to carry relevant information about movements on government spending. My results indicate that fiscal multipliers during recession is both statistically larger than in expansions and greater than one. Importantly, consumer confidence is shown to play a decisive role on determining the effects of an anticipated spending shock within nonlinear framework. In particular, the response of confidence is key in explaining the statically larger fiscal multiplier during recessions. Moreover, the role of confidence is found to be relevant for the transmission of anticipated shocks only. These results qualify confidence as a key ingredient for understanding the transmission of fiscal news shocks (as opposed to unanticipated fiscal shocks). The second chapter is titled “Fiscal-Monetary Policy Mix in Recessions and Expansions”. This study investigates the role of monetary policy in determining the size of the fiscal spending multiplier in recessions and expansions as for the U.S. economy. To this end, I quantify the size of state-dependent fiscal multipliers by using a nonlinear VAR model endowed with fiscal and monetary variables. I carefully separate anticipated and unexpected fiscal spending shocks by jointly modeling fiscal spending and the measure of spending news proposed by Ramey (2011 QJE). My results indicate that the fiscal multiplier in recessions is larger than one and statistically different from that corresponding to expansions. Importantly, the role of monetary policy during recessions triggers a crowding out effect. In particular, a counterfactual exercise clearly have the role played for the systematic policy to emerge. These findings highlight the importance of jointly consider monetary and fiscal indicators when studying the effects of a fiscal stimulus. The third chapter titled “Economic Policy Uncertainty Spillovers in Booms and Busts” is joint paper with Giovanni Caggiano and Efrem Castelnuovo. This study aims at quantifying the impact of economic policy uncertainty shocks originating in the U.S. on the Canadian business cycle in booms and busts. It does so by employing a nonlinear Smooth-Transition VAR model to identify and simulate an increase in the U.S. economic policy uncertainty on a number of Canadian macroeconomics variables, including real activity indicators (industrial production and unemployment), inflation, a short-term interest rate, and the bilateral exchange rate. Our results point to statistically and economically relevant nonlinear spillover effects. Uncertainty shocks originated in the U.S. explain about the 27% of the variance of the 2-years ahead forecast error of the Canadian unemployment rate in periods of slack vs. 8% during economic booms. Counterfactual simulations lead to the identification of a novel “economic policy uncertainty spillovers channel”. According to this channel, spikes in the U.S. economic policy uncertainty foster economic policy uncertainty in Canada in first place and, because of the latter, an increase in the Canadian rate of unemployment occurs

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF