9,556 research outputs found

    Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse

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    This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses. This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups. In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena

    Integrative multi-omics analysis for the effect of genetic alterations in cancer xenograft and organoid models

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    Department of Biomedical EngineeringDNA damage is a well-recognized factor in the development and progression of cancer. Numerous studies on genetic changes associated with cancer or the DNA repair pathway have been conducted, however, there is still a need for additional research on their function. The establishment of patient-derived xenografts or organoids for the purpose of testing functional genomic approaches is the subject of ongoing research. According to model-specific characteristics, it is not fully understood how these attempts to simulate patient cancer differ from original cancer. To comprehend the distinction between genuine patient cancer and these patient-derived disease models in more depth, multi-omics analysis is required to comprehend the overall genotypes, phenotypes, and environmental variables. Depending on the characteristics of each disease model, distinct omics analysis approaches and factors must be considered. In addition, care must be taken to avoid technical errors when integrating omics data generated by different sequencing equipment. There is currently no golden rule for data integration, but several approaches are being developed. It is crucial to determine the function of genes linked with the DNA repair pathway because these genes contribute to the induction or prevention of cancer. In chapter 1, I identified the interaction between MRE11 and TRIP13 through proximity labeling combined with the SILAC method which is quantitative proteomics using metabolic labeling. TRIP13 depletion doesn???t affect the nuclease activity and conformation of the MRN complex but directly inhibits the interaction of MDC1 with MRN complex and MDC1 recruitment on the DNA damage site. TRIP13 degradation with mirin treatment shows additive effects on ATM signaling activation. In conclusion, TRIP13 regulates immediate-early DNA damage sensing through MRE11 and ATM signaling independently of mirin. When assessing the functional genomic approach using patient-derived disease models, it is essential to determine which aspects of the models' correlation to actual cancer should be properly considered. In chapter 2, I found there are a few overlapped deleterious somatic mutations of the PDX model and their original tumor. I suspected novel mutagen exposure during PDX establishment or sample contamination. However, germline mutations of PDX models are well conserved from original tumors, and their mutational signatures of PDX also mimic that of their tumor. Though the number of overlapped mutations between the PDX model and their tumor was few, brain tumor-specific mutations are found in PDX samples. Especially, histone methylation- and cilia-related gene mutations are enriched in PDX samples. While it suggested these mutated genes are needed for maintaining the stemness of brain tumor PDX model or PDX model would be more appropriate for the samples with high heterogeneity, I have presented precautions and considerations in PDX model genome analysis. Multi-omics analysis that takes into consideration genetic, expressive, and clinical aspects can provide important information for the study of diseases with complicated etiologies, such as cancer, and can contribute to the development of diagnosis and treatment. To utilize colorectal cancer organoids for Companion Diagnostics (CDx), in chapter 3, I characterized patient-derived colorectal cancer (CRC) organoids through well-known genomic markers such as Tumor mutation burden (TMB), Microsatellite instability (MSI) and propose a novel grouping method using sharing same mutation site. The classification of CRC patients was more detailed combined with consensus molecular subtype (CMS) classifications. Additionally, I extract the expression features of the patients who experience recurrence or metastasis after first-line chemotherapy treatment with reference to clinical data. Drug response of CRC organoids by patient group and knockdown of the extracted features in the selected organoids would be validated in further study. In summary, with this dissertation, I conducted functional research on the DNA repair pathway of cancer-related genes, as well as the genetic analysis between patient-derived xenograft and original tumors, and introduced a novel perspective on the diagnosis and treatment of colorectal cancer patients using patient-derived organoids through multi-omics analysis.ope

    Um modelo para suporte automatizado ao reconhecimento, extração, personalização e reconstrução de gráficos estáticos

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    Data charts are widely used in our daily lives, being present in regular media, such as newspapers, magazines, web pages, books, and many others. A well constructed data chart leads to an intuitive understanding of its underlying data and in the same way, when data charts have wrong design choices, a redesign of these representations might be needed. However, in most cases, these charts are shown as a static image, which means that the original data are not usually available. Therefore, automatic methods could be applied to extract the underlying data from the chart images to allow these changes. The task of recognizing charts and extracting data from them is complex, largely due to the variety of chart types and their visual characteristics. Computer Vision techniques for image classification and object detection are widely used for the problem of recognizing charts, but only in images without any disturbance. Other features in real-world images that can make this task difficult are not present in most literature works, like photo distortions, noise, alignment, etc. Two computer vision techniques that can assist this task and have been little explored in this context are perspective detection and correction. These methods transform a distorted and noisy chart in a clear chart, with its type ready for data extraction or other uses. The task of reconstructing data is straightforward, as long the data is available the visualization can be reconstructed, but the scenario of reconstructing it on the same context is complex. Using a Visualization Grammar for this scenario is a key component, as these grammars usually have extensions for interaction, chart layers, and multiple views without requiring extra development effort. This work presents a model for automated support for custom recognition, and reconstruction of charts in images. The model automatically performs the process steps, such as reverse engineering, turning a static chart back into its data table for later reconstruction, while allowing the user to make modifications in case of uncertainties. This work also features a model-based architecture along with prototypes for various use cases. Validation is performed step by step, with methods inspired by the literature. This work features three use cases providing proof of concept and validation of the model. The first use case features usage of chart recognition methods focused on documents in the real-world, the second use case focus on vocalization of charts, using a visualization grammar to reconstruct a chart in audio format, and the third use case presents an Augmented Reality application that recognizes and reconstructs charts in the same context (a piece of paper) overlaying the new chart and interaction widgets. The results showed that with slight changes, chart recognition and reconstruction methods are now ready for real-world charts, when taking time, accuracy and precision into consideration.Os gráficos de dados são amplamente utilizados na nossa vida diária, estando presentes nos meios de comunicação regulares, tais como jornais, revistas, páginas web, livros, e muitos outros. Um gráfico bem construído leva a uma compreensão intuitiva dos seus dados inerentes e da mesma forma, quando os gráficos de dados têm escolhas de conceção erradas, poderá ser necessário um redesenho destas representações. Contudo, na maioria dos casos, estes gráficos são mostrados como uma imagem estática, o que significa que os dados originais não estão normalmente disponíveis. Portanto, poderiam ser aplicados métodos automáticos para extrair os dados inerentes das imagens dos gráficos, a fim de permitir estas alterações. A tarefa de reconhecer os gráficos e extrair dados dos mesmos é complexa, em grande parte devido à variedade de tipos de gráficos e às suas características visuais. As técnicas de Visão Computacional para classificação de imagens e deteção de objetos são amplamente utilizadas para o problema de reconhecimento de gráficos, mas apenas em imagens sem qualquer ruído. Outras características das imagens do mundo real que podem dificultar esta tarefa não estão presentes na maioria das obras literárias, como distorções fotográficas, ruído, alinhamento, etc. Duas técnicas de visão computacional que podem ajudar nesta tarefa e que têm sido pouco exploradas neste contexto são a deteção e correção da perspetiva. Estes métodos transformam um gráfico distorcido e ruidoso em um gráfico limpo, com o seu tipo pronto para extração de dados ou outras utilizações. A tarefa de reconstrução de dados é simples, desde que os dados estejam disponíveis a visualização pode ser reconstruída, mas o cenário de reconstrução no mesmo contexto é complexo. A utilização de uma Gramática de Visualização para este cenário é um componente chave, uma vez que estas gramáticas têm normalmente extensões para interação, camadas de gráficos, e visões múltiplas sem exigir um esforço extra de desenvolvimento. Este trabalho apresenta um modelo de suporte automatizado para o reconhecimento personalizado, e reconstrução de gráficos em imagens estáticas. O modelo executa automaticamente as etapas do processo, tais como engenharia inversa, transformando um gráfico estático novamente na sua tabela de dados para posterior reconstrução, ao mesmo tempo que permite ao utilizador fazer modificações em caso de incertezas. Este trabalho também apresenta uma arquitetura baseada em modelos, juntamente com protótipos para vários casos de utilização. A validação é efetuada passo a passo, com métodos inspirados na literatura. Este trabalho apresenta três casos de uso, fornecendo prova de conceito e validação do modelo. O primeiro caso de uso apresenta a utilização de métodos de reconhecimento de gráficos focando em documentos no mundo real, o segundo caso de uso centra-se na vocalização de gráficos, utilizando uma gramática de visualização para reconstruir um gráfico em formato áudio, e o terceiro caso de uso apresenta uma aplicação de Realidade Aumentada que reconhece e reconstrói gráficos no mesmo contexto (um pedaço de papel) sobrepondo os novos gráficos e widgets de interação. Os resultados mostraram que com pequenas alterações, os métodos de reconhecimento e reconstrução dos gráficos estão agora prontos para os gráficos do mundo real, tendo em consideração o tempo, a acurácia e a precisão.Programa Doutoral em Engenharia Informátic

    Investigation of a Histidine-Based Probe for the Exploration of Proteomes

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    Leishmaniasis is a neglected tropical disease which affects 0.7-1 million people per year. Current chemotherapies for leishmaniasis are toxic with long treatment times and reports of increasing resistance, which stresses the importance of this research area. Inositol phosphorylceramide synthase is a membrane bound enzyme that has no direct human homologue, which converts ceramide to inositol phosphorylceramide through the action of a highly conserved HHD catalytic triad. An ideal method to study this enzyme further would be through activity-based protein profiling, however, there are currently no activity-based probes reported that reacts with this type of active site. Therefore, an activity-based probe was designed based on the structure of diethyl pyrocarbonate, a compound known to bind covalently to active site histidine residues. The synthesised activity-based probe was shown to inhibit Leishmania major inositol phosphorylceramide synthase in a simple assay. In addition, the probe was shown to selectively bind to the active site histidine residue in two pure enzyme models; one of which has the same catalytic triad as inositol phosphorylceramide synthase, and the other was an acid base active site histidine residue. Further, this activity-based probe was able to isolate an overexpressed enzyme in the lysate of Escherichia coli as well as bind to intrinsic proteins. Following the function validation of the activity-based probe, preliminary work was started in Leishmania to isolate proteins identify expressed enzymes

    Investigating the mechanism of human beta defensin-2-mediated protection of skin barrier in vitro

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    The human skin barrier is a biological imperative. Chronic inflammatory skin diseases, such as Atopic Dermatitis (AD), are characterised by a reduction in skin barrier function and an increased number of secondary infections. Staphyloccocus aureus (S. aureus) has an increased presence on AD lesional skin and contributes significantly to AD pathology. It was previously demonstrated that the damage induced by a virulence factor of S. aureus, V8 protease, which causes further breakdown in skin barrier function, can be reduced by induction of human β- defensin (HBD)2 (by IL-1β) or exogenous HBD2 application. Induction of this defensin is impaired in AD skin. This thesis examines the mechanism of HBD2-mediated barrier protection in vitro; demonstrating that in this system, HBD2 was not providing protection through direct protease inhibition, nor was it altering keratinocyte proliferation or migration, or exhibiting specific localisation within the monolayer. Proteomics data demonstrated that HBD2 did not induce expression of known antiproteases but suggested that HBD2 stimulation may function by modulating expression of extracellular matrix proteins, specifically collagen- IVα2 and Laminin-β-1. Alternative pathways of protection initiated by IL-1β and TNFα stimulation were also investigated, as well as their influence over generalised wound healing. Finally, novel 3D human skin epidermal models were used to better recapitulate the structure of human epidermis and examine alterations to skin barrier function in a more physiological system. These data validate the barrier-protective properties of HBD2 and extended our knowledge of the consequences of exposure to this peptide in this context

    Statistical Learning for Gene Expression Biomarker Detection in Neurodegenerative Diseases

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    In this work, statistical learning approaches are used to detect biomarkers for neurodegenerative diseases (NDs). NDs are becoming increasingly prevalent as populations age, making understanding of disease and identification of biomarkers progressively important for facilitating early diagnosis and the screening of individuals for clinical trials. Advancements in gene expression profiling has enabled the exploration of disease biomarkers at an unprecedented scale. The work presented here demonstrates the value of gene expression data in understanding the underlying processes and detection of biomarkers of NDs. The value of novel approaches to previously collected -omics data is shown and it is demonstrated that new therapeutic targets can be identified. Additionally, the importance of meta-analysis to improve power of multiple small studies is demonstrated. The value of blood transcriptomics data is shown in applications to researching NDs to understand underlying processes using network analysis and a novel hub detection method. Finally, after demonstrating the value of blood gene expression data for investigating NDs, a combination of feature selection and classification algorithms were used to identify novel accurate biomarker signatures for the diagnosis and prognosis of Parkinson’s disease (PD) and Alzheimer’s disease (AD). Additionally, the use of feature pools based on previous knowledge of disease and the viability of neural networks in dimensionality reduction and biomarker detection is demonstrated and discussed. In summary, gene expression data is shown to be valuable for the investigation of ND and novel gene biomarker signatures for the diagnosis and prognosis of PD and AD

    AIUCD 2022 - Proceedings

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    L’undicesima edizione del Convegno Nazionale dell’AIUCD-Associazione di Informatica Umanistica ha per titolo Culture digitali. Intersezioni: filosofia, arti, media. Nel titolo è presente, in maniera esplicita, la richiesta di una riflessione, metodologica e teorica, sull’interrelazione tra tecnologie digitali, scienze dell’informazione, discipline filosofiche, mondo delle arti e cultural studies

    Novel strategies for the modulation and investigation of memories in the hippocampus

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    Disruptions of the memory systems in the brain are linked to the manifestation of many neuropsychiatric diseases such as Alzheimer’s disease, depression, and post-traumatic stress disorder. The limited efficacy of current treatments necessities the development of more effective therapies. Neuromodulation has proven effective in a variety of neurological diseases and could be an attractive solution for memory disorders. However, the application of neuromodulation requires a more detailed understanding of the network dynamics associated with memory formation and recall. In this work, we applied a combination of optical and computational tools in the development of a novel strategy for the modulation of memories, and have expanded its application for interrogation of the hippocampal circuitry underlying memory processing in mice. First, we developed a closed-loop optogenetic stimulation platform to activate neurons implicated in memory processing (engram neurons) with a high temporal resolution. We applied this platform to modulate the activity of engram neurons and assess memory processing with respect to synchronous network activity. The results of our investigation support the proposal that encoding new information and recalling stored memories occur during distinct epochs of hippocampal network-wide oscillations. Having established the high efficacy of the modulation of engram neurons’ activity in a closed-loop fashion, we sought to combine it with two-photon imaging to enable high spatial resolution interrogation of hippocampal circuitry. We developed a behavioral apparatus for head-fixed engram modulation and the assessment of memory recall in immobile animals. Moreover, through the optimization of dual color two-photon imaging, we improved the ability to monitor activity of neurons in the subfields of the hippocampus with cellular specificity. The platform created here will be applied to investigate the effects of engram reactivation on downstream projections targets with high spatial and cell subtype specificity. Following these lines of investigations will enhance our understanding of memory modulation and could lead to novel neuromodulation treatments for neurological disorders associated with memory malfunctioning

    The Neural Mechanisms of Value Construction

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    Research in decision neuroscience has characterized how the brain makes decisions by assessing the expected utility of each option in an abstract value space that affords the ability to compare dissimilar options. Experiments at multiple levels of analysis in multiple species have localized the ventromedial prefrontal cortex (vmPFC) and nearby orbitofrontal cortex (OFC) as the main nexus where this abstract value space is represented. However, much less is known about how this value code is constructed by the brain in the first place. By using a combination of behavioral modeling and cutting edge tools to analyze functional magnetic resonance imaging (fMRI) data, the work of this thesis proposes that the brain decomposes stimuli into their constituent attributes and integrates across them to construct value. These stimulus features embody appetitive or aversive properties that are either learned from experience or evaluated online by comparing them to previously experienced stimuli with similar features. Stimulus features are processed by cortical areas specialized for the perception of a particular stimulus type and then integrated into a value signal in vmPFC/OFC. The project presented in Chapter 2 examines how food items are evaluated by their constituent attributes, namely their nutrient makeup. A linear attribute integration model succinctly captures how subjective values can be computed from a weighted combination of the constituent nutritive attributes of the food. Multivariate analysis methods revealed that these nutrient attributes are represented in the lateral OFC, while food value is encoded both in medial and lateral OFC. Connectivity between lateral and medial OFC allows this nutrient attribute information to be integrated into a value representation in medial OFC. In Chapter 3, I show that this value construction process can operate over higher-level abstractions when the context requires bundles of items to be valued, rather than isolated items. When valuing bundles of items, the constituent items themselves become the features, and their values are integrated with a subadditive function to construct the value of the bundle. Multiple subregions of PFC including but not limited to vmPFC compute the value of a bundle with the same value code used to evaluate individual items, suggesting that these general value regions contextually adapt within this hierarchy. When valuing bundles and single items in interleaved trials, the value code rapidly switches between levels in this hierarchy by normalizing to the distribution of values in the current context rather than representing all options on an absolute scale. Although the attribute integration model of value construction characterizes human behavior on simple decision-making tasks, it is unclear how it can scale up to environments of real-world complexity. Taking inspiration from modern advances in artificial intelligence, and deep reinforcement learning in particular, in Chapter 4 I outline how connectionist models generalize the attribute integration model to naturalistic tasks by decomposing sensory input into a high dimensional set of nonlinear features that are encoded with hierarchical and distributed processing. Participants freely played Atari video games during fMRI scanning, and a deep reinforcement learning algorithm trained on the games was used as an end-to-end model for how humans evaluate actions in these high-dimensional tasks. The features represented in the intermediate layers of the artificial neural network were found to also be encoded in a distributed fashion throughout the cortex, specifically in the dorsal visual stream and posterior parietal cortex. These features emerge from nonlinear transformations of the sensory input that connect perception to action and reward. In contrast to the stimulus attributes used to evaluate the stimuli presented in the preceding chapters, these features become highly complex and inscrutable as they are driven by the statistical properties of high-dimensional data. However, they do not solely reflect a set of features that can be identified by applying common dimensionality reduction techniques to the input, as task-irrelevant sensory features are stripped away and task-relevant high-level features are magnified.</p

    The role of cellular chloride channels during human respiratory syncytial virus infection

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    Human respiratory syncytial virus (HRSV) is a common cause of respiratory tract infections (RTIs) globally. Of those infected, 25%–40% aged ≤1 year develop severe lower RTIs leading to pneumonia and bronchiolitis, with ~10% requiring hospitalisation. There is currently no HRSV vaccine and clinically approved treatments are only moderately effective. New and more effective anti-HRSV strategies are urgently required. It is established that viruses require cellular ion channels to infect cells. Ion channels are a diverse class of transmembrane proteins that selectively allow ions across membranes, influencing a multitude of cellular processes. Modulation of these channels by viruses is an important host-pathogen interaction that regulates critical stages of the virus multiplication cycle including entry, replication, and egress. Cellular chloride (Cl-) channels are large family of ion channels which were historically overlooked, however the importance of these proteins, especially within the respiratory tract, is now being revealed. This thesis examined the role of Cl- channels during HRSV infection. Utilising GFP-expressing HRSV in combination with an extensive panel of channel-specific pharmacological inhibitors, a critical requirement for calcium (Ca2+)-activated chloride channels (CaCCs) during HRSV infection was highlighted. For the first time, a role for TMEM16A as a host-factor was revealed and the channel was implicated as a post-exposure antiviral target. An investigation into the mechanisms underpinning the relationship between HRSV and TMEM16A revealed that the channel was involved at the genome replication and/or transcription stage of infection, and evidence suggested that this interaction may occur at or near the Golgi, in HRSV replication factories. Lastly, a role for TMEM16A was described within the HRSV-mediated production of antiviral protein interferon γ-induced protein 10 (IP-10), which supported a hypothesis wherein HRSV sequestered TMEM16A for genome replication, and simultaneously prevented the cellular antiviral response. Therefore, these findings have revealed TMEM16A as an exciting target for future host-directed antiviral therapeutics
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