320 research outputs found

    Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data

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    Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques. Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Elementary School Leadership, Climate, and Resilience during COVID-19: A Comparative Case Study of Two Independent Schools

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    The recent worldwide pandemic impacted educational systems on a global scale, forcing school leaders to reimagine educational structures as they faced the ongoing wounding of the unprecedented, shared trauma wrought by COVID-19. Mandated U.S. school closures in March of 2020 forced an immediate transition to distance learning and presented unforeseen academic and social challenges for students, educators, parents, and school leaders. As school campuses re-opened over the next year, the pandemic continued to present hardships. School leaders were tasked with developing systems to follow appropriate health and safety measures, develop systems to accommodate stakeholders’ individual health circumstances, and communicate school policies regularly to those affected by them while still prioritizing the needs of students and their academic progress. Using comparative case-study methodology, this study explored the relationship between school leadership, school climate and organizational resilience in response to the ongoing wounding of COVID-19 from its onset in March of 2020 to the declared end of the pandemic in May of 2023, at two small independent elementary schools. This study illuminated the experiences of the schools’ leaders and provided actionable and transferable guidelines for educational leaders facing organizational trauma or crisis. The five key findings support practical implications for school leaders striving to support organizational resilience. They include: the importance of positive school climate, enhanced communication, adaptive capacity, organizational structure and embracing change. The study concludes with implications for future research. This dissertation is available in open access at AURA (https://aura.antioch.edu/) and OhioLINK ETD Center (https://etd.ohiolink.edu/)

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    Training Load and Injury in Professional Ballet

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    In professional ballet, training load has frequently been suggested to be associated with the risk of musculoskeletal injury. Despite a recent surge in the number of training load research studies in high performance sport, relatively little research has been conducted investigating training load in ballet. The aim of this thesis was, therefore, to describe the training loads undertaken by professional ballet dancers, explore the load-injury relationship in ballet, and provide valid methods and recommendations for load management in professional ballet. Two five-season cohort studies were conducted, investigating scheduling and medical data at an elite professional ballet company. Shared frailty models were used to investigate relationships between individual risk factors, accumulated dance volume, and hazard ratios for injury risk. Greater week-to-week changes in dance volume and smaller seven-day dance volume were associated with increase rates of overuse injury, whilst age (traumatic injury), previous injury, and company rank (overuse injury) were also associated with increases in hazard ratios for injury. Analyses of scheduling data were consistent with previous research regarding the large rehearsal and performance volumes completed by ballet dancers. For the first time, however, the present research revealed the large variation in dance hours occurring from week-to-week, across the season, and between company ranks. In professional ballet, there is great scope to optimise training loads from increased emphasis on periodisation of the repertoire and rehearsal schedule alone. Three methodological studies explored the development and validation of training load measures in professional ballet. Firstly, the validity of session rating of perceived exertion in professional ballet dancers was investigated, revealing very large positive linear relationships with Edwards and Banister training impulse scores. Correlation coefficients were comparable across men and women, though were larger in rehearsals compared with ballet class. Secondly, a rule-based classifier for measuring jump frequency and height from accelerometer data was developed and validated, demonstrating a high degree of accuracy, and providing a simple means of managing jump load. Finally, a series of recurrent neural networks were developed to facilitate the measurement of tissue-specific forces outside of a laboratory using inertial measurement units, outperforming single variable linear regression approaches for the measurement of Achilles tendon, patellar tendon, and tibial force. Open-source software was developed and presented to house these algorithms, and database and visualize longitudinal training load data. This thesis demonstrates the importance of managing a rehearsal and performance schedule throughout a professional ballet season. Where more in-depth understanding of training load is required for managing high-risk dancers, this thesis provides practical, valid, and open-source methods for quantifying load
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