1,693 research outputs found

    Inter-individual variation of the human epigenome & applications

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    Inter-individual variation of the human epigenome & applications

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    Genome-wide association studies (GWAS) have led to the discovery of genetic variants influencing human phenotypes in health and disease. However, almost two decades later, most human traits can still not be accurately predicted from common genetic variants. Moreover, genetic variants discovered via GWAS mostly map to the non-coding genome and have historically resisted interpretation via mechanistic models. Alternatively, the epigenome lies in the cross-roads between genetics and the environment. Thus, there is great excitement towards the mapping of epigenetic inter-individual variation since its study may link environmental factors to human traits that remain unexplained by genetic variants. For instance, the environmental component of the epigenome may serve as a source of biomarkers for accurate, robust and interpretable phenotypic prediction on low-heritability traits that cannot be attained by classical genetic-based models. Additionally, its research may provide mechanisms of action for genetic associations at non-coding regions that mediate their effect via the epigenome. The aim of this thesis was to explore epigenetic inter-individual variation and to mitigate some of the methodological limitations faced towards its future valorisation.Chapter 1 is dedicated to the scope and aims of the thesis. It begins by describing historical milestones and basic concepts in human genetics, statistical genetics, the heritability problem and polygenic risk scores. It then moves towards epigenetics, covering the several dimensions it encompasses. It subsequently focuses on DNA methylation with topics like mitotic stability, epigenetic reprogramming, X-inactivation or imprinting. This is followed by concepts from epigenetic epidemiology such as epigenome-wide association studies (EWAS), epigenetic clocks, Mendelian randomization, methylation risk scores and methylation quantitative trait loci (mQTL). The chapter ends by introducing the aims of the thesis.Chapter 2 focuses on stochastic epigenetic inter-individual variation resulting from processes occurring post-twinning, during embryonic development and early life. Specifically, it describes the discovery and characterisation of hundreds of variably methylated CpGs in the blood of healthy adolescent monozygotic (MZ) twins showing equivalent variation among co-twins and unrelated individuals (evCpGs) that could not be explained only by measurement error on the DNA methylation microarray. DNA methylation levels at evCpGs were shown to be stable short-term but susceptible to aging and epigenetic drift in the long-term. The identified sites were significantly enriched at the clustered protocadherin loci, known for stochastic methylation in neurons in the context of embryonic neurodevelopment. Critically, evCpGs were capable of clustering technical and longitudinal replicates while differentiating young MZ twins. Thus, discovered evCpGs can be considered as a first prototype towards universal epigenetic fingerprint, relevant in the discrimination of MZ twins for forensic purposes, currently impossible with standard DNA profiling. Besides, DNA methylation microarrays are the preferred technology for EWAS and mQTL mapping studies. However, their probe design inherently assumes that the assayed genomic DNA is identical to the reference genome, leading to genetic artifacts whenever this assumption is not fulfilled. Building upon the previous experience analysing microarray data, Chapter 3 covers the development and benchmarking of UMtools, an R-package for the quantification and qualification of genetic artifacts on DNA methylation microarrays based on the unprocessed fluorescence intensity signals. These tools were used to assemble an atlas on genetic artifacts encountered on DNA methylation microarrays, including interactions between artifacts or with X-inactivation, imprinting and tissue-specific regulation. Additionally, to distinguish artifacts from genuine epigenetic variation, a co-methylation-based approach was proposed. Overall, this study revealed that genetic artifacts continue to filter through into the reported literature since current methodologies to address them have overlooked this challenge.Furthermore, EWAS, mQTL and allele-specific methylation (ASM) mapping studies have all been employed to map epigenetic variation but require matching phenotypic/genotypic data and can only map specific components of epigenetic inter-individual variation. Inspired by the previously proposed co-methylation strategy, Chapter 4 describes a novel method to simultaneously map inter-haplotype, inter-cell and inter-individual variation without these requirements. Specifically, binomial likelihood function-based bootstrap hypothesis test for co-methylation within reads (Binokulars) is a randomization test that can identify jointly regulated CpGs (JRCs) from pooled whole genome bisulfite sequencing (WGBS) data by solely relying on joint DNA methylation information available in reads spanning multiple CpGs. Binokulars was tested on pooled WGBS data in whole blood, sperm and combined, and benchmarked against EWAS and ASM. Our comparisons revealed that Binokulars can integrate a wide range of epigenetic phenomena under the same umbrella since it simultaneously discovered regions associated with imprinting, cell type- and tissue-specific regulation, mQTL, ageing or even unknown epigenetic processes. Finally, we verified examples of mQTL and polymorphic imprinting by employing another novel tool, JRC_sorter, to classify regions based on epigenotype models and non-pooled WGBS data in cord blood. In the future, we envision how this cost-effective approach can be applied on larger pools to simultaneously highlight regions of interest in the methylome, a highly relevant task in the light of the post-GWAS era.Moving towards future applications of epigenetic inter-individual variation, Chapters 5 and 6 are dedicated to solving some of methodological issues faced in translational epigenomics.Firstly, due to its simplicity and well-known properties, linear regression is the starting point methodology when performing prediction of a continuous outcome given a set of predictors. However, linear regression is incompatible with missing data, a common phenomenon and a huge threat to the integrity of data analysis in empirical sciences, including (epi)genomics. Chapter 5 describes the development of combinatorial linear models (cmb-lm), an imputation-free, CPU/RAM-efficient and privacy-preserving statistical method for linear regression prediction on datasets with missing values. Cmb-lm provide prediction errors that take into account the pattern of missing values in the incomplete data, even at extreme missingness. As a proof-of-concept, we tested cmb-lm in the context of epigenetic ageing clocks, one of the most popular applications of epigenetic inter-individual variation. Overall, cmb-lm offer a simple and flexible methodology with a wide range of applications that can provide a smooth transition towards the valorisation of linear models in the real world, where missing data is almost inevitable. Beyond microarrays, due to its high accuracy, reliability and sample multiplexing capabilities, massively parallel sequencing (MPS) is currently the preferred methodology of choice to translate prediction models for traits of interests into practice. At the same time, tobacco smoking is a frequent habit sustained by more than 1.3 billion people in 2020 and a leading (and preventable) health risk factor in the modern world. Predicting smoking habits from a persistent biomarker, such as DNA methylation, is not only relevant to account for self-reporting bias in public health and personalized medicine studies, but may also allow broadening forensic DNA phenotyping. Previously, a model to predict whether someone is a current, former, or never smoker had been published based on solely 13 CpGs from the hundreds of thousands included in the DNA methylation microarray. However, a matching lab tool with lower marker throughput, and higher accuracy and sensitivity was missing towards translating the model in practice. Chapter 6 describes the development of an MPS assay and data analysis pipeline to quantify DNA methylation on these 13 smoking-associated biomarkers for the prediction of smoking status. Though our systematic evaluation on DNA standards of known methylation levels revealed marker-specific amplification bias, our novel tool was still able to provide highly accurate and reproducible DNA methylation quantification and smoking habit prediction. Overall, our MPS assay allows the technological transfer of DNA methylation microarray findings and models to practical settings, one step closer towards future applications.Finally, Chapter 7 provides a general discussion on the results and topics discussed across Chapters 2-6. It begins by summarizing the main findings across the thesis, including proposals for follow-up studies. It then covers technical limitations pertaining bisulfite conversion and DNA methylation microarrays, but also more general considerations such as restricted data access. This chapter ends by covering the outlook of this PhD thesis, including topics such as bisulfite-free methods, third-generation sequencing, single-cell methylomics, multi-omics and systems biology.<br/

    Functional Nanomaterials and Polymer Nanocomposites: Current Uses and Potential Applications

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    This book covers a broad range of subjects, from smart nanoparticles and polymer nanocomposite synthesis and the study of their fundamental properties to the fabrication and characterization of devices and emerging technologies with smart nanoparticles and polymer integration

    Temperature Reduction Technologies Meet Asphalt Pavement: Green and Sustainability

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    This Special Issue, "Temperature Reduction Technologies Meet Asphalt Pavement: Green and Sustainability", covers various subjects related to advanced temperature reduction technologies in bituminous materials. It can help civil engineers and material scientists better identify underlying views for sustainable pavement constructions

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Data- og ekspertdreven variabelseleksjon for prediktive modeller i helsevesenet : mot økt tolkbarhet i underbestemte maskinlæringsproblemer

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    Modern data acquisition techniques in healthcare generate large collections of data from multiple sources, such as novel diagnosis and treatment methodologies. Some concrete examples are electronic healthcare record systems, genomics, and medical images. This leads to situations with often unstructured, high-dimensional heterogeneous patient cohort data where classical statistical methods may not be sufficient for optimal utilization of the data and informed decision-making. Instead, investigating such data structures with modern machine learning techniques promises to improve the understanding of patient health issues and may provide a better platform for informed decision-making by clinicians. Key requirements for this purpose include (a) sufficiently accurate predictions and (b) model interpretability. Achieving both aspects in parallel is difficult, particularly for datasets with few patients, which are common in the healthcare domain. In such cases, machine learning models encounter mathematically underdetermined systems and may overfit easily on the training data. An important approach to overcome this issue is feature selection, i.e., determining a subset of informative features from the original set of features with respect to the target variable. While potentially raising the predictive performance, feature selection fosters model interpretability by identifying a low number of relevant model parameters to better understand the underlying biological processes that lead to health issues. Interpretability requires that feature selection is stable, i.e., small changes in the dataset do not lead to changes in the selected feature set. A concept to address instability is ensemble feature selection, i.e. the process of repeating the feature selection multiple times on subsets of samples of the original dataset and aggregating results in a meta-model. This thesis presents two approaches for ensemble feature selection, which are tailored towards high-dimensional data in healthcare: the Repeated Elastic Net Technique for feature selection (RENT) and the User-Guided Bayesian Framework for feature selection (UBayFS). While RENT is purely data-driven and builds upon elastic net regularized models, UBayFS is a general framework for ensembles with the capabilities to include expert knowledge in the feature selection process via prior weights and side constraints. A case study modeling the overall survival of cancer patients compares these novel feature selectors and demonstrates their potential in clinical practice. Beyond the selection of single features, UBayFS also allows for selecting whole feature groups (feature blocks) that were acquired from multiple data sources, as those mentioned above. Importance quantification of such feature blocks plays a key role in tracing information about the target variable back to the acquisition modalities. Such information on feature block importance may lead to positive effects on the use of human, technical, and financial resources if systematically integrated into the planning of patient treatment by excluding the acquisition of non-informative features. Since a generalization of feature importance measures to block importance is not trivial, this thesis also investigates and compares approaches for feature block importance rankings. This thesis demonstrates that high-dimensional datasets from multiple data sources in the medical domain can be successfully tackled by the presented approaches for feature selection. Experimental evaluations demonstrate favorable properties of both predictive performance, stability, as well as interpretability of results, which carries a high potential for better data-driven decision support in clinical practice.Moderne datainnsamlingsteknikker i helsevesenet genererer store datamengder fra flere kilder, som for eksempel nye diagnose- og behandlingsmetoder. Noen konkrete eksempler er elektroniske helsejournalsystemer, genomikk og medisinske bilder. Slike pasientkohortdata er ofte ustrukturerte, høydimensjonale og heterogene og hvor klassiske statistiske metoder ikke er tilstrekkelige for optimal utnyttelse av dataene og god informasjonsbasert beslutningstaking. Derfor kan det være lovende å analysere slike datastrukturer ved bruk av moderne maskinlæringsteknikker for å øke forståelsen av pasientenes helseproblemer og for å gi klinikerne en bedre plattform for informasjonsbasert beslutningstaking. Sentrale krav til dette formålet inkluderer (a) tilstrekkelig nøyaktige prediksjoner og (b) modelltolkbarhet. Å oppnå begge aspektene samtidig er vanskelig, spesielt for datasett med få pasienter, noe som er vanlig for data i helsevesenet. I slike tilfeller må maskinlæringsmodeller håndtere matematisk underbestemte systemer og dette kan lett føre til at modellene overtilpasses treningsdataene. Variabelseleksjon er en viktig tilnærming for å håndtere dette ved å identifisere en undergruppe av informative variabler med hensyn til responsvariablen. Samtidig som variabelseleksjonsmetoder kan lede til økt prediktiv ytelse, fremmes modelltolkbarhet ved å identifisere et lavt antall relevante modellparametere. Dette kan gi bedre forståelse av de underliggende biologiske prosessene som fører til helseproblemer. Tolkbarhet krever at variabelseleksjonen er stabil, dvs. at små endringer i datasettet ikke fører til endringer i hvilke variabler som velges. Et konsept for å adressere ustabilitet er ensemblevariableseleksjon, dvs. prosessen med å gjenta variabelseleksjon flere ganger på en delmengde av prøvene i det originale datasett og aggregere resultater i en metamodell. Denne avhandlingen presenterer to tilnærminger for ensemblevariabelseleksjon, som er skreddersydd for høydimensjonale data i helsevesenet: "Repeated Elastic Net Technique for feature selection" (RENT) og "User-Guided Bayesian Framework for feature selection" (UBayFS). Mens RENT er datadrevet og bygger på elastic net-regulariserte modeller, er UBayFS et generelt rammeverk for ensembler som muliggjør inkludering av ekspertkunnskap i variabelseleksjonsprosessen gjennom forhåndsbestemte vekter og sidebegrensninger. En case-studie som modellerer overlevelsen av kreftpasienter sammenligner disse nye variabelseleksjonsmetodene og demonstrerer deres potensiale i klinisk praksis. Utover valg av enkelte variabler gjør UBayFS det også mulig å velge blokker eller grupper av variabler som representerer de ulike datakildene som ble nevnt over. Kvantifisering av viktigheten av variabelgrupper spiller en nøkkelrolle for forståelsen av hvorvidt datakildene er viktige for responsvariablen. Tilgang til slik informasjon kan føre til at bruken av menneskelige, tekniske og økonomiske ressurser kan forbedres dersom informasjonen integreres systematisk i planleggingen av pasientbehandlingen. Slik kan man redusere innsamling av ikke-informative variabler. Siden generaliseringen av viktighet av variabelgrupper ikke er triviell, undersøkes og sammenlignes også tilnærminger for rangering av viktigheten til disse variabelgruppene. Denne avhandlingen viser at høydimensjonale datasett fra flere datakilder fra det medisinske domenet effektivt kan håndteres ved bruk av variabelseleksjonmetodene som er presentert i avhandlingen. Eksperimentene viser at disse kan ha positiv en effekt på både prediktiv ytelse, stabilitet og tolkbarhet av resultatene. Bruken av disse variabelseleksjonsmetodene bærer et stort potensiale for bedre datadrevet beslutningsstøtte i klinisk praksis

    The Dangerous Loss of Meaning Through Consumerist Practices

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    The internet appears and it is a better television, a better radio, and a better way of transmitting information to one another, but is this information actually being communicated? In this essay, I will be looking at Jodi Dean’s Blog Theory: Feedback and Capture in the Circuits of Drive to describe how an overabundance of content provided by the internet renders all content meaningless, and the ways in which engagement with the internet generates more behaviors in people whose purpose is purely the act of communication, and not at all the contents of what is being communicated. Finally, I will turn to Bernard Stiegler’s The Age of Disruption: Technology and Madness in Computational Capitalism, where he describes the consequences of newer technologies and devices that have not had the opportunity to be considered given the abundance and demand for new content to be reacted to. With no opportunity or time to think, Stiegler sees the world heading towards demise, a demise that he nonetheless faces courageously in an attempt to avoid this inevitable fate. We conclude by seeing whether this attempt is a worthwhile endeavor

    Nanofluids with optimised thermal properties based on metal chalcogenides with different morphology

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    Over the last decades, the interest around renewable energies has increased considerably because of the growing energy demand and the environmental problems derived from fossil fuels combustion. In this scenario, concentrating solar power (CSP) is a renewable energy with a high potential to cover the global energy demand. However, improving the efficiency and reducing the cost of technologies based on this type of energy to make it more competitive is still a work in progress. One of the current lines of research is the replacement of the heat transfer fluid used in the absorber tube of parabolic trough collectors with nano-colloidal suspensions of nanomaterials in a base fluid, typically named nanofluids. Nanofluids are considered as a new generation of heat transfer fluids since they exhibit thermophysical properties improvements compared with conventional heat transfer fluids. But there are still some barriers to overcome for the implementation of nanofluids. For example, obtaining nanofluids with high stability is a priority challenge for this kind of system. Also ensuring that nanoparticles will not clog pipes or cause pressure drops. In this Doctoral Thesis, the use of transition metal dichalcogenide-based nanofluids as a heat transfer fluid in solar power plants has been investigated for the first time. Specifically, nanofluids based on one-dimensional, two-dimensional and three-dimensional MoS2 , WS2 and WSe2 nanostructures have been researched. The base fluid used in the preparation of these nanofluids is the eutectic mixture of biphenyl and diphenyl oxide typically employed as heat transfer fluid in concentrating solar power plants. Mainly two preparation methods have been explored: the liquid phase exfoliation method, and the solvothermal synthesis of the nanomaterial and its subsequent dispersion in the thermal oil by ultrasound. Experimental parameters such as surfactant concentration, time and sonication frequency for preparation of nanofluids have also been analysed. The nanofluids have been subjected to an extensive characterisation which includes the study of colloidal stability over time, characterisation of thermal properties such as isobaric specific heat or thermal conductivity, rheological properties and optical properties. The results have revealed that nanofluids based on 1D and 2D nanostructures of transition metal dichalcogenides are colloidally stable over time and exhibit improved thermal properties compared to the typical thermal fluid used in solar power plants. The most promising nanofluids are those based on MoS 2 nanosheets and those based on WSe 2 nanosheets with heat transfer coefficient improvements of 36.2% and 34.1% respectively with respect to thermal oil. Furthermore, the dramatic role of WSe2 nanosheets in enhancing optical extinction of the thermal oil suggests the use of these nanofluids in direct absorption solar collectors. In conclusion, the present work demonstrates the feasibility of using nanofluids based on transition metal dichalcogenide nanostructures as heat transfer fluids in concentrating solar power plants based on parabolic trough collectors.En las últimas décadas, el interés en torno a las energías renovables ha aumentado considerablemente debido a la creciente demanda energética y a los problemas medioambientales derivados de la combustión de combustibles fósiles. En este escenario, la energía solar de concentración (CSP) es una energía renovable con un alto potencial para cubrir la demanda energética mundial. Sin embargo, es necesario trabajar para mejorar la eficiencia y reducir el coste de las tecnologías basadas en este tipo de energía con el objetivo de hacerla más competitiva. Una de las líneas de investigación actuales es la sustitución del fluido caloportador utilizado en el tubo absorbedor de los colectores cilindroparabólicos por suspensiones nanocoloidales de nanomateriales en un fluido base, típicamente denominados nanofluidos. Los nanofluidos se consideran una nueva generación de fluidos de transferencia de calor, ya que presentan mejoras en sus propiedades termofísicas en comparación con los fluidos de transferencia de calor convencionales. Pero aún quedan algunos obstáculos por superar para la aplicación de los nanofluidos. Por ejemplo, obtener nanofluidos con alta estabilidad es un reto prioritario en este tipo de sistemas. También garantizar que las nanopartículas no obstruyan las tuberías ni provoquen caídas de presión. En esta Tesis Doctoral se ha investigado por primera vez el uso de nanofluidos basados en dicalcogenuros de metales de transición como fluido caloportador en centrales solares. En concreto, se han investigado nanofluidos basados en nanoestructuras unidimensionales, bidimensionales y tridimensionales de MoS2, WS2 y WSe2. El fluido base utilizado en la preparación de estos nanofluidos es la mezcla eutéctica de bifenilo y óxido de difenilo empleada habitualmente como fluido de transferencia de calor en las centrales de concentración de energía solar. Se han explorado principalmente dos métodos de preparación: el método de exfoliación en fase líquida y la síntesis solvotermal del nanomaterial y su posterior dispersión en el aceite térmico mediante ultrasonidos. También se han analizado parámetros experimentales como la concentración de surfactante, el tiempo y la frecuencia de sonicación para la preparación de los nanofluidos. Los nanofluidos han sido sometidos a una extensa caracterización que incluye el estudio de la estabilidad coloidal a lo largo del tiempo, la caracterización de propiedades térmicas como el calor específico isobárico o la conductividad térmica, propiedades reológicas y propiedades ópticas. Los resultados han revelado que los nanofluidos basados en nanoestructuras 1D y 2D de dicalcogenuros de metales de transición son coloidalmente estables en el tiempo y presentan propiedades térmicas mejoradas en comparación con el fluido térmico típico utilizado en las centrales solares. Los nanofluidos más prometedores son los basados en nanoláminas de MoS2 y los basados en nanoláminas de WSe2, con mejoras del coeficiente de transferencia térmica del 36,2% y el 34,1%, respectivamente, con respecto al aceite térmico. Además, el espectacular papel de las nanoláminas de WSe2 en la mejora de la extinción óptica del aceite térmico sugiere el uso de estos nanofluidos en colectores solares de absorción directa. En conclusión, el presente trabajo demuestra la viabilidad del uso de nanofluidos basados en nanoestructuras de dicalcogenuros de metales de transición como fluidos de transferencia de calor en centrales solares de concentración basadas en colectores cilindro-parabólicos

    Sensing Collectives: Aesthetic and Political Practices Intertwined

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    Are aesthetics and politics really two different things? The book takes a new look at how they intertwine, by turning from theory to practice. Case studies trace how sensory experiences are created and how collective interests are shaped. They investigate how aesthetics and politics are entangled, both in building and disrupting collective orders, in governance and innovation. This ranges from populist rallies and artistic activism over alternative lifestyles and consumer culture to corporate PR and governmental policies. Authors are academics and artists. The result is a new mapping of the intermingling and co-constitution of aesthetics and politics in engagements with collective orders
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