37 research outputs found

    Evolving meaning: using genetic programming to learn similarity perspectives for mining biomedical data

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    Tese de mestrado, Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2019Nos últimos anos, as ontologias biomédicas tornaram-se fundamentais para descrever o conhecimento biológico na forma de grafos de conhecimento. Consequentemente, foram propostas várias abordagens de mineração de dados que tiram partido destes grafos de conhecimento. Estas abordagens baseiam-se em representações vetoriais que podem não capturar toda a informação semântica subjacente aos grafos. Uma abordagem alternativa consiste em utilizar a semelhança semântica como representação semântica. No entanto, como as ontologias podem modelar várias perspetivas, a semelhança semântica pode ser calculada tendo em consideração diferentes aspetos. Deste modo, diferentes tarefas de aprendizagem automática podem exigir diferentes perspetivas do grafo de conhecimento. Selecionar os aspetos semânticos mais relevantes, ou a melhor combinação destes para suportar uma determinada tarefa de aprendizagem não é trivial e, normalmente, exige conhecimento especializado. Nesta dissertação, apresentamos uma nova abordagem usando a Programação Genética sobre um conjunto de semelhanças semânticas, cada uma calculada com base num aspeto semântico dos dados, para obter a melhor combinação para uma dada tarefa de aprendizagem supervisionada. A metodologia inclui três etapas sequenciais: calcular a semelhança semântica para cada aspeto semântico; aprender a melhor combinação desses aspetos usando a Programação Genética; integrar a melhor combinação com o algoritmo de classificação. A abordagem foi avaliada em nove conjuntos de dados para prever a interação entre proteínas. Nesta aplicação, a Gene Ontology foi utilizada como grafo de conhecimento para suportar o cálculo da semelhança semântica. Como referência, utilizámos uma variação da abordagem proposta com estratégias manuais frequentemente utilizadas para combinar os aspetos semânticos. Os resultados demonstraram que as combinações obtidas com a Programação Genética superaram as combinações escolhidas manualmente que emulam o conhecimento especializado. A nossa abordagem foi também capaz de aprender modelos agnósticos em relação à espécie usando diferentes combinações de espécies para treino e teste, ultrapassando assim as limitações de prever interações entre proteínas para espécies com poucas interações conhecidas. Esta nova metodologia supera as limitações impostas pela necessidade de selecionar manualmente os aspetos semânticos que devem ser considerados para uma dada tarefa de aprendizagem. A aplicação da metodologia à previsão da interação entre proteínas foi bem-sucedida, perspetivando outras aplicações.In recent years, biomedical ontologies have become important for describing existing biological knowledge in the form of knowledge graphs. Data mining approaches that work with knowledge graphs have been proposed, but they are based on vector representations that do not capture the full underlying semantics. An alternative is to use machine learning approaches that explore semantic similarity. However, since ontologies can model multiple perspectives, semantic similarity computations for a given learning task need to be fine-tuned to account for this. Obtaining the best combination of semantic similarity aspects for each learning task is not trivial and typically depends on expert knowledge. In this dissertation, we developed a novel approach that applies Genetic Programming over a set of semantic similarity features, each based on a semantic aspect of the data, to obtain the best combination for a given supervised learning task. The methodology includes three sequential steps: compute the semantic similarity for each semantic aspect; learn the best combination of those aspects using Genetic Programming; integrate the best combination with a classification algorithm. The approach was evaluated on several benchmark datasets of protein-protein interaction prediction. The quality of the classifications is evaluated using the weighted average F-measure for each dataset. As a baseline, we employed a variation of the proposed methodology that instead of using evolved combinations, uses static combinations. For protein-protein interaction prediction, Gene Ontology was used as the knowledge graph to support semantic similarity, and it outperformed manually selected combinations of semantic aspects emulating expert knowledge. Our approach was also able to learn species-agnostic models with different combinations of species for training and testing, effectively addressing the limitations of predicting proteinprotein interactions for species with fewer known interactions. This dissertation proposes a novel methodology to overcome one of the limitations in knowledge graph-based semantic similarity applications: the need to expertly select which aspects should be taken into account for a given application. The methodology is particularly important for biomedical applications where data is often complex and multi-domain. Applying this methodology to protein-protein interaction prediction proved successful, paving the way to broader applications

    Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

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    Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

    Trajetórias da Educomunicação nas Políticas Públicas e a Formação de seus Profissionais

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    Esta obra é composta com os trabalhos apresentados no primeiro subtema, TRAJETÓRIA – Educação para a Comunicação como Política pública, nas perspectivas da Educomunicação e da Mídia-Educação, do II Congresso Internacional de Comunicação e Educação. Os artigos pretendem propiciar trocas de informações e produzir reflexões com os leitores sobre os caminhos percorridos, e ainda a percorrer, tendo como meta a expansão e a legitimação das práticas educomunicativas e/ou mídia-educativas como política pública para o atendimento à formação de crianças, adolescentes, jovens e adultos, no Brasil e no mundo

    A global experiment on motivating social distancing during the COVID-19 pandemic

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    Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges

    Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

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    Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

    A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

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    The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world
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