Bacterial inter-species communication mediated by the autoinducer-2 signal

Dissertation presented to obtain the Ph.D degree in Biology by Universidade Nova de Lisboa, Instituto de Tecnologia Química e Biológica, Instituto Gulbenkian de Ciência.During the last few decades, scientists have come to appreciate the immense complexity in bacterial signaling interactions that sustain microbial communities. Quorum-sensing (QS) is a cell-cell communication process whereby single cell bacteria regulate gene expression synchronously in a population in response to self-produced extracellular signal molecules, called autoinducers. Autoinducer-2 (AI-2), the synthase of which, LuxS, is present in both Gram-negative and Gram-positive bacteria, was proposed to represent a non-species-specific signal that mediates inter-species communication. In enteric bacteria, extracellular AI-2 levels peak in late exponential phase and rapidly decline as bacteria continue to grow. This depletion occurs because AI-2 activates the expression of an operon, lsr (for LuxS Regulated), encoding the Lsr transporter and enzymes that degrade the signal. As the Lsr system imports self and non-self AI-2, lsr-containing bacteria can interfere with AI-2 signaling of other species and shut off group behaviors regulated by this molecule: this system represents the first example of interference with a bacterial inter-species QS signal.(...)Fundação para a Ciência e Tecnologia financial support with the grant SFRH / BD / 28543 / 2006

Risk prediction analysis for post-surgical complications in cardiothoracic surgery

Cardiothoracic surgery patients have the risk of developing surgical site infections (SSIs), which causes hospital readmissions, increases healthcare costs and may lead to mortality. The first 30 days after hospital discharge are crucial for preventing these kind of infections. As an alternative to a hospital-based diagnosis, an automatic digital monitoring system can help with the early detection of SSIs by analyzing daily images of patient’s wounds. However, analyzing a wound automatically is one of the biggest challenges in medical image analysis. The proposed system is integrated into a research project called CardioFollowAI, which developed a digital telemonitoring service to follow-up the recovery of cardiothoracic surgery patients. This present work aims to tackle the problem of SSIs by predicting the existence of worrying alterations in wound images taken by patients, with the help of machine learning and deep learning algorithms. The developed system is divided into a segmentation model which detects the wound region area and categorizes the wound type, and a classification model which predicts the occurrence of alterations in the wounds. The dataset consists of 1337 images with chest wounds (WC), drainage wounds (WD) and leg wounds (WL) from 34 cardiothoracic surgery patients. For segmenting the images, an architecture with a Mobilenet encoder and an Unet decoder was used to obtain the regions of interest (ROI) and attribute the wound class. The following model was divided into three sub-classifiers for each wound type, in order to improve the model’s performance. Color and textural features were extracted from the wound’s ROIs to feed one of the three machine learning classifiers (random Forest, support vector machine and K-nearest neighbors), that predict the final output. The segmentation model achieved a final mean IoU of 89.9%, a dice coefficient of 94.6% and a mean average precision of 90.1%, showing good results. As for the algorithms that performed classification, the WL classifier exhibited the best results with a 87.6% recall and 52.6% precision, while WC classifier achieved a 71.4% recall and 36.0% precision. The WD had the worst performance with a 68.4% recall and 33.2% precision. The obtained results demonstrate the feasibility of this solution, which can be a start for preventing SSIs through image analysis with artificial intelligence.Os pacientes submetidos a uma cirurgia cardiotorácica tem o risco de desenvolver infeções no local da ferida cirúrgica, o que pode consequentemente levar a readmissões hospitalares, ao aumento dos custos na saúde e à mortalidade. Os primeiros 30 dias após a alta hospitalar são cruciais na prevenção destas infecções. Assim, como alternativa ao diagnóstico no hospital, a utilização diária de um sistema digital e automático de monotorização em imagens de feridas cirúrgicas pode ajudar na precoce deteção destas infeções. No entanto, a análise automática de feridas é um dos grandes desafios em análise de imagens médicas. O sistema proposto integra um projeto de investigação designado CardioFollow.AI, que desenvolveu um serviço digital de telemonitorização para realizar o follow-up da recuperação dos pacientes de cirurgia cardiotorácica. Neste trabalho, o problema da infeção de feridas cirúrgicas é abordado, através da deteção de alterações preocupantes na ferida com ajuda de algoritmos de aprendizagem automática. O sistema desenvolvido divide-se num modelo de segmentação, que deteta a região da ferida e a categoriza consoante o seu tipo, e num modelo de classificação que prevê a existência de alterações na ferida. O conjunto de dados consistiu em 1337 imagens de feridas do peito (WC), feridas dos tubos de drenagem (WD) e feridas da perna (WL), provenientes de 34 pacientes de cirurgia cardiotorácica. A segmentação de imagem foi realizada através da combinação de Mobilenet como codificador e Unet como decodificador, de forma a obter-se as regiões de interesse e atribuir a classe da ferida. O modelo seguinte foi dividido em três subclassificadores para cada tipo de ferida, de forma a melhorar a performance do modelo. Caraterísticas de cor e textura foram extraídas da região da ferida para serem introduzidas num dos modelos de aprendizagem automática de forma a prever a classificação final (Random Forest, Support Vector Machine and K-Nearest Neighbors). O modelo de segmentação demonstrou bons resultados ao obter um IoU médio final de 89.9%, um dice de 94.6% e uma média de precisão de 90.1%. Relativamente aos algoritmos que realizaram a classificação, o classificador WL exibiu os melhores resultados com 87.6% de recall e 62.6% de precisão, enquanto o classificador das WC conseguiu um recall de 71.4% e 36.0% de precisão. Por fim, o classificador das WD teve a pior performance com um recall de 68.4% e 33.2% de precisão. Os resultados obtidos demonstram a viabilidade desta solução, que constitui o início da prevenção de infeções em feridas cirúrgica a partir da análise de imagem, com recurso a inteligência artificial

Discourse polarization index: Analysis of top-down and ground-up political discourses in Portugal

An increasing number of events across the world have been a warning for democracy stability in established democratic countries. Events such as Hungary’s prime minister Viktor Orbán publicity doubting that liberal democracies could remain globally competitive, and the increasing voting share of anti-establishment parties in European member states are consequences of the political polarization phenomenon which endangers our democracy. To understand if we are becoming more polarized, literature has been focusing on measuring political polarization through surveys and voting data, without consistent evidence for any trend. Although the theoretical definition of political polarization has found stability in the literature, the different forms of measuring it have not. The measurement of political polarization needs to be more robust and extended to mass society besides elite society, enabling a comparison between the two, and within the real life and the digital. This dissertation answers this need, measuring political polarization, using text-as-data and computational social science methods, in an effective way independent of manual tasks, language, survey or pooling, polarization’s actors, and environments. It uses an empirical framework applied to parliamentary discourses and Twitter data to create a Discourse Polarization Index (DPI) which enables the assessment of the evolution of political polarization in discourse, considering its state and process. Portugal is used as use case, showing an increase in political polarization from 2015 to 2021, for the elite and the mass society, with similar behaviour between the two groups. A semantic validation is done, and research future steps are given.Diversos acontecimentos mundiais põem em causa a estabilidade democrática nos países democráticos. Destacando-se o comentário do primeiro-ministro húngaro, Viktor Orban, que declarou que as democracias atuais podem não ser competitivas globalmente, justificando a inclinação por uma autocracia, assim como o número crescente de partidos antissistema na Europa ocidental. Ambos os eventos são consequência da polarização política, um fenómeno que tem vindo a pôr em risco as democracias ocidentais. Para perceber a tendência, a literatura tem-se focado na medição quantitativa da polarização política através de questionários e sondagens, sem nenhuma tendência identificada. A quantificação da polarização política precisa de ser mais robusta e estudar também a polarização da massa publica, para além da elite, sendo possível assim a comparação da polarização entre os dois grupos, mas também entre os ambientes em que interagem, na vida real ou no digital. Esta dissertação responde a essa necessidade, medindo a polarização política, usando texto e métodos de ciências sociais computacionais, independente da língua, dos questionários, das sondagens e de tarefas manuais. A dissertação usa um modelo matemático empírico aplicado ao discurso parlamentar e a dados retirados do Twitter para criar o Índice de Polarização no Discurso. Este índice permite avaliar a evolução da polarização no discurso, considerando as suas características de estado e processo. Portugal é usado como caso de estudo, mostrando um aumento da polarização política entre 2015 e 2021, para a elite e massa pública, com comportamentos semelhantes. É efetuada uma validação semântica e são dadas recomendações para próximos passos de investigação

An analysis of bitcoin as an asset

Mestrado Bolonha em Mathematical FinanceBitcoin is nowadays one of the most popular topics among the public and academia. The increased popularity and market capitalization of Bitcoin have generated much controversy among the scientific community about whether this cryptocurrency can be used as an asset or as a new form of a medium of exchange. With these questions in mind, I decided first to assess if Bitcoin can be incorporated into one singular category, asset, or medium of exchange; or if it is a mix of these two. Second, enquire how its variance shifts according to shocks in the market and if this reaction can be compared with Gold. Third, infer if there is an asymmetry effect in volatility, i.e., if bad news generate less volatility than good news. The conclusions drawn from this study are that Bitcoin does not fit entirely into one category. There is no clear indication as to whether Bitcoin is a medium of exchange or an asset. Regarding its behavior in the market, it is possible to conclude that Bitcoin does not have management capabilities, and concerning the comparison with Gold, I concluded that Gold is still superior in terms of being a good asset to hedge market risk. The asymmetry effect is not significant in Bitcoin, whereas in Gold, several studies proved that this effect is one of the main properties that make Gold a safe haven.info:eu-repo/semantics/publishedVersio