89,356 research outputs found

    Modelling uncertainties for measurements of the H → γγ Channel with the ATLAS Detector at the LHC

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    The Higgs boson to diphoton (H → γγ) branching ratio is only 0.227 %, but this final state has yielded some of the most precise measurements of the particle. As measurements of the Higgs boson become increasingly precise, greater import is placed on the factors that constitute the uncertainty. Reducing the effects of these uncertainties requires an understanding of their causes. The research presented in this thesis aims to illuminate how uncertainties on simulation modelling are determined and proffers novel techniques in deriving them. The upgrade of the FastCaloSim tool is described, used for simulating events in the ATLAS calorimeter at a rate far exceeding the nominal detector simulation, Geant4. The integration of a method that allows the toolbox to emulate the accordion geometry of the liquid argon calorimeters is detailed. This tool allows for the production of larger samples while using significantly fewer computing resources. A measurement of the total Higgs boson production cross-section multiplied by the diphoton branching ratio (σ × Bγγ) is presented, where this value was determined to be (σ × Bγγ)obs = 127 ± 7 (stat.) ± 7 (syst.) fb, within agreement with the Standard Model prediction. The signal and background shape modelling is described, and the contribution of the background modelling uncertainty to the total uncertainty ranges from 18–2.4 %, depending on the Higgs boson production mechanism. A method for estimating the number of events in a Monte Carlo background sample required to model the shape is detailed. It was found that the size of the nominal γγ background events sample required a multiplicative increase by a factor of 3.60 to adequately model the background with a confidence level of 68 %, or a factor of 7.20 for a confidence level of 95 %. Based on this estimate, 0.5 billion additional simulated events were produced, substantially reducing the background modelling uncertainty. A technique is detailed for emulating the effects of Monte Carlo event generator differences using multivariate reweighting. The technique is used to estimate the event generator uncertainty on the signal modelling of tHqb events, improving the reliability of estimating the tHqb production cross-section. Then this multivariate reweighting technique is used to estimate the generator modelling uncertainties on background V γγ samples for the first time. The estimated uncertainties were found to be covered by the currently assumed background modelling uncertainty

    The empty space in abstract photography: a psychoanalytical perspective

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    The aim of the research that this thesis is based on is to explore the theoretical problems raised by the concept of photographic abstraction. These consist in the tension between the two aspects of the photographic sign, the indexical and iconic, and are examined in the context of the particular exploration of the empty space in abstract photography which I have pursued through my practice. The investigation draws mainly upon the psychoanalytic theory of transitional phenomena as proposed by Winnicott, as well as other art theories (Deleuze & Guattari, Ehrenzweig, Fer, Fuller, Greenberg, Joselit, Kuspit, Leider, Worringer) of abstraction. It explores the relationship of the abstract photographic image to notions of exteriority and interiority as these relate to the transition from the unconscious to conscious reality. The development of this research suggests the psychoanalytical concept of potential space as a contribution to an aesthetic model of abstraction. This concept is employed as a methodological tool in the development of the practical work and creates a framework for its interpretation. The concept of potential space is based on Winnicott's ideas around "playing with the real" in an intermediate area of experience between the internal and external reality, where creativity originates as a zone of fictive play that facilitates the subject's journey from "what is subjectively conceived of' to "what is objectively perceived. " The outcome of this investigation constitutes the production of a series of photographs describing an empty abstract space, one that is invested with a psychic dimension that produces the effect of ambiguity between its representational and abstract readings. It provides a redefinition of abstraction in a space of tension between the iconic and indexical aspects of the sign and opens up the space of abstraction in photography as one in which the relationship between inner and outer reality can be performed and can become a space of action and intervention

    The influence of carbon morphologies and concentrations on the rheology and electrical performance of screen-printed carbon pastes

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    Screen-printing inks containing various morphologies of carbon are used in the production of a variety of printed electronics applications. Particle morphology influences the rheology of the ink which will affect the deposition and therefore the electrical performance of a printed component. To assess the effect of both carbon morphology and concentration on print topography and conductivity, screen printable carbon inks with differing loading concentrations of graphite, carbon black and graphite nanoplatelets (GNPs) were formulated, printed and characterised, with rheological and novel print visualisation techniques used to elucidate the mechanisms responsible. Carbon morphology had significant effects on the packing of particles. The smaller carbon black particles had more interparticle interactions leading to better conductivities, but also higher ink viscosities and elasticities than the other morphologies. Increases in carbon concentration led to increases in film thickness and roughness for all morphologies. However, beyond a critical point further increases in carbon concentration led to agglomerations of particles, mesh marking and increases in surface roughness, preventing further improvements in the print conductivity. The optimal loading concentrations were identifiable using a custom-made screen-printing apparatus used with high speed imaging for all morphologies. Notable increases in filamentation during ink separation were found to occur with further increases in carbon concentration beyond the optimum. As this point could not be identified using shear rheology alone, this method combined with shear rheology could be used to optimise the carbon concentration of screen-printing inks, preventing the use of excess material which has no benefit on print quality and conductivity

    Quantitative analysis of phase transitions in two-dimensional XY models using persistent homology

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    We use persistent homology and persistence images as an observable of three different variants of the two-dimensional XY model in order to identify and study their phase transitions. We examine models with the classical XY action, a topological lattice action, and an action with an additional nematic term. In particular, we introduce a new way of computing the persistent homology of lattice spin model configurations and, by considering the fluctuations in the output of logistic regression and k-nearest neighbours models trained on persistence images, we develop a methodology to extract estimates of the critical temperature and the critical exponent of the correlation length. We put particular emphasis on finite-size scaling behaviour and producing estimates with quantifiable error. For each model we successfully identify its phase transition(s) and are able to get an accurate determination of the critical temperatures and critical exponents of the correlation length

    Structural-Epistemic Interdisciplinarity and the Nature of Interdisciplinary Challenges

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    Research on interdisciplinarity has been concentrated on the methodological and educational aspects of this complex phenomenon and less on its theoretical nature. Within a theoretical framework specific to the philosophy of science, I propose a structural scheme of how interdisciplinary processes go, focusing on the concepts of availability of the methods, concept linking, and theoretical modeling. In this model, the challenges interdisciplinarity is claimed to pose to its practitioners are of the same nature as the challenges scientists encounter within the evolution of their own disciplines

    Applications and practical considerations of polarisation structuring by a Fresnel cone

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    The polarisation property of light has been known about for hundreds of years. Often its use in technology has been limited to uniform states, however, more recently light with structured polarisation has gained interest. This is largely prompted by availability of spatial light modulators for generation, and increased computation speed to model complex focal fields. My PhD research has extended upon work carried out during a master’s project where we investigated the use of a solid glass cone (so-called Fresnel cone) for generating vector vortex beams. The aim of this thesis is to report on the potential use of a Fresnel cone in microscopy and polarimetry applications, and practical implications discovered. Expanding on the previous work, enhanced fidelity polarisation states are measured and a newly developed Fresnel cone coupling technique is shown, allowing high-efficiency annular vector vortex beam generation. We demonstrate through simulations based on vector diffraction theory that azimuthally polarised light with OAM generated using a Fresnel cone can provide sub-diffraction limited focal spots, below those of more well-known radially polarised light. Practical implications were encountered, prompting investigation into the effects of phase aberrations on resulting focal spots, and experimental measurement of cone surface topology. We find the uniformity of the Fresnel cone shape and apex angle is crucial to the focussing properties. For polarimetry application, full details are provided for a single-shot full-Stokes polarimeter technique and proof-of-principle experiment, where broadband operation is demonstrated. I conclude by summarising the findings of my research and suggest potential future work in this area

    Molecular architecture of the antiophidic protein DM64 and its binding specificity to myotoxin II from Bothrops asper venom

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    DM64 is a toxin-neutralizing serum glycoprotein isolated from Didelphis aurita, an ophiophagous marsupial naturally resistant to snake envenomation. This 64 kDa antitoxin targets myotoxic phospholipases A2, which account for most local tissue damage of viperid snakebites. We investigated the noncovalent complex formed between native DM64 and myotoxin II, a myotoxic phospholipase-like protein from Bothrops asper venom. Analytical ultracentrifugation (AUC) and size exclusion chromatography indicated that DM64 is monomeric in solution and binds equimolar amounts of the toxin. Attempts to crystallize native DM64 for X-ray diffraction were unsuccessful. Obtaining recombinant protein to pursue structural studies was also challenging. Classical molecular modeling techniques were impaired by the lack of templates with more than 25% sequence identity with DM64. An integrative structural biology approach was then applied to generate a three-dimensional model of the inhibitor bound to myotoxin II. I-TASSER individually modeled the five immunoglobulin-like domains of DM64. Distance constraints generated by cross-linking mass spectrometry of the complex guided the docking of DM64 domains to the crystal structure of myotoxin II, using Rosetta. AUC, small-angle X-ray scattering (SAXS), molecular modeling, and molecular dynamics simulations indicated that the DM64-myotoxin II complex is structured, shows flexibility, and has an anisotropic shape. Inter-protein cross-links and limited hydrolysis analyses shed light on the inhibitor’s regions involved with toxin interaction, revealing the critical participation of the first, third, and fifth domains of DM64. Our data showed that the fifth domain of DM64 binds to myotoxin II amino-terminal and beta-wing regions. The third domain of the inhibitor acts in a complementary way to the fifth domain. Their binding to these toxin regions presumably precludes dimerization, thus interfering with toxicity, which is related to the quaternary structure of the toxin. The first domain of DM64 interacts with the functional site of the toxin putatively associated with membrane anchorage. We propose that both mechanisms concur to inhibit myotoxin II toxicity by DM64 binding. The present topological characterization of this toxin-antitoxin complex constitutes an essential step toward the rational design of novel peptide-based antivenom therapies targeting snake venom myotoxins.Fundação Oswaldo Cruz/[INOVA GC VPPCB-007-FIO-18-2-9]/Fiocruz/BrasilFundação de Amparo à Pesquisa do Estado do Rio de Janeiro/[APQ1 E-6/010.001929/2019]/FAPERJ/BrasilConselho Nacional de Desenvolvimento Científico e Tecnológico/[Universal 426290/2018-6]/CNPq/BrasilNational Institutes of Health/[GM120600]/NIH/Estados UnidosNational Science Foundation/[NSF-ACI-1339649]/NSF/Estados UnidosSan Diego Supercomputer Center/[TG-MCB070039N]/SDSC/Estados UnidosTexas Advanced Computing Center/[TG457201]/TACC/Estados UnidosUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Instituto Clodomiro Picado (ICP

    Machine learning and large scale cancer omic data: decoding the biological mechanisms underpinning cancer

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    Many of the mechanisms underpinning cancer risk and tumorigenesis are still not fully understood. However, the next-generation sequencing revolution and the rapid advances in big data analytics allow us to study cells and complex phenotypes at unprecedented depth and breadth. While experimental and clinical data are still fundamental to validate findings and confirm hypotheses, computational biology is key for the analysis of system- and population-level data for detection of hidden patterns and the generation of testable hypotheses. In this work, I tackle two main questions regarding cancer risk and tumorigenesis that require novel computational methods for the analysis of system-level omic data. First, I focused on how frequent, low-penetrance inherited variants modulate cancer risk in the broader population. Genome-Wide Association Studies (GWAS) have shown that Single Nucleotide Polymorphisms (SNP) contribute to cancer risk with multiple subtle effects, but they are still failing to give further insight into their synergistic effects. I developed a novel hierarchical Bayesian regression model, BAGHERA, to estimate heritability at the gene-level from GWAS summary statistics. I then used BAGHERA to analyse data from 38 malignancies in the UK Biobank. I showed that genes with high heritable risk are involved in key processes associated with cancer and are often localised in genes that are somatically mutated drivers. Heritability, like many other omics analysis methods, study the effects of DNA variants on single genes in isolation. However, we know that most biological processes require the interplay of multiple genes and we often lack a broad perspective on them. For the second part of this thesis, I then worked on the integration of Protein-Protein Interaction (PPI) graphs and omics data, which bridges this gap and recapitulates these interactions at a system level. First, I developed a modular and scalable Python package, PyGNA, that enables robust statistical testing of genesets' topological properties. PyGNA complements the literature with a tool that can be routinely introduced in bioinformatics automated pipelines. With PyGNA I processed multiple genesets obtained from genomics and transcriptomics data. However, topological properties alone have proven to be insufficient to fully characterise complex phenotypes. Therefore, I focused on a model that allows to combine topological and functional data to detect multiple communities associated with a phenotype. Detecting cancer-specific submodules is still an open problem, but it has the potential to elucidate mechanisms detectable only by integrating multi-omics data. Building on the recent advances in Graph Neural Networks (GNN), I present a supervised geometric deep learning model that combines GNNs and Stochastic Block Models (SBM). The model is able to learn multiple graph-aware representations, as multiple joint SBMs, of the attributed network, accounting for nodes participating in multiple processes. The simultaneous estimation of structure and function provides an interpretable picture of how genes interact in specific conditions and it allows to detect novel putative pathways associated with cancer
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