19,082 research outputs found

    Data-to-text generation with neural planning

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    In this thesis, we consider the task of data-to-text generation, which takes non-linguistic structures as input and produces textual output. The inputs can take the form of database tables, spreadsheets, charts, and so on. The main application of data-to-text generation is to present information in a textual format which makes it accessible to a layperson who may otherwise find it problematic to understand numerical figures. The task can also automate routine document generation jobs, thus improving human efficiency. We focus on generating long-form text, i.e., documents with multiple paragraphs. Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or its variants. These models generate fluent (but often imprecise) text and perform quite poorly at selecting appropriate content and ordering it coherently. This thesis focuses on overcoming these issues by integrating content planning with neural models. We hypothesize data-to-text generation will benefit from explicit planning, which manifests itself in (a) micro planning, (b) latent entity planning, and (c) macro planning. Throughout this thesis, we assume the input to our generator are tables (with records) in the sports domain. And the output are summaries describing what happened in the game (e.g., who won/lost, ..., scored, etc.). We first describe our work on integrating fine-grained or micro plans with data-to-text generation. As part of this, we generate a micro plan highlighting which records should be mentioned and in which order, and then generate the document while taking the micro plan into account. We then show how data-to-text generation can benefit from higher level latent entity planning. Here, we make use of entity-specific representations which are dynam ically updated. The text is generated conditioned on entity representations and the records corresponding to the entities by using hierarchical attention at each time step. We then combine planning with the high level organization of entities, events, and their interactions. Such coarse-grained macro plans are learnt from data and given as input to the generator. Finally, we present work on making macro plans latent while incrementally generating a document paragraph by paragraph. We infer latent plans sequentially with a structured variational model while interleaving the steps of planning and generation. Text is generated by conditioning on previous variational decisions and previously generated text. Overall our results show that planning makes data-to-text generation more interpretable, improves the factuality and coherence of the generated documents and re duces redundancy in the output document

    Towards A Graphene Chip System For Blood Clotting Disease Diagnostics

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    Point of care diagnostics (POCD) allows the rapid, accurate measurement of analytes near to a patient. This enables faster clinical decision making and can lead to earlier diagnosis and better patient monitoring and treatment. However, despite many prospective POCD devices being developed for a wide range of diseases this promised technology is yet to be translated to a clinical setting due to the lack of a cost-effective biosensing platform.This thesis focuses on the development of a highly sensitive, low cost and scalable biosensor platform that combines graphene with semiconductor fabrication tech-niques to create graphene field-effect transistors biosensor. The key challenges of designing and fabricating a graphene-based biosensor are addressed. This work fo-cuses on a specific platform for blood clotting disease diagnostics, but the platform has the capability of being applied to any disease with a detectable biomarker.Multiple sensor designs were tested during this work that maximised sensor ef-ficiency and costs for different applications. The multiplex design enabled different graphene channels on the same chip to be functionalised with unique chemistry. The Inverted MOSFET design was created, which allows for back gated measurements to be performed whilst keeping the graphene channel open for functionalisation. The Shared Source and Matrix design maximises the total number of sensing channels per chip, resulting in the most cost-effective fabrication approach for a graphene-based sensor (decreasing cost per channel from £9.72 to £4.11).The challenge of integrating graphene into a semiconductor fabrication process is also addressed through the development of a novel vacuum transfer method-ology that allows photoresist free transfer. The two main fabrication processes; graphene supplied on the wafer “Pre-Transfer” and graphene transferred after met-allisation “Post-Transfer” were compared in terms of graphene channel resistance and graphene end quality (defect density and photoresist). The Post-Transfer pro-cess higher quality (less damage, residue and doping, confirmed by Raman spec-troscopy).Following sensor fabrication, the next stages of creating a sensor platform involve the passivation and packaging of the sensor chip. Different approaches using dielec-tric deposition approaches are compared for passivation. Molecular Vapour Deposi-tion (MVD) deposited Al2O3 was shown to produce graphene channels with lower damage than unprocessed graphene, and also improves graphene doping bringing the Dirac point of the graphene close to 0 V. The packaging integration of microfluidics is investigated comparing traditional soft lithography approaches and the new 3D printed microfluidic approach. Specific microfluidic packaging for blood separation towards a blood sampling point of care sensor is examined to identify the laminar approach for lower blood cell count, as a method of pre-processing the blood sample before sensing.To test the sensitivity of the Post-Transfer MVD passivated graphene sensor de-veloped in this work, real-time IV measurements were performed to identify throm-bin protein binding in real-time on the graphene surface. The sensor was function-alised using a thrombin specific aptamer solution and real-time IV measurements were performed on the functionalised graphene sensor with a range of biologically relevant protein concentrations. The resulting sensitivity of the graphene sensor was in the 1-100 pg/ml concentration range, producing a resistance change of 0.2% per pg/ml. Specificity was confirmed using a non-thrombin specific aptamer as the neg-ative control. These results indicate that the graphene sensor platform developed in this thesis has the potential as a highly sensitive POCD. The processes developed here can be used to develop graphene sensors for multiple biomarkers in the future

    Ero1α-Dependent ERp44 Dissociation From RyR2 Contributes to Cardiac Arrhythmia

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    Background:Oxidative stress in cardiac disease promotes proarrhythmic disturbances in Ca2+ homeostasis, impairing luminal Ca2+ regulation of the sarcoplasmic reticulum (SR) Ca2+ release channel, the RyR2 (ryanodine receptor), and increasing channel activity. However, exact mechanisms underlying redox-mediated increase of RyR2 function in cardiac disease remain elusive. We tested whether the oxidoreductase family of proteins that dynamically regulate the oxidative environment within the SR are involved in this process.Methods:A rat model of hypertrophy induced by thoracic aortic banding (TAB) was used for ex vivo whole heart optical mapping and for Ca2+ and reactive oxygen species imaging in isolated ventricular myocytes (VMs).Results:The SR-targeted reactive oxygen species biosensor ERroGFP showed increased intra-SR oxidation in TAB VMs that was associated with increased expression of oxidoreductase Ero1α. Pharmacological (EN460) or genetic Ero1α inhibition normalized SR redox state, increased Ca2+ transient amplitude and SR Ca2+ content, and reduced proarrhythmic spontaneous Ca2+ waves in TAB VMs under β-adrenergic stimulation (isoproterenol). Ero1α overexpression in Sham VMs had opposite effects. Ero1α inhibition attenuated Ca2+-dependent ventricular tachyarrhythmias in TAB hearts challenged with isoproterenol. Experiments in TAB VMs and human embryonic kidney 293 cells expressing human RyR2 revealed that an Ero1α-mediated increase in SR Ca2+-channel activity involves dissociation of intraluminal protein ERp44 from the RyR2 complex. Site-directed mutagenesis and molecular dynamics simulations demonstrated a novel redox-sensitive association of ERp44 with RyR2 mediated by intraluminal cysteine 4806. ERp44-RyR2 association in TAB VMs was restored by Ero1α inhibition, but not by reducing agent dithiothreitol, as hypo-oxidation precludes formation of covalent bond between RyR2 and ERp44.Conclusions:A novel axis of intraluminal interaction between RyR2, ERp44, and Ero1α has been identified. Ero1α inhibition exhibits promising therapeutic potential by stabilizing RyR2-ERp44 complex, thereby reducing spontaneous Ca2+ release and Ca2+-dependent tachyarrhythmias in hypertrophic hearts, without causing hypo-oxidative stress in the SR

    Annals [...].

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    Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo Simão Diniz Dalmolin

    Quantification of epigenetic bases and oxidative lesions in lung tissues

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    Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning

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    Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led to neural approaches to natural language generation (NLG). These methods combine generative language learning techniques with neural-networks based frameworks. With a wide range of applications in natural language processing, neural NLG (NNLG) is a new and fast growing field of research. In this state-of-the-art report, we investigate the recent developments and applications of NNLG in its full extent from a multidimensional view, covering critical perspectives such as multimodality, multilinguality, controllability and learning strategies. We summarize the fundamental building blocks of NNLG approaches from these aspects and provide detailed reviews of commonly used preprocessing steps and basic neural architectures. This report also focuses on the seminal applications of these NNLG models such as machine translation, description generation, automatic speech recognition, abstractive summarization, text simplification, question answering and generation, and dialogue generation. Finally, we conclude with a thorough discussion of the described frameworks by pointing out some open research directions.This work has been partially supported by the European Commission ICT COST Action “Multi-task, Multilingual, Multi-modal Language Generation” (CA18231). AE was supported by BAGEP 2021 Award of the Science Academy. EE was supported in part by TUBA GEBIP 2018 Award. BP is in in part funded by Independent Research Fund Denmark (DFF) grant 9063-00077B. IC has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 838188. EL is partly funded by Generalitat Valenciana and the Spanish Government throught projects PROMETEU/2018/089 and RTI2018-094649-B-I00, respectively. SMI is partly funded by UNIRI project uniri-drustv-18-20. GB is partly supported by the Ministry of Innovation and the National Research, Development and Innovation Office within the framework of the Hungarian Artificial Intelligence National Laboratory Programme. COT is partially funded by the Romanian Ministry of European Investments and Projects through the Competitiveness Operational Program (POC) project “HOLOTRAIN” (grant no. 29/221 ap2/07.04.2020, SMIS code: 129077) and by the German Academic Exchange Service (DAAD) through the project “AWAKEN: content-Aware and netWork-Aware faKE News mitigation” (grant no. 91809005). ESA is partially funded by the German Academic Exchange Service (DAAD) through the project “Deep-Learning Anomaly Detection for Human and Automated Users Behavior” (grant no. 91809358)

    Labour Markets in Professional Sports

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    Measuring performance and quantifying outcomes can prove a difficult task in empirical economics research. Because of this, economists have often turned to the setting of professional sports to overcome these data limitations. Sports and sports data presents a unique opportunity to study the behaviour of workers, firms and supervisors, since performance can be accurately measured and compared across agents. This thesis offers three chapters in the broad fields of labour and personnel economics, using data from professional sports to illustrate. In Chapter One, we consider the role of Head Coaches at football clubs, and whether teams can benefit from Head Coach turnover. This extends on previous work on this topic along several lines. Most notably, Head Coach turnover can either be voluntary or involuntary. In a principal-agent framework, these are theoretically two quite different events, with each producing different predictions about changes to team performance. We also use data from multiple leagues and can distinguish between a short run “bump” effect, and a longer run learning effect. Results show that teams can benefit from Head Coach turnover, particularly following a dismissal, though the result is sensitive to how we define our follow up period. In Chapter 2, we examine the ability of baseball pitchers to switch between different tasks, by considering how their pitching performance is affected by the additional demands of having to bat and run bases. Despite the prevalence of task switching in modern day work, there is a surprising lack of empirical evidence on its effects on productivity. Baseball is an ideal setting to consider this question empirically, making use of the two-league structure of Major League Baseball. In one league, pitchers are faced with a forced task switching rule of having to both pitch and bat, while in the other, pitchers can focus on their primary job; pitching. The structure of the game of baseball, consisting of innings and a batting order, also means we can cleanly identify cases of workers switching back and forth between tasks. Our results indicate that pitchers can actually benefit from batting, but at all costs should avoid excessive fatigue after running bases. Finally, in Chapter 3, we return to Coaches, this time in the National Football League. We examine the determinants of coaching changes at the levels of Head Coach and Coordinator. In particular, we pay close attention to the role of the league’s affirmative action policy, the Rooney Rule, on the likelihood of minority coaches being appointed to a Head Coaching role. Results suggest that the rule has been somewhat successful, since teams now appear to be hiring equally skilled black and white coaches, despite evidence that there had always been a supply of equally skilled black coaches

    Investigating PAX6 and SOX2 dynamic interactions at the single molecule level in live cells

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    The abundance of transcription factor (TF) molecules in the nuclei of eukaryotic cells are in the range of thousands. However, the functional binding sites of most TFs lie in the range of hundreds. This suggests that there is a surplus of the number of molecules for many TFs, relative to their binding sites at any given time. Nevertheless, precise TF levels are instrumental for normal development and maintenance, with haploinsufficiency (namely lowering the dosage of a TF by half) being a hallmark of many TF-related human developmental disorders. Qualitative methods assessing TF binding such as chromatin immunoprecipitation, provide static information, from fixed cell populations and so fail to provide insight into TF dynamic behaviour. Live-cell imaging methodologies such as Fluorescence Correlation Spectroscopy (FCS) offer the ability to measure kinetics of binding to chromatin, protein-protein interactions, absolute concentrations of molecules and the underlying cell-to-cell variability. SOX2 and PAX6 TFs exhibit haploinsufficiency in humans. Heterozygous point mutations, deletions or insertions in these genes can lead to a plethora of abnormal ocular developmental disorders (e.g. coloboma, aniridia, microphthalmia, anopthalmia). SOX2 encodes a high-mobility group (HMG) domain-containing TF, essential for maintaining self-renewal of embryonic stem cells and is expressed in proliferating central nervous system (CNS) progenitors. PAX6 contains two DNA binding domains; a PAIRED domain (PD) and a homeodomain (HD). Both DNA binding domains present in PAX6 (PD and HD) can function either jointly, or separately, to regulate a plethora of genes implicated in the development and maintenance of the CNS, the eye and the pancreas. Despite existing genetic and phenotypic evidence, it remains unclear how PAX6 and SOX2 influence each other at the molecular level and how sensitive their stoichiometry is during ocular development. In this thesis I investigated the dynamic interplay between PAX6/SOX2 and chromatin in live cells, at the molecular level. I compared wild-type protein function with pathogenic missense variants using advanced fluorescence microscopy techniques and assessed how these mutations quantitatively and qualitatively affected molecular behaviour. My results showed that both SOX2 and PAX6 pathogenic missense mutants display differential subnuclear localisation, as well as altered protein-protein and protein-chromatin interactions, linking molecular diffusion to pathogenic phenotype in humans. More importantly, I identified a novel role of SOX2 in stabilising PAX6- chromatin complexes in live cells, providing further insight into the complex and dynamic relation of PAX6 and SOX2 in ocular tissue specification, maintenance and development

    Graphical scaffolding for the learning of data wrangling APIs

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    In order for students across the sciences to avail themselves of modern data streams, they must first know how to wrangle data: how to reshape ill-organised, tabular data into another format, and how to do this programmatically, in languages such as Python and R. Despite the cross-departmental demand and the ubiquity of data wrangling in analytical workflows, the research on how to optimise the instruction of it has been minimal. Although data wrangling as a programming domain presents distinctive challenges - characterised by on-the-fly syntax lookup and code example integration - it also presents opportunities. One such opportunity is how tabular data structures are easily visualised. To leverage the inherent visualisability of data wrangling, this dissertation evaluates three types of graphics that could be employed as scaffolding for novices: subgoal graphics, thumbnail graphics, and parameter graphics. Using a specially built e-learning platform, this dissertation documents a multi-institutional, randomised, and controlled experiment that investigates the pedagogical effects of these. Our results indicate that the graphics are well-received, that subgoal graphics boost the completion rate, and that thumbnail graphics improve navigability within a command menu. We also obtained several non-significant results, and indications that parameter graphics are counter-productive. We will discuss these findings in the context of general scaffolding dilemmas, and how they fit into a wider research programme on data wrangling instruction
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