6,198 research outputs found

    Japanese Expert Teachers' Understanding of the Application of Rhythm in Judo: a New Pedagogy

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    Aim The aim of this research is to understand the application of rhythm in judo through the experience of expert Japanese coaches. Background Scientists and experienced coaches agree rhythm is an important skill in people’s everyday life. There is currently no research that investigates the importance of rhythm in judo. People with a highly developed sense of rhythm, move properly, breathe properly, or begin and finish work at the right time. Where sport is concerned, motion and dance can play an important role not only in the improvement of performance, but also in the reduction, or even prevention of, injuries. Those who are naturally musically inclined (have a musical ear) may find they can improve their technique faster than others, and this is something that, by investigating the way expert coaches understand the application of rhythm in judo, this research seeks to understand. As Lange, (1970) stated, factors of movement are ‘weight, space, time, and flow on the background of the general flux of movement in proportional arrangements’ (Bradley, 2008; Selioni, 2013; Youngerman, 1976), therefore, this research will investigate the interaction of body and mind. Dance training as well as judo are somatic experiences that have as their ultimate goal the attainment of a skilled body. With quality training an athlete gains an increased awareness of their body which leads to better control of movement and is very important for judo athletes. This training is found in Japanese kabuki dance (Hahn, 2007), the Greek syrtaki dance (Zografou & Pateraki, 2007), and in walking techniques used in the traditional and Olympic sports of Japanese judo and Greek wrestling. Methods Interpretative phenomenological analysis (IPA) was the most suitable data analysis approach for this study for a number of reasons, mainly because it was considered to most closely reflect the author's realist epistemological view. The idiographic approach and framework, particularly on IPA, was regarded as a useful framework in which the current topic could meaningfully be explored. As this study is one of the first to explore this new thematic area, IPA was the preferred approach to address the goal of providing a detailed account of the expert’s experience. Therefore, semi-structured interviews were used as a data source. This is the most conventional form of data collection using IPA and most closely reflects the researcher-participant relationship. Semi-structured interviews provide considerable flexibility by allowing the researcher to be guided by the phenomena of interest to the participant. In this study, purposive sampling was achieved using inclusion criteria pertaining to the research question. Using the ranking system criteria based on the belt in combination with age employed by the International Judo Federation (IJF) and Kodokan Judo Institute, six expert coaches of forty years old and over with a minimum belt rank of 6th dan were selected as a sample. Results Both interviews and the codification process contributed to new findings regarding the application of rhythm to judo, and judo itself as a pedagogical tool. The diagrammatic model can be considered a 'guideline' to the phenomena deemed most significant. The personal significance of rhythm in judo was evidenced by the frequency with which the interviewees naturally referred to it during the interviews. A number of interviewees said that it was important for rhythm to be second nature. Rhythm was also described as an integrated and representative element in the context of training. This framework was seen as essential in providing the reader with a contextualised understanding of the phenomena considered most important for the current research. Interviewees reported various motives for employing training in rhythm such as faster technical development, better attack/defence, fitness, speed, skills acquisition, personal and spiritual growth, competition results. Conclusions This study offers first-hand accounts from professional coaches of a previously unknown phenomena, namely the use of rhythm in judo, and sheds insight on how judo experts understand rhythm in terms of training, competition, and personal growth. These findings suggest that outside of training, coaches play an important role in teaching, mentoring, and leading students. In conclusion, the research revealed four important points which form the basis of a new method of teaching judo: pedagogy, skills, rhythm and movement

    Machine Learning Approaches for the Prioritisation of Cardiovascular Disease Genes Following Genome- wide Association Study

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    Genome-wide association studies (GWAS) have revealed thousands of genetic loci, establishing itself as a valuable method for unravelling the complex biology of many diseases. As GWAS has grown in size and improved in study design to detect effects, identifying real causal signals, disentangling from other highly correlated markers associated by linkage disequilibrium (LD) remains challenging. This has severely limited GWAS findings and brought the method’s value into question. Although thousands of disease susceptibility loci have been reported, causal variants and genes at these loci remain elusive. Post-GWAS analysis aims to dissect the heterogeneity of variant and gene signals. In recent years, machine learning (ML) models have been developed for post-GWAS prioritisation. ML models have ranged from using logistic regression to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models (i.e., neural networks). When combined with functional validation, these methods have shown important translational insights, providing a strong evidence-based approach to direct post-GWAS research. However, ML approaches are in their infancy across biological applications, and as they continue to evolve an evaluation of their robustness for GWAS prioritisation is needed. Here, I investigate the landscape of ML across: selected models, input features, bias risk, and output model performance, with a focus on building a prioritisation framework that is applied to blood pressure GWAS results and tested on re-application to blood lipid traits

    Using machine learning to predict pathogenicity of genomic variants throughout the human genome

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    GeschĂ€tzt mehr als 6.000 Erkrankungen werden durch VerĂ€nderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begĂŒnstigen. All diese Prozesse mĂŒssen ĂŒberprĂŒft werden, um die zum beschriebenen PhĂ€notyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer PathogenitĂ€t. Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier prĂ€sentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores. Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells fĂŒr das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf AllelhĂ€ufigkeit basierten, Trainingsdatensatz entwickelt. Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfĂŒgbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity. Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants. The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency. In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org

    Machine Learning Meets Mental Training -- A Proof of Concept Applied to Memory Sports

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    This work aims to combine these two fields together by presenting a practical implementation of machine learning to the particular form of mental training that is the art of memory, taken in its competitive version called "Memory Sports". Such a fusion, on the one hand, strives to raise awareness about both realms, while on the other it seeks to encourage research in this mixed field as a way to, ultimately, drive forward the development of this seemingly underestimated sport.Comment: 75 pages, 47 figures, 2 tables, 26 code excerpt

    Vulnerability of the Nigerian coast and communities to climate change induced coastal erosion

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    Improving coastal resilience to climate change hazards requires understanding past shoreline changes. As the coastal population grows, evaluation and monitoring of shoreline changes are essential for planning and development. Population growth increases exposure to sea level rise and coastal hazards. Nigeria, where the study is situated, is among the top fifteen countries in the world for coastal population exposure to sea level rise. This study provided a novel lens in establishing a link between social factors and the intensifying coastal erosion along the Akwa Ibom State study coast. The mixed-method approach used in the study to assess the vulnerability of the Nigerian coast and communities to climate change-induced coastal erosion proved to be essential in gathering a wide range of data (physical, socio economic, participatory GIS maps and social learning) that contributed to a more robust and holistic assessment of coastal erosion, which is a complex issue due to the interplay between the human and natural environments. Remotely sensed data was used to examine the susceptibility and coastal evolution of Akwa Ibom State over 36 years (1984 -2020). Longer-term (1984- 2020) and short-term (2015-2020) shoreline change analyses were used to understand coastal erosion and accretion. From 1984-2020, the total average linear regression rate (LRR) was - 2.7+0.18m/yr and from 2015-2020, it was -3.94 +1.28m/yr, demonstrating an erosional trend along the study coast. Although the rate of erosion varies along the study coast, the linear regression rates (LRR) results show a predominant trend of erosion in both the short and longer term. According to the 2022 Intergovernmental Panel on Climate Change report, loss of land, loss of assets, community disruption and livelihood, loss of environmental resources, ecosystem, loss of life, or adverse health impact are all potential risks along the African coast due to climate change – this study shows that these risks are already occurring today. To quantify the anticipated future coastal erosion risk by 2040 along the study coast, the findings in this study show an overall average LRR of -2.73+ 0.99 m/yr which anticipates that coastal erosion will still be prevalent along the coast by 2040. And, given the current global climate change situation, should be expected to be much higher than the current forecasting. This study re-conceptualised the European Environmental Agency Driver-Pressure StateImpact-Response (DPSIR) model to show Hazard-Driver-Pressure-State-Impact ResponseObservation causal linkages to coastal erosion hazards. The results showed how human activities and environmental interactions have evolved through time, causing coastal erosion. Removal of vegetation cover/backstop for residential and agricultural purposes, indicate that human activities significantly contribute to the study area's susceptibility, rapid shoreline changes, and vulnerability to coastal erosion, in addition to oceanic and climate change drivers such as sea level rise and storminess. Risk perception of coastal erosion in the study area was analysed using the rhizoanalytic method proposed by Deleueze. The method demonstrates how connections and movements can be related and how data can be used to show multiplicity, mark and unmark ideas, rupture pre-conceptions and make new connections. This study shows that coastal erosion awareness is insufficient to build a long-term management plan and sustain coastal resilience. The Hino's conceptual model which provides in-depth understanding on planned retreat was used to illustrate migratory and planned retreat for the study coast where relocation has already occurred due to coastal erosion. The result fell within the Self-Reliance quadrant, indicating that people left the risk zone without government backing or retreat plans. Other coastal residents who have not relocated fell within the Hunkered Down quadrant, showing that they are willing to stay in the risk zone and cope with the threat unless the government/environmental agencies relocate them. This study shows that coastal resilience requires adaptive capacity and government support. However, multilevel governance has inhibited government-community dialogue and involvement, increasing coastal erosion vulnerability. The coastal vulnerability index to coastal erosion was calculated using the Analytical Hierarchy Process weightings. It revealed that 67.55% of the study coast falls within the high-very high vulnerability class while 32.45% is within the very low-low vulnerability class. This study developed and combined a risk perception index to coastal erosion (RPIerosion) and participatory GIS (PGIS) mapping into a novel coastal vulnerability index called the integrated coastal erosion vulnerability index (ICEVI). The case study evaluation in Akata, showed an improvement in the overall vulnerability assessment to reflect the real-world scenario, which was consistent with field data. This study demonstrated not only the presence and challenges of coastal erosion in the research area but also the relevance of involvement between the local stakeholders, government and environmental agencies. Thus, showing the potential for the perspectives of the inhabitants of these regions to inform the understanding of the resilience capacity of the people impacted, and importantly to inform future co-design and/or selection of effective adaptation methods, to better support coastal climate change resilience in these communities. Overall, the study provides a useful contribution to coastal erosion vulnerability assessments in data-scarce regions more broadly, where the mixed-methods approach used here can be applied elsewhere

    Colour technologies for content production and distribution of broadcast content

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    The requirement of colour reproduction has long been a priority driving the development of new colour imaging systems that maximise human perceptual plausibility. This thesis explores machine learning algorithms for colour processing to assist both content production and distribution. First, this research studies colourisation technologies with practical use cases in restoration and processing of archived content. The research targets practical deployable solutions, developing a cost-effective pipeline which integrates the activity of the producer into the processing workflow. In particular, a fully automatic image colourisation paradigm using Conditional GANs is proposed to improve content generalisation and colourfulness of existing baselines. Moreover, a more conservative solution is considered by providing references to guide the system towards more accurate colour predictions. A fast-end-to-end architecture is proposed to improve existing exemplar-based image colourisation methods while decreasing the complexity and runtime. Finally, the proposed image-based methods are integrated into a video colourisation pipeline. A general framework is proposed to reduce the generation of temporal flickering or propagation of errors when such methods are applied frame-to-frame. The proposed model is jointly trained to stabilise the input video and to cluster their frames with the aim of learning scene-specific modes. Second, this research explored colour processing technologies for content distribution with the aim to effectively deliver the processed content to the broad audience. In particular, video compression is tackled by introducing a novel methodology for chroma intra prediction based on attention models. Although the proposed architecture helped to gain control over the reference samples and better understand the prediction process, the complexity of the underlying neural network significantly increased the encoding and decoding time. Therefore, aiming at efficient deployment within the latest video coding standards, this work also focused on the simplification of the proposed architecture to obtain a more compact and explainable model

    The European AI Liability Directives -- Critique of a Half-Hearted Approach and Lessons for the Future

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    As ChatGPT et al. conquer the world, the optimal liability framework for AI systems remains an unsolved problem across the globe. In a much-anticipated move, the European Commission advanced two proposals outlining the European approach to AI liability in September 2022: a novel AI Liability Directive and a revision of the Product Liability Directive. They constitute the final cornerstone of EU AI regulation. Crucially, the liability proposals and the EU AI Act are inherently intertwined: the latter does not contain any individual rights of affected persons, and the former lack specific, substantive rules on AI development and deployment. Taken together, these acts may well trigger a Brussels Effect in AI regulation, with significant consequences for the US and beyond. This paper makes three novel contributions. First, it examines in detail the Commission proposals and shows that, while making steps in the right direction, they ultimately represent a half-hearted approach: if enacted as foreseen, AI liability in the EU will primarily rest on disclosure of evidence mechanisms and a set of narrowly defined presumptions concerning fault, defectiveness and causality. Hence, second, the article suggests amendments, which are collected in an Annex at the end of the paper. Third, based on an analysis of the key risks AI poses, the final part of the paper maps out a road for the future of AI liability and regulation, in the EU and beyond. This includes: a comprehensive framework for AI liability; provisions to support innovation; an extension to non-discrimination/algorithmic fairness, as well as explainable AI; and sustainability. I propose to jump-start sustainable AI regulation via sustainability impact assessments in the AI Act and sustainable design defects in the liability regime. In this way, the law may help spur not only fair AI and XAI, but potentially also sustainable AI (SAI).Comment: under peer-review; contains 3 Table

    Antimicrobial Peptides Aka Host Defense Peptides – From Basic Research to Therapy

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    This Special Issue reprint will address the most current and innovative developments in the field of HDP research across a range of topics, such as structure and function analysis, modes of action, anti-microbial effects, cell and animal model systems, the discovery of novel host-defense peptides, and drug development

    Sport team leadership coaching and captaincy in elite level rugby union football

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    A wide range of literature exists on coaching but it is concerned predominantly with the high school and college levels, is based upon athlete or coach perceptions, or is confined to observations of training or competition. As leaders of sports teams, coaches and captains have rarely been studied at the highest level of national or international sports competition. In the present study, the team leadership roles of the coach and captain in elite rugby union football in New Zealand were examined using participant observation and other qualitative research methods. Elite was defined as New Zealand rugby’s highest internal level of competition: (a) the national provincial championships and (b) international test matches of the national team, the All Blacks. The study explored the roles of the elite rugby coach and captain in vivo in a wide variety of team situations. It was felt that this could provide first-hand information on particular team leader behaviours, on what a coach and captain actually do, and how they are perceived by those around them. The main objective, however, was to use grounded theory techniques to create a model of elite rugby team leadership that might guide developmental programmes on such leadership. The research phases undertaken were those of participant observation with a Provincial Team for five matches, a survey of provincial teams’ coaches and captains on their leadership associated with actual matches, three years’ participant observation with the All Blacks (including observation in eight test match weeks), multiple perspectives on elite team leadership from past rugby test players in New Zealand and overseas, and interviews with national team leaders in sports other than rugby. Participant observation, interviews, questionnaires and document analysis generated data from the research settings. These data were considered in terms of symbolic interactionism and subjected to a grounded theory process. This led to a set of elite rugby team leadership categories and properties which, in turn, generated a comprehensive set of theoretical propositions. The propositions became the basis for a model of elite rugby team leadership. This model was then considered as the basis for a programme to develop elite rugby team leaders. Significant aspects of the research findings which have not featured in previous research literature included the coach’s vision, team culture, centrality of the game plan, match week build-up, the importance of the captain’s playing example, the coach's ability to utilise teaching precepts, the coach’s personal qualities, and the need to develop and evaluate team leaders. The model, and the developmental programme principles emanating from it, are seen as relevant for developing elite level leaders in team sports other than rugby
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