Determining the immune response in human immunodefficiency virus infection : HIV -1 diversity as tool for epidemic monitoring

The Human Immunodeficiency Virus type 1 (HIV-1) is characterized by extensive genetic diversity at the population level but also within a single infected individual. The swift capacity of the virus to generate extensive diversity within the human host played a central role in the origin of the disease and is also key for the current global proportions of the HIV-1 pandemic. The epidemic started in Africa with multiple zoonotic transmissions of simian immunodeficiency virus (SIV) to humans. This was followed by a period of diversification and adaptation to the human population that, enhanced by the high rates of mutation and recombination of the virus, allowed the emergence of a virus capable of efficient sexual transmission among humans. The spread of the human adapted virus is estimated to have initiated from late 1950s to the early 1960s from Africa to the rest of the world. The predominance of the subtype B HIV-1 virus in Western Europe suggests that this was the first subtype to be introduced in this region. The subtype diversity pattern of HIV-1 in Portugal resembles the ones found in Central Africa being far more complex than the viral diversity patterns observed in the rest of Western Europe highlighting the relevance of in detailed studies of the Portuguese HIV-1 epidemics. In this work we have characterized the local HIV-1 epidemic of the Portuguese city of Braga in the years from 2000 to 2012. We found that the most frequent HIV-1 subtypes were G and B and by combining epidemiological and phylogenetic analysis we were able to uncover local transmission clusters of non-B and non-G subtypes among locals in association with sexual transmission networks that initiated transmission in the early 2000s. This corroborates Portugal as an early point of introduction of non-B HIV-1 subtypes in Western Europe. Having performed this characterization at the level of this local population we then focused on analyzing the duration of infection at the level of the infected patient. For this purpose we have optimized a methodology to differentiate recent from chronic infections. It was based on the study of ambiguous nucleotide calls obtained from routine HIV-1 genotyping. We found that the analysis of these ambiguities, as an expression of intra-host HIV-1 diversity, allowed the inference of the duration of infection in this study population. Subsequently, we questioned if high HIV-1 subtype diversity found in this region correlated with higher rates of transmission of drug resistance mutations. We found that the level of transmitted drug resistance in this population was similar to other European regions and independent predictors of transmitted drug resistance (TDR) could not be identified supporting the recommendation of universal viral sequencing at patient admission. This study performed in a country that is unique in Western Europe in what regards to HIV-1 diversity supported Portugal as one of the early entry-points of non-B HIV-1 subtypes in Western Europe and also reinforced the need for more efficacious local control measures targeting sexual transmission routes. We believe this study is of general importance especially in a time when several reports suggest that the prevalence of non-B subtypes in Western Europe is increasing. The knowledge herein generated also contributed for the development of method to discriminate recent from non-recent HIV-1 infections, a step of crucial importance to validate prevention strategies. Importantly, it was also shown that the higher HIV-1 subtype diversity found in this study population does not correlate with an increase in the rate of transmission of drug resistance when compared to the rest of the Western Europe.O Vírus da Imunodeficiência Humana Tipo 1 (VIH-1) é caracterizado por uma extensa diversidade genética não só a nível da população, mas também a nível individual, em cada hospedeiro. A rapidez do vírus para gerar grande diversidade dentro do hospedeiro humano desempenhou um papel central na génese da doença e é também essencial para as proporções globais atuais da pandemia do VIH-1. A epidemia começou em África, com várias transmissões zoonóticas de vírus da imunodeficiência símia (SIV) para seres humanos. Isto foi seguido por um período de diversificação e adaptação na população humana que, amplificada pelas altas taxas de mutação e de recombinação do vírus, permitiu o surgimento de uma nova espécie de vírus capaz de transmissão sexual eficiente entre os seres humanos. O início da propagação deste vírus já adaptado ao ser humano é estimada a partir do final dos anos 1950 ao início dos anos 1960, da África Central para o resto do mundo. A predominância do subtipo B do VIH-1 na Europa Ocidental sugere que este foi o primeiro subtipo a ser introduzido nesta região. O padrão de diversidade dos subtipos do VIH-1 em Portugal assemelha-se ao encontrado na África Central, sendo muito mais complexo do que os padrões de diversidade vírica observados no resto da Europa Ocidental. Este facto justifica o relevo que estudos detalhados sobre o VIH-1 em Portugal possam ter para a compreensão das epidemias de VIH-1. Neste trabalho foi caracterizada a epidemia local por VIH-1 na cidade portuguesa de Braga, entre os anos 2000 e 2012. Descobrimos que os subtipos VIH-1 mais frequentes foram G e B. Pela combinação de análise epidemiológica e filogenética pudemos demonstrar grupos de transmissão locais de subtipos não-B e não-G entre os residentes em associação com redes de transmissão sexual que iniciaram a transmissão no início da década de 2000. Isto indicia o papel de Portugal como um ponto de início da introdução de subtipos não-B do VIH-1 na Europa Ocidental. Tendo realizado esta caracterização a nível da população local, o trabalho concentrou-se na análise da duração da infeção ao nível individual. Para este efeito, aperfeiçoou-se uma metodologia para diferenciar infeção recente de infeção crónica. Baseados no estudo de posições nucleotídicas ambíguas obtidas a partir de genotipagem rotineira doVIH-1,descobrimos que a análise dessas ambiguidades, como uma expressão da diversidade intra-hospedeiro do VIH-1, permite inferir a duração da infeção nesta população em estudo. Posteriormente questionamos se a elevada diversidade do VIH-1 encontrada nesta região se poderia correlacionar com maiores taxas de transmissão de mutações de resistência aos antiretrovíricos. Descobrimos que o nível de resistência à terapêutica transmitido nesta população foi semelhante a outras regiões europeias. Não foram identificados preditores independentes de resistência transmissível aos antiretrovíricos, suportando a recomendação de sequenciamento viral universal no momento do contacto do doente com os serviços de saúde. Este estudo realizado num país que é único na Europa Ocidental no que diz respeito à diversidade do VIH-1,validoua noção de Portugal como um dos pontos de entrada iniciais de subtipos não-B do VIH-1 na Europa Ocidental e também reforçou a necessidade de medidas locais de controlo mais eficazes, que visem modos de transmissão sexual. Acreditamos que este estudo é relevante, especialmente num tempo em que vários artigos sugerem que a prevalência de subtipos não-B na Europa Ocidental está a aumentar. O conhecimento aqui gerado também contribuiu para o desenvolvimento de um método para discriminar infeções recentes pelo HIV-1 das não-recentes, um passo de importância crucial para validar as estratégias de prevenção. Relevantemente, também foi demonstrado que à maior diversidade doVIH-1 encontrada na população em estudo, não correspondeu um aumento na taxa de transmissão de resistência à terapêutica, quando comparada com o resto da Europa Ocidental

Applying dynamic Bayesian networks in transliteration detection and generation

Peter Nabende promoveert op methoden die programma’s voor automatisch vertalen kunnen verbeteren. Hij onderzocht twee systemen voor het genereren en vergelijken van transcripties: een DBN-model (Dynamische Bayesiaanse Netwerken) waarin Pair Hidden Markovmodellen zijn geïmplementeerd en een DBN-model dat op transductie is gebaseerd. Nabende onderzocht het effect van verschillende DBN-parameters op de kwaliteit van de geproduceerde transcripties. Voor de evaluatie van de DBN-modellen gebruikte hij standaard dataverzamelingen van elf taalparen: Engels-Arabisch, Engels-Bengaals, Engels-Chinees, Engels-Duits, Engels-Frans, Engels-Hindi, Engels-Kannada, Engels-Nederlands, Engels-Russisch, Engels-Tamil en Engels-Thai. Tijdens het onderzoek probeerde hij om verschillende modellen te combineren. Dat bleek een goed resultaat op te leveren

Automatic extraction of robotic surgery actions from text and kinematic data

The latest generation of robotic systems is becoming increasingly autonomous due to technological advancements and artificial intelligence. The medical field, particularly surgery, is also interested in these technologies because automation would benefit surgeons and patients. While the research community is active in this direction, commercial surgical robots do not currently operate autonomously due to the risks involved in dealing with human patients: it is still considered safer to rely on human surgeons' intelligence for decision-making issues. This means that robots must possess human-like intelligence, including various reasoning capabilities and extensive knowledge, to become more autonomous and credible. As demonstrated by current research in the field, indeed, one of the most critical aspects in developing autonomous systems is the acquisition and management of knowledge. In particular, a surgical robot must base its actions on solid procedural surgical knowledge to operate autonomously, safely, and expertly. This thesis investigates different possibilities for automatically extracting and managing knowledge from text and kinematic data. In the first part, we investigated the possibility of extracting procedural surgical knowledge from real intervention descriptions available in textbooks and academic papers on the robotic-surgical domains, by exploiting Transformer-based pre-trained language models. In particular, we released SurgicBERTa, a RoBERTa-based pre-trained language model for surgical literature understanding. It has been used to detect procedural sentences in books and extract procedural elements from them. Then, with some use cases, we explored the possibilities of translating written instructions into logical rules usable for robotic planning. Since not all the knowledge required for automatizing a procedure is written in texts, we introduce the concept of surgical commonsense, showing how it relates to different autonomy levels. In the second part of the thesis, we analyzed surgical procedures from a lower granularity level, showing how each surgical gesture is associated with a given combination of kinematic data

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

Vaccine semantics : Automatic methods for recognizing, representing, and reasoning about vaccine-related information

Post-marketing management and decision-making about vaccines builds on the early detection of safety concerns and changes in public sentiment, the accurate access to established evidence, and the ability to promptly quantify effects and verify hypotheses about the vaccine benefits and risks. A variety of resources provide relevant information but they use different representations, which makes rapid evidence generation and extraction challenging. This thesis presents automatic methods for interpreting heterogeneously represented vaccine information. Part I evaluates social media messages for monitoring vaccine adverse events and public sentiment in social media messages, using automatic methods for information recognition. Parts II and III develop and evaluate automatic methods and res

Investigating multilingual approaches for parsing universal dependencies

Multilingual dependency parsing encapsulates any attempt to parse multiple languages. It can involve parsing multiple languages in isolation (poly-monolingual), leveraging training data from multiple languages to process any of the included languages (polyglot), or training on one or multiple languages to process a low-resource language with no training data (zero-shot). In this thesis, we explore multilingual dependency parsing across all three paradigms, first analysing whether polyglot training on a number of source languages is beneficial for processing a target language in a zero-shot cross-lingual dependency parsing experiment using annotation projection. The results of this experiment show that polyglot training produces an overall trend of better results on the target language but a highly-related single source language can still be better for transfer. We then look at the role of pretrained language models in processing a moderately low-resource language in Irish. Here, we develop our own monolingual Irish BERT model gaBERT from scratch and compare it to a number of multilingual baselines, showing that developing a monolingual language model for Irish is worthwhile. We then turn to the topic of parsing Enhanced Universal Dependencies (EUD) Graphs, which are an extension of basic Universal Dependencies trees, where we describe the DCU-EPFL submission to the 2021 IWPT shared task on EUD parsing. Here, we developed a multitask model to jointly learn the tasks of basic dependency parsing and EUD graph parsing, showing improvements over a single-task basic dependency parser. Lastly, we revisit the topic of polyglot parsing and investigate whether multiview learning can be applied to the problem of multilingual dependency parsing. Here, we learn different views based on the dataset source. We show that multiview learning can be used to train parsers with multiple datasets, showing a general improvement over single-view baselines

Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise