1,752 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 BiotecnologĂ­a, IngenierĂ­a y TecnologĂ­a QuĂ­micaLĂ­nea de InvestigaciĂłn: IngenierĂ­a, Ciencia de Datos y BioinformĂĄticaClave Programa: DBICĂłdigo LĂ­nea: 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 InformĂĄtic

    Undergraduate Catalog of Studies, 2022-2023

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    A study of the inter-relationship of identity and urban heritage in Chiang Mai Old City, Thailand

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    The urban heritage identity of historical cities has received growing attention due to the weakening of their urban identity. For this reason, urban identity has been identified as a preliminary study of this research. Forty years ago, many researchers attempted to explain a broader understanding of urban heritage identity, which is relevant to human factors that affect urban, place, and built environment relationships. This involved the three interrelated concepts of identity: distinctiveness; urban heritage; and place attachment. These establish a balance between people and their identification with places. Urban heritage identity is associated a place's physicality and heritage attributes that reflect socio-cultural values. It can be concluded that urban heritage identity becomes significant through concepts of environmental psychology. Distinctiveness theory, as a part of identity theory, has been used in this study to describe the genuine perception of local participants and is a fundamental part of defining place identity. Furthermore, the definition of place attachment has been used to explain the relationship of distinct places on time of residence, frequency of use, emotional, physical, social, and activities. The study also explores Chiang Mai Old City’s built environment, which especially analyses the façade and streetscape characteristics that reflect the city's socio-cultural value. The research concludes with suggestions for preserving the city's urban heritage characteristics. Chiang Mai Old City has unprecedented diversity and cultural dynamics related to its intangible and tangible urban heritage. Moreover, the city is in the critical stage of being nominated as a new World Heritage Site by UNESCO, with the city's distinctiveness and place attachment being significant in supporting further heritage management strategies. The research mainly focuses on how local people interpret and understand the urban heritage identity of Chiang Mai Old City. This has been achieved through surveys of four hundred participants living in the Old City, two-way focus groups with five participants in each group, in-depth interviews with twenty-five participants, and ten architects drawing suggestions for further built environment management strategies. The results are described through seven aspects that explore the distinctiveness and place attachment theories of Chiang Mai Old City. The findings can be described in seven aspects: historical value; cultural activities; a particular character; landmark; identity; community; and everyday life. The results reveal that there are five distinct places in the city: Pra Singha Temple; Chedi Luang Temple; Three Kings monument square; Tha-Pare gate square; and Chiang Mai Old City's Moat. The results can also be used to develop an assessment indicator for defining the distinctiveness of other historic cities through the engagement of local people. The study repeatedly employs distinct places to describe in-place attachment theory. The results reveal positivity, emotion, and the spiritual anchor of place attached to local people in social engagement, explicitly divulging the rootedness of religion, culture, and community activities through the length of time. All five distinct places have an inseparable ability to display tangible heritage value and such a positive emotion to places is crucial in contributing to urban heritage characteristics. Moreover, the time or length of residency is a vital aspect to people’s perception of the city's distinctiveness; however, the value of the physical setting itself can increase the sense of belonging of newcomers.This research used a mixed methods approach in defining place identity process and socio-cultural values in distinctive streetscapes scenes in the city. This study strongly believes that the findings demonstrate that local people can help to develop the management of the city. The results presented suggest that the heritage value of streetscapes is related to historical attributes, natural objects, people, and cultural events in the scenes that explain the meanings ascribed to places associated with social and cultural values. The built environment characteristics and heritage value can be assumed from human experience. The study can be a new perspective for local authorities, urban designers, and heritage teams to determine whether projects will strengthen the existing urban heritage identity. Most importantly, this research has revealed new perspectives on urban heritage identity and practical study methods whilst also contributing to management strategies. In addition, continuing research into urban heritage identity will significantly improve knowledge development, practical support, and collaboration with local people and architects to establish and maintain cherished distinct places and living environments for urban residents

    Soil histories continue to structure the bacterial and oomycete communities of Brassicaceae host plants through time on the Canadian prairies

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    Afin d’étudier l’écologie microbienne, il est nĂ©cessaire, dans un premier temps, de dĂ©terminer quels micro-organismes sont prĂ©sents dans un milieu et Ă  quel instant. Ces informations sont requises pour pouvoir ensuite dĂ©velopper des outils permettant de prĂ©dire l’assemblage des communautĂ©s et les fonctions que celles-ci peuvent contenir. Cependant, la multitude des processus entrant en jeu dans la structure et la composition des communautĂ©s microbiennes, rendent leur Ă©tude complexe. Parmi les nombreux processus Ă  Ă©tudier, il est notamment question de l’échelle temporelle Ă  prendre en compte pour comprendre l’assemblage des communautĂ©s microbiennes. En effet, les Ă©vĂ©nements historiques conditionnent la composition et la biodiversitĂ© des futures communautĂ©s microbiennes. Pourtant, dans les sols, peu d’études se sont intĂ©ressĂ©es Ă  l’impact des Ă©vĂ©nements historiques dans l’assemblage des communautĂ©s microbiennes. Par consĂ©quent, l’objectif de cette thĂšse Ă©tait de quantifier comment les diffĂ©rentes histoires du sol ont influencĂ© la structure et biodiversitĂ© des communautĂ©s bactĂ©riennes et oomycĂštes associĂ©es aux plantes hĂŽtes des Brassicaceae Ă  travers le temps. Les rotations de cultures de Brassicaceae sont de plus en plus courantes dans le monde et ont dĂ©montrĂ© des avantages pour les cultures concernĂ©es, telles que la rĂ©tention de l’humiditĂ© du sol ou la suppression de certains agents pathogĂšnes des plantes. En revanche, l’impact des rotations de cultures de Brassicaceae sur la structure et biodiversitĂ© des communautĂ©s microbiennes rĂ©sidentes est peu connu. Ainsi, des terrains agricoles des prairies canadiennes ayant des expĂ©riences de rotations de cultures en cours ont Ă©tĂ© utilisĂ©s pour modĂ©liser l’impact des histoires de sol prĂ©cĂ©demment Ă©tablies sur les futures communautĂ©s microbiennes. Les communautĂ©s microbiennes des racines, de la rhizosphĂšre, et du sol Ă©loignĂ© des racines des Brassicaceae ont Ă©tĂ© Ă©tudiĂ©es grĂące aux mĂ©tabarcodes d’ARNr 16S ou ITS. La PCR quantitative et des mĂ©thodes phylogĂ©nĂ©tiques ont Ă©tĂ© utilisĂ©es pour amĂ©liorer l’analyse des communautĂ©s microbiennes. Cette thĂšse illustre comment diffĂ©rentes histories de sol Ă©tablies par les cultures de l’annĂ©e prĂ©cĂ©dente ont continuĂ© Ă  structurer les communautĂ©s microbiennes de la rhizosphĂšre tout au long de la saison de croissance, Ă  diffĂ©rents stades de croissance, jusqu’à un an aprĂšs leur Ă©tablissement. Cependant, le phĂ©nomĂšne de rĂ©troactions entre plantes et micro-organismes a permis de masquer cet hĂ©ritage dans la rhizosphere de diffĂ©rentes espĂšces hĂŽtes de Brassicacea pour lesquelles des communautĂ©s bactĂ©riennes phylogĂ©nĂ©tiquement similaires ont Ă©tĂ© retrouvĂ©es malgrĂ© diverses histoires du sol. Nos rĂ©sultats montrent Ă©galement que les diffĂ©rentes espĂšces hĂŽtes de Brassicacea n’avaient pas d’impact sur la structure des communautĂ©s d’oomycĂštes et que le stress hydrique limitait Ă©galement cette structuration pour les communautĂ©s bactĂ©riennes. Dans ces deux cas, l’effet de l’histoire du sol Ă©tait donc encore visible sur la structure les communautĂ©s microbiennes durant l’annĂ©e subsĂ©quente. Les dĂ©couvertes selon lesquelles diffĂ©rentes histoires de sol persistent jusqu'Ă  un an, mĂȘme en prĂ©sence