11,252 research outputs found

    Anuário científico da Escola Superior de Tecnologia da Saúde de Lisboa - 2021

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    É com grande prazer que apresentamos a mais recente edição (a 11.ª) do Anuário Científico da Escola Superior de Tecnologia da Saúde de Lisboa. Como instituição de ensino superior, temos o compromisso de promover e incentivar a pesquisa científica em todas as áreas do conhecimento que contemplam a nossa missão. Esta publicação tem como objetivo divulgar toda a produção científica desenvolvida pelos Professores, Investigadores, Estudantes e Pessoal não Docente da ESTeSL durante 2021. Este Anuário é, assim, o reflexo do trabalho árduo e dedicado da nossa comunidade, que se empenhou na produção de conteúdo científico de elevada qualidade e partilhada com a Sociedade na forma de livros, capítulos de livros, artigos publicados em revistas nacionais e internacionais, resumos de comunicações orais e pósteres, bem como resultado dos trabalhos de 1º e 2º ciclo. Com isto, o conteúdo desta publicação abrange uma ampla variedade de tópicos, desde temas mais fundamentais até estudos de aplicação prática em contextos específicos de Saúde, refletindo desta forma a pluralidade e diversidade de áreas que definem, e tornam única, a ESTeSL. Acreditamos que a investigação e pesquisa científica é um eixo fundamental para o desenvolvimento da sociedade e é por isso que incentivamos os nossos estudantes a envolverem-se em atividades de pesquisa e prática baseada na evidência desde o início dos seus estudos na ESTeSL. Esta publicação é um exemplo do sucesso desses esforços, sendo a maior de sempre, o que faz com que estejamos muito orgulhosos em partilhar os resultados e descobertas dos nossos investigadores com a comunidade científica e o público em geral. Esperamos que este Anuário inspire e motive outros estudantes, profissionais de saúde, professores e outros colaboradores a continuarem a explorar novas ideias e contribuir para o avanço da ciência e da tecnologia no corpo de conhecimento próprio das áreas que compõe a ESTeSL. Agradecemos a todos os envolvidos na produção deste anuário e desejamos uma leitura inspiradora e agradável.info:eu-repo/semantics/publishedVersio

    Strategies for Early Learners

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    Welcome to learning about how to effectively plan curriculum for young children. This textbook will address: • Developing curriculum through the planning cycle • Theories that inform what we know about how children learn and the best ways for teachers to support learning • The three components of developmentally appropriate practice • Importance and value of play and intentional teaching • Different models of curriculum • Process of lesson planning (documenting planned experiences for children) • Physical, temporal, and social environments that set the stage for children’s learning • Appropriate guidance techniques to support children’s behaviors as the self-regulation abilities mature. • Planning for preschool-aged children in specific domains including o Physical development o Language and literacy o Math o Science o Creative (the visual and performing arts) o Diversity (social science and history) o Health and safety • Making children’s learning visible through documentation and assessmenthttps://scholar.utc.edu/open-textbooks/1001/thumbnail.jp

    TOWARDS AN UNDERSTANDING OF EFFORTFUL FUNDRAISING EXPERIENCES: USING INTERPRETATIVE PHENOMENOLOGICAL ANALYSIS IN FUNDRAISING RESEARCH

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    Physical-activity oriented community fundraising has experienced an exponential growth in popularity over the past 15 years. The aim of this study was to explore the value of effortful fundraising experiences, from the point of view of participants, and explore the impact that these experiences have on people’s lives. This study used an IPA approach to interview 23 individuals, recognising the role of participants as proxy (nonprofessional) fundraisers for charitable organisations, and the unique organisation donor dynamic that this creates. It also bought together relevant psychological theory related to physical activity fundraising experiences (through a narrative literature review) and used primary interview data to substantiate these. Effortful fundraising experiences are examined in detail to understand their significance to participants, and how such experiences influence their connection with a charity or cause. This was done with an idiographic focus at first, before examining convergences and divergences across the sample. This study found that effortful fundraising experiences can have a profound positive impact upon community fundraisers in both the short and the long term. Additionally, it found that these experiences can be opportunities for charitable organisations to create lasting meaningful relationships with participants, and foster mutually beneficial lifetime relationships with them. Further research is needed to test specific psychological theory in this context, including self-esteem theory, self determination theory, and the martyrdom effect (among others)

    Machine learning for managing structured and semi-structured data

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    As the digitalization of private, commercial, and public sectors advances rapidly, an increasing amount of data is becoming available. In order to gain insights or knowledge from these enormous amounts of raw data, a deep analysis is essential. The immense volume requires highly automated processes with minimal manual interaction. In recent years, machine learning methods have taken on a central role in this task. In addition to the individual data points, their interrelationships often play a decisive role, e.g. whether two patients are related to each other or whether they are treated by the same physician. Hence, relational learning is an important branch of research, which studies how to harness this explicitly available structural information between different data points. Recently, graph neural networks have gained importance. These can be considered an extension of convolutional neural networks from regular grids to general (irregular) graphs. Knowledge graphs play an essential role in representing facts about entities in a machine-readable way. While great efforts are made to store as many facts as possible in these graphs, they often remain incomplete, i.e., true facts are missing. Manual verification and expansion of the graphs is becoming increasingly difficult due to the large volume of data and must therefore be assisted or substituted by automated procedures which predict missing facts. The field of knowledge graph completion can be roughly divided into two categories: Link Prediction and Entity Alignment. In Link Prediction, machine learning models are trained to predict unknown facts between entities based on the known facts. Entity Alignment aims at identifying shared entities between graphs in order to link several such knowledge graphs based on some provided seed alignment pairs. In this thesis, we present important advances in the field of knowledge graph completion. For Entity Alignment, we show how to reduce the number of required seed alignments while maintaining performance by novel active learning techniques. We also discuss the power of textual features and show that graph-neural-network-based methods have difficulties with noisy alignment data. For Link Prediction, we demonstrate how to improve the prediction for unknown entities at training time by exploