841 research outputs found

    The Development of Novel Genomic Technologies that Classify Pathogens of Public Health Significance

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    Recent advances in whole genome sequencing (WGS) have led to the routine sequencing of pathogenic bacteria that burden public health care. Taking advantage of the freely available genomes of tens of thousands of bacterial isolates, this thesis developed novel genomic classification technologies that described the long-and-short-term genomic epidemiology of two pathogens of significant disease burden, Vibrio cholerae and Staphylococcus aureus. V. cholerae is the etiological agent of cholera disease and has circumnavigated the globe in a series of seven pandemics. In Chapter 3, a novel tool named Multilevel Genome Typing (MGT) was developed to classify the V. cholerae species with a focus on the seventh pandemic. The V. cholerae MGT analysed a seventh pandemic dataset (n=4,770), described the seventh pandemic's global population structure, and reconfirmed the origins of the 2016 Yemen outbreak, considered the worst cholera outbreak in modern history. Informed by the successful application of the V. cholerae MGT in Chapter 4, a S. aureus MGT was developed. S. aureus asymptotically colonises approximately 30% of the population and is responsible for the onset of over a dozen diseases. The MGT characterised the global population structure of a clone that emerged in the early 2000s and became the major cause of infections in North America. The S. aureus MGT further investigated the persistent colonisation of patients not associated with hospitals. The MGT described the carriage of multiple isolates colonising the same patient. To gain a high-level view of the population structure consistent with phylogenetic divisions, in Chapter 5, a novel genomic classification tool named the S. aureus Lineage Typer (SaLTy) was developed for the species-level classification of S. aureus. We applied SaLTy to a species dataset (n=50,481) to generate a snapshot of the species population structure and identified six large lineages representing most of the species. To summarise, this thesis developed three novel genomic technologies that, when applied, improved the description of S. aureus and V. cholerae genomic epidemiology. The classifications defined by these technologies can inform the design of prevention and control strategies aiming to lower the disease and economic burdens caused by S. aureus and V. cholerae

    Abstract Book of the II Congress of the Latin American Society for Vector Ecology

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    Recopilación de los resúmenes de las conferencias, simposios, paneles de discusión y "turbo talks" ofrecidos en el II Congreso de la Sociedad Latinoamericana de Ecología de Vectores (LA SOVE), realizado entre el 29 de octubre y el 3 de noviembre de 2022 en la ciudad de La Plata (Buenos Aires, Argentina).Sociedad Latinoamericana de Ecología de Vectores (LA SOVE

    An optimal approach to the design of experiments for the automatic characterisation of biosystems

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    The Design-Build-Test-Learn cycle is the main approach of synthetic biology to re-design and create new biological parts and systems, targeting the solution for complex and challenging paramount problems. The applications of the novel designs range from biosensing and bioremediation of water pollutants (e.g. heavy metals) to drug discovery and delivery (e.g. cancer treatment) or biofuel production (e.g. butanol and ethanol), amongst others. Standardisation, predictability and automation are crucial elements for synthetic biology to efficiently attain these objectives. Mathematical modelling is a powerful tool that allows us to understand, predict, and control these systems, as shown in many other disciplines such as particle physics, chemical engineering, epidemiology and economics. Yet, the inherent difficulties of using mathematical models substantially slowed their adoption by the synthetic biology community. Researchers might develop different competing model alternatives in absence of in-depth knowledge of a system, consequently being left with the burden of with having to find the best one. Models also come with unknown and difficult to measure parameters that need to be inferred from experimental data. Moreover, the varying informative content of different experiments hampers the solution of these model selection and parameter identification problems, adding to the scarcity and noisiness of laborious-to-obtain data. The difficulty to solve these non-linear optimisation problems limited the widespread use of advantageous mathematical models in synthetic biology, broadening the gap between computational and experimental scientists. In this work, I present the solutions to the problems of parameter identification, model selection and experimental design, validating them with in vivo data. First, I use Bayesian inference to estimate model parameters, relaxing the traditional noise assumptions associated with this problem. I also apply information-theoretic approaches to evaluate the amount of information extracted from experiments (entropy gain). Next, I define methodologies to quantify the informative content of tentative experiments planned for model selection (distance between predictions of competing models) and parameter inference (model prediction uncertainty). Then, I use the two methods to define efficient platforms for optimal experimental design and use a synthetic gene circuit (the genetic toggle switch) to substantiate the results, computationally and experimentally. I also expand strategies to optimally design experiments for parameter identification to update parameter information and input designs during the execution of these (on-line optimal experimental design) using microfluidics. Finally, I developed an open-source and easy-to-use Julia package, BOMBs.jl, automating all the above functionalities to facilitate their dissemination and use amongst the synthetic biology community

    Complexity Science in Human Change

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    This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

    Recent Advances in Research on Island Phenomena

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    In natural languages, filler-gap dependencies can straddle across an unbounded distance. Since the 1960s, the term “island” has been used to describe syntactic structures from which extraction is impossible or impeded. While examples from English are ubiquitous, attested counterexamples in the Mainland Scandinavian languages have continuously been dismissed as illusory and alternative accounts for the underlying structure of such cases have been proposed. However, since such extractions are pervasive in spoken Mainland Scandinavian, these languages may not have been given the attention that they deserve in the syntax literature. In addition, recent research suggests that extraction from certain types of island structures in English might not be as unacceptable as previously assumed either. These findings break new empirical ground, question perceived knowledge, and may indeed have substantial ramifications for syntactic theory. This volume provides an overview of state-of-the-art research on island phenomena primarily in English and the Scandinavian languages, focusing on how languages compare to English, with the aim to shed new light on the nature of island constraints from different theoretical perspectives

    Statistical analysis and mathematical modelling of lymphocyte population dynamics

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    Lymphocytes, comprising of B and T cells, are important members of the adaptive immune system of vertebrates that play a crucial role in defending against harmful pathogens. They are equipped with receptors capable of recognising specific antigens. After activation, they proliferate to form an exponentially growing clone army. Eventually, those cells cease to divide and then largely die over a period of weeks, but leave a small number of cells, called memory cells, that can rapidly respond to any repeated infection. To study such non-linear population dynamics, experimental systems have been designed that generate data at the level of populations, families and single cells to elucidate underlying mechanisms that regulate expansion, cessation, and contraction of cell numbers. In this thesis, we report on the development of a novel stochastic model of cellular population dynamics, based on Hawkins et al. (2007a), that accounts for experimentally observed correlation structure within family members. In particular, the inheritance of cell division, cessation, and death times within a stochastic model framework considered, and their impact on cell population dynamics are investigated. Model assumptions are informed by datasets from time-lapse microscopy experiments and statistically tested within the Bayesian framework. Consequences of the dependencies are demonstrated with family trees generated by a Monte-Carlo simulation. To assess the model's ability to extract meaningful inferences from population-level data, we design an optimisation strategy to estimate model parameters and investigate its accuracy and precision for a given dataset from in vitro murine system. With the analysis pipeline, the model is applied to both in vitro murine and human lymphocyte populations to test hypotheses and draw meaningful biological conclusions. For instance, we demonstrate signal integration for T cells from transgenic mice as a linear sum in a time domain, and as a result, the model successfully recapitulates the data. Lastly, we extend the remit of the stochastic modelling framework by exploring mechanisms of B cell differentiation to antibody-secreting cells and their class switching to different isotypes. A simple probabilistic model that captures molecular changes within these cells sheds light on the process of determining the types of antibodies to produce and predicting the magnitude associated with them

    Topological data analysis of organoids

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    Organoids are multi-cellular structures which are cultured in vitro from stem cells to resemble specific organs (e.g., colon, liver) in their three- dimensional composition. The gene expression and the tissue composition of organoids constantly affect each other. Dynamic changes in the shape, cellular composition and transcriptomic profile of these model systems can be used to understand the effect of mutations and treatments in health and disease. In this thesis, I propose new techniques in the field of topological data analysis (TDA) to analyse the gene expression and the morphology of organoids. I use TDA methods, which are inspired by topology, to analyse and quantify the continuous structure of single-cell RNA sequencing data, which is embedded in high dimensional space, and the shape of an organoid. For single-cell RNA sequencing data, I developed the multiscale Laplacian score (MLS) and the UMAP diffusion cover, which both extend and im- prove existing topological analysis methods. I demonstrate the utility of these techniques by applying them to a published benchmark single-cell data set and a data set of mouse colon organoids. The methods validate previously identified genes and detect additional genes with known involvement cancers. To study the morphology of organoids I propose DETECT, a rotationally invariant signature of dynamically changing shapes. I demonstrate the efficacy of this method on a data set of segmented videos of mouse small intestine organoid experiments and show that it outperforms classical shape descriptors. I verify the method on a synthetic organoid data set and illustrate how it generalises to 3D to conclude that DETECT offers rigorous quantification of organoids and opens up computationally scalable methods for distinguishing different growth regimes and assessing treatment effects. Finally, I make a theoretical contribution to the statistical inference of the method underlying DETECT
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