de nouvelles plantes hĂŽtes, et qu’elles peuvent continuer Ă  façonner les communautĂ©s microbiennes ont des implications importantes pour la gestion agricole et les recherches futures sur les composants physiques de l'histoire du sol. Comprendre comment l'histoire du sol est impliquĂ©e dans la structure et la biodiversitĂ© des communautĂ©s microbiennes Ă  travers le temps est une limitation de l'Ă©cologie microbienne et est nĂ©cessaire pour utiliser les technologies microbiennes Ă  l'avenir pour une agriculture durable et dans toute la sociĂ©tĂ©.A fundamental task of microbial ecology is determining which organisms are present, and when, in order to improve the predictive models of community assembly and functions. However, the heterogeneity of community assembly processes that underlie how microbial communities are formed and structured are makes assembly of taxonomic and functional profiles difficult. One reason for this challenge is the compounding effect temporal scales have on microbial communities. For example, historical events have been shown to condition future microbial community composition and biodiversity. Yet, how historical events structure microbial communities in the soil has not been well tested. Therefore, the objective of this thesis was to quantify how different soil histories influenced the structure and biodiversity of bacterial and oomycete communities associated with Brassicaceae host plants through time. Brassicaceae crop rotations are increasingly common globally, and have demonstrated benefits for the crops involved, such as retaining soil moisture, or suppressing certain plant pathogens. In contrast, there is a lack of knowledge surrounding how Brassicaceae crop rotations impact the structure and biodiversity of resident microbial communities. As such, on-going agricultural field experiments with crop rotations on the Canadian prairies were used to model how previously established soil histories impacted future microbial communities. The Brassicaceae microbial communities were inferred from the roots, rhizosphere and bulk soil using 16S rRNA or ITS metabarcodes. Quantitative PCR and phylogenetic methods were used to improve the analysis of the microbial communities. This thesis illustrates how different soil histories established by the previous year’s crops continued to structure the microbial rhizosphere communities throughout the growing season, at various growth stages, and up to a year after being established. However, active plant-soil microbial feedback allowed different Brassicaceae host species to mask the soil history in the rhizosphere and derive phylogenetically similar bacterial communities from these diverse soil histories. Furthermore, host plants were unable to structure the oomycete communities, and lost the ability to structure the bacterial rhizosphere communities under water stress. In both circumstances, the soil history continued to structure the microbial communities. The findings that different soil histories persist for up to a year, even in the presence of new host plants, and can continue to shape microbial communities has important implications for agricultural management and future research on the physical components of soil history. Understanding how soil history is involved in the structure and biodiversity of microbial communities through time is a limitation in microbial ecology and is required for employing microbial technologies in the future for sustainable agriculture and throughout society

    IMAGINING, GUIDING, PLAYING INTIMACY: - A Theory of Character Intimacy Games -

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    Within the landscape of Japanese media production, and video game production in particular, there is a niche comprising video games centered around establishing, developing, and fulfilling imagined intimate relationships with anime-manga characters. Such niche, although very significant in production volume and lifespan, is left unexplored or underexplored. When it is not, it is subsumed within the scope of wider anime-manga media. This obscures the nature of such video games, alternatively identified with descriptors including but not limited to ‘visual novel’, ‘dating simulator’ and ‘adult computer game’. As games centered around developing intimacy with characters, they present specific ensembles of narrative content, aesthetics and software mechanics. These ensembles are aimed at eliciting in users what are, by all intents and purposes, parasocial phenomena towards the game’s characters. In other words, these software products encourage players to develop affective and bodily responses towards characters. They are set in a way that is coherent with shared, circulating scripts for sexual and intimate interaction to guide player imaginative action. This study defines games such as the above as ‘character intimacy games’, video game software where traversal is contingent on players knowingly establishing, developing, and fulfilling intimate bonds with fictional characters. To do so, however, player must recognize themselves as playing that type of game, and to be looking to develop that kind of response towards the game’s characters. Character Intimacy Games are contingent upon player developing affective and bodily responses, and thus presume that players are, at the very least, non-hostile towards their development. This study approaches Japanese character intimacy games as its corpus, and operates at the intersection of studies of communication, AMO studies and games studies. The study articulates a research approach based on the double need of approaching single works of significance amidst a general scarcity of scholarly background on the subject. It juxtaposes data-driven approaches derived from fan-curated databases – The Visual Novel Database and Erogescape -Erogē Hyƍron KĆ«kan – with a purpose-created ludo-hermeneutic process. By deploying an observation of character intimacy games through fan-curated data and building ludo-hermeneutics on the resulting ontology, this study argues that character intimacy games are video games where traversal is contingent on players knowingly establishing, developing, and fulfilling intimate bonds with fictional characters and recognizing themselves as doing so. To produce such conditions, the assemblage of software mechanics and narrative content in such games facilitates intimacy between player and characters. This is, ultimately, conductive to the emergence of parasocial phenomena. Parasocial phenomena, in turn, are deployed as an integral assumption regarding player activity within the game’s wider assemblage of narrative content and software mechanics

    Digital agriculture: research, development and innovation in production chains.

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    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil

    Improving Outcomes in Machine Learning and Data-Driven Learning Systems using Structural Causal Models

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    The field of causal inference has experienced rapid growth and development in recent years. Its significance in addressing a diverse array of problems and its relevance across various research and application domains are increasingly being acknowledged. However, the current state-of-the-art approaches to causal inference have not yet gained widespread adoption in mainstream data science practices. This research endeavor begins by seeking to motivate enthusiasm for contemporary approaches to causal investigation utilizing observational data. It explores the existing applications and potential future prospects for employing causal inference methods to enhance desired outcomes in data-driven learning applications across various domains, with a particular focus on their relevance in artificial intelligence (AI). Following this motivation, this dissertation proceeds to offer a broad review of fundamental concepts, theoretical frameworks, methodological advancements, and existing techniques pertaining to causal inference. The research advances by investigating the problem of data-driven root cause analysis through the lens of causal structure modeling. Data-driven approaches to root cause analysis (RCA) have received attention recently due to their ability to exploit increasing data availability for more effective root cause identification in complex processes. Advancements in the field of causal inference enable unbiased causal investigations using observational data. This study proposes a data-driven RCA method and a time-to-event (TTE) data simulation procedure built on the structural causal model (SCM) framework. A novel causality-based method is introduced for learning a representation of root cause mechanisms, termed in this work as root cause graphs (RCGs), from observational TTE data. Three case scenarios are used to generate TTE datasets for evaluating the proposed method. The utility of the proposed RCG recovery method is demonstrated by using recovered RCGs to guide the estimation of root cause treatment effects. In the presence of mediation, RCG-guided models produce superior estimates of root cause total effects compared to models that adjust for all covariates. The author delves into the subject of integrating causal inference and machine learning. Incorporating causal inference into machine learning offers many benefits including enhancing model interpretability and robustness to changes in data distributions. This work considers the task of feature selection for prediction model development in the context of potentially changing environments. First, a filter feature selection approach that improves on the select k-best method and prioritizes causal features is introduced and compared to the standard select k-best algorithm. Secondly, a causal feature selection algorithm which adapts to covariate shifts in the target domain is proposed for domain adaptation. Causal approaches to feature selection are demonstrated to be capable of yielding optimal prediction performance when modeling assumptions are met. Additionally, they can mitigate the degrading effects of some forms of dataset shifts on prediction performance

    Measuring the impact of COVID-19 on hospital care pathways

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    Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted
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