1,064 research outputs found

    Graduate Catalog of Studies, 2023-2024

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    Discovery of genetic factors for reading ability and dyslexia

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    The ability to read is critical to access wider learning and achieve qualifications, for accessing employment, and for adult life skills. Approximately one in ten individuals are affected by dyslexia, a learning difficulty which primarily impacts word reading and spelling. Specifically, phonological processing (the ability to decode phonemes) is impaired in dyslexia. Whilst some believe dyslexia represents the extreme end of a continuum of reading ability, others have suggested it is a distinct trait. Variation in reading ability is a highly heritable (possibly 70%) complex trait caused by many genetic variants with a small effect size. However, the genetic architecture of reading ability and dyslexia is largely unknown due to a lack of quantitative genetic studies with sufficient statistical power to detect such small effect sizes. Previously, most genetic studies of reading ability have been conducted using samples of children with dyslexia, which tend to be modest in size. Whilst large samples of genotyped unselected adults have been collected (for example UK Biobank), phenotypic data on reading or language skills is rarely prioritised. The overall aim of this thesis is to discover genetic variants associated with dyslexia and variation in reading skill in order to better understand the aetiology of reading difficulties, which in turn, may inform prediction, identification and intervention strategies in the future. Firstly, I will conduct a genome-wide association (GWA) study of over 50,000 adults with a self-reported dyslexia diagnosis and over 1 million controls to identify associated single nucleotide polymorphisms (SNPs). I will also explore ways to improve power for discovering genetic factors associated with reading ability. To do this, I will first investigate whether unselected adult samples are valid as a means to identify genetic factors associated with reading skill through a candidate gene approach. Secondly, I will investigate whether proxy reading phenotypes are also a means to gain power through large cohorts that have no quantitative measure of reading ability. Such samples may be informative for future GWA meta-analysis of quantitative reading ability. In Chapter 1, I will first introduce reading ability and dyslexia. I will discuss how reading ability is a quantitative trait and how it can be measured before discussing how dyslexia is identified. Then, I will consider how dyslexia may relate to reading ability: whether it represents the extreme end of a continuum of reading or whether it is a distinct trait. I will then introduce the known causes of variation in reading ability and dyslexia, which includes both environmental and genetic factors. Next, I will present the history of genetic studies of reading ability and dyslexia and their limitations. Finally, I will discuss the current state of genetic research into reading ability and introduce the aims of my thesis in detail. Chapter 2 is a publication in Nature Genetics entitled ‘Discovery of 42 genome-wide significant loci associated with dyslexia’ which includes GWA analysis of over 1 million 23andMe, Inc participants reporting on dyslexia diagnosis. I identify 42 independent genome-wide significant loci, 15 of which are in genes previously linked to cognitive ability and/or educational attainment, and 27 of which are novel and may be more specific to dyslexia. Extensive downstream biological analysis is performed alongside genetic correlations with other traits and dyslexia polygenic score prediction of quantitative reading scores. Chapter 3 is a publication in Twin Research and Human Genetics on ‘The association of dyslexia and developmental speech and language disorder candidate genes with reading and language abilities in adults’ which analyses an adult population cohort with quantitative measures of reading and language ability to replicate previous associations of candidate genes and biological pathways with dyslexia. I demonstrate that unselected adult populations are a valid means by which to identify genes which have previously been associated with dyslexia and/or speech and language disorder. Chapter 4 is a research chapter in which I construct a proxy reading phenotype from measures of reading frequency in an unselected adult sample for whom a quantitative measure of reading ability is not available. I find that a dyslexia polygenic score constructed from the dyslexia GWA analysis in Chapter 3 cannot explain variation in the proxy phenotype suggesting that book reading is not a sufficient substitute for reading ability. Finally, in Chapter 5, I integrate and discuss my research findings. I highlight the discovery of 42 variants associated with dyslexia through GWAS, in addition to the discovery of new genes and biological pathways which may form part of the biological basis of dyslexia. Following this, I consider what GWAS tells us about candidate gene findings. I discuss traits which are genetically correlated with dyslexia, including quantitative reading skills and ADHD. I consider the relationship between dyslexia and reading ability, and how genetic studies can help us to understand this better. I also consider the relationship between dyslexia and other developmental disorders, and how genetic studies can help us to understand this better. Lastly, I discuss methods to boost power for GWAS of reading ability

    Graduate Catalog of Studies, 2023-2024

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    Integrative pathway enrichment analysis of multivariate omics data

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    Multi-omics datasets represent distinct aspects of the central dogma of molecular biology. Such high-dimensional molecular profiles pose challenges to data interpretation and hypothesis generation. ActivePathways is an integrative method that discovers significantly enriched pathways across multiple datasets using statistical data fusion, rationalizes contributing evidence and highlights associated genes. As part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we integrated genes with coding and non-coding mutations and revealed frequently mutated pathways and additional cancer genes with infrequent mutations. We also analyzed prognostic molecular pathways by integrating genomic and transcriptomic features of 1780 breast cancers and highlighted associations with immune response and anti-apoptotic signaling. Integration of ChIP-seq and RNA-seq data for master regulators of the Hippo pathway across normal human tissues identified processes of tissue regeneration and stem cell regulation. ActivePathways is a versatile method that improves systems-level understanding of cellular organization in health and disease through integration of multiple molecular datasets and pathway annotations

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

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    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (ForlĂŹ Campus) in collaboration with the Romagna Chamber of Commerce (ForlĂŹ-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices

    The little skate genome and the evolutionary emergence of wing-like fins

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    Skates are cartilaginous fish whose body plan features enlarged wing-like pectoral fins, enabling them to thrive in benthic environments1,2. However, the molecular underpinnings of this unique trait remain unclear. Here we investigate the origin of this phenotypic innovation by developing the little skate Leucoraja erinacea as a genomically enabled model. Analysis of a high-quality chromosome-scale genome sequence for the little skate shows that it preserves many ancestral jawed vertebrate features compared with other sequenced genomes, including numerous ancient microchromosomes. Combining genome comparisons with extensive regulatory datasets in developing fins—including gene expression, chromatin occupancy and three-dimensional conformation—we find skate-specific genomic rearrangements that alter the three-dimensional regulatory landscape of genes that are involved in the planar cell polarity pathway. Functional inhibition of planar cell polarity signalling resulted in a reduction in anterior fin size, confirming that this pathway is a major contributor to batoid fin morphology. We also identified a fin-specific enhancer that interacts with several hoxa genes, consistent with the redeployment of hox gene expression in anterior pectoral fins, and confirmed its potential to activate transcription in the anterior fin using zebrafish reporter assays. Our findings underscore the central role of genome reorganization and regulatory variation in the evolution of phenotypes, shedding light on the molecular origin of an enigmatic trait

    Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics

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    Sampling from known probability distributions is a ubiquitous task in computational science, underlying calculations in domains from linguistics to biology and physics. Generative machine-learning (ML) models have emerged as a promising tool in this space, building on the success of this approach in applications such as image, text, and audio generation. Often, however, generative tasks in scientific domains have unique structures and features -- such as complex symmetries and the requirement of exactness guarantees -- that present both challenges and opportunities for ML. This Perspective outlines the advances in ML-based sampling motivated by lattice quantum field theory, in particular for the theory of quantum chromodynamics. Enabling calculations of the structure and interactions of matter from our most fundamental understanding of particle physics, lattice quantum chromodynamics is one of the main consumers of open-science supercomputing worldwide. The design of ML algorithms for this application faces profound challenges, including the necessity of scaling custom ML architectures to the largest supercomputers, but also promises immense benefits, and is spurring a wave of development in ML-based sampling more broadly. In lattice field theory, if this approach can realize its early promise it will be a transformative step towards first-principles physics calculations in particle, nuclear and condensed matter physics that are intractable with traditional approaches.Comment: 11 pages, 5 figure

    High-throughput Single-Entity Analysis Methods: From Single-Cell Segmentation to Single-Molecule Force Measurements

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    This work is focused on the development of new microscopy-based analysis methods with single-entity resolution and high-throughput capabilities from the cellular to the molecular level to study biomembrane-associated interactions. Currently, there is a variety of methods available for obtaining quantitative information on cellular and molecular responses to external stimuli, but many of them lack either high sensitivity or high throughput. Yet, the combination of both aspects is critical for studying the weak but often complex and multivalent interactions at the interface of biological mem-branes. These interactions include binding of pathogens such as some viruses (e.g., influenza A virus, herpes simplex virus, and SARS-CoV-2), transmembrane signaling such as ligand-based oli-gomerization processes, and transduction of mechanical forces acting on cells. The goal of this work was to overcome the shortcomings of current methods by developing and es-tablishing new methods with unprecedented levels of automation, sensitivity, and parallelization. All methods are based on the combination of optical (video) microscopy followed by highly refined data analysis to study single cellular and molecular events, allowing the detection of rare events and the identification and quantification of cellular and molecular populations that would remain hidden in ensemble-averaging approaches. This work comprises four different projects. At the cellular level, two methods have been developed for single-cell segmentation and cell-by-cell readout of fluorescence reporter systems, mainly to study binding and inhibition of binding of viruses to host cells. The method developed in the first pro-ject features a high degree of automation and automatic estimation of sufficient analysis parameters (background threshold, segmentation sensitivity, and fluorescence cutoff) to reduce the manual ef-fort required for the analysis of cell-based infection assays. This method has been used for inhibition potency screening based on the IC50 value of various virus binding inhibitors. With the method used in the second project, the sensitivity of the first method is extended by providing an estimate of the number of fluorescent nanoparticles bound to the cells. The image resolution was chosen to allow many cells to be imaged in parallel. This allowed for the quantification of cell-to-cell heterogeneity of particle binding, at the expense of resolution of the individual fluorescent nanoparticles. To account for this, a new approach was developed and validated by simulations to estimate the number of fluo-rescent nanoparticles below the diffraction limit with an accuracy of about 80 to 100 %. In the third project, an approach for the analysis and refinement of two-dimensional single-particle tracking ex-periments was presented. It focused on the quality assessment of the derived tracks by providing a guide for the selection of an appropriate maximal linking distance. This tracking approach was used in the fourth project to quantify small molecule responses to hydrodynamic shear forces with sub-nm resolution. Here, the combination of TIRF microscopy, microfluidics, and single particle tracking enabled the development of a new single molecule force spectroscopy method with high resolution and parallelization capabilities. This method was validated by quantifying the mechanical response of well-defined PEG linkers and subsequently used to study the energy barriers of dissociation of mul-tivalent biotin-NeutrAvidin complexes under low (~ 1.5 to 12 pN) static forces. In summary, with this work, the repertoire of appropriate methods for high-throughput investigation of the properties and interactions of cells, nanoparticles, and molecules at single resolution is expand-ed. In the future, the methods developed here will be used to screen for additional virus binding inhib-itors, to study the oligomerization of membrane receptors on cells and model membranes, and to quantify the mechanical response of force-bearing proteins and ligand-receptor complexes under low force conditions

    New computational methods for structural modeling protein-protein and protein-nucleic acid interactions

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    Programa de Doctorat en Biomedicina[eng] The study of the 3D structural details of protein-protein and protein-DNA interactions is essential to understand biomolecular functions at the molecular level. Given the difficulty of the structural determination of these complexes by experimental techniques, computational tools are becoming a powerful to increase the actual structural coverage of protein-protein and protein-DNA interactions. pyDock is one of these tools, which uses its scoring function to determine the quality of models generated by other tools. pyDock is usually combined with the model sampling methods FTDOCK or ZDOCK. This combination has shown a consistently good prediction performance in community-wide assessment experiments like CAPRI or CASP and has provided biological insights and insightful interpretation of experiments by modeling many biomolecular interactions of biomedical and biotechnological interest. This software combination has demonstrated good predictive performance in the blinded evaluation experiments CAPRI and CASP. It has provided biological insights by modeling many biomolecular interactions of biomedical and biotechnological interest. Here, we describe a pyDock software update, which includes its adaptation to the newest python code, the capability of including cofactor and other small molecules, and an internal parallelization to use the computational resources more efficiently. A strategy was designed to integrate the template-based docking and ab initio docking approaches by creating a new scoring function based on the pyDock scoring energy basis function and the TM-score measure of structural similarity of protein structures. This strategy was partially used for our participation in the 7th CAPRI, the 3rd CASP-CAPRI and the 4th CASP-CAPRI joint experiments. These experiments were challenging, as we needed to model protein-protein complexes, multimeric oligomerization proteins, protein-peptide, and protein-oligosaccharide interactions. Many proposed targets required the efficient integration of rigid-body docking, template-based modeling, flexible optimization, multi- parametric scoring, and experimental restraints. This was especially relevant for the multi- molecular assemblies proposed in the 3er and 4th CASP-CAPRI joint experiments. In addition, a case study, in which electron transfer protein complexes were modelled to test the software new capabilities. Good results were achieved as the structural models obtained help explaining the differences in photosynthetic efficiency between red and green algae
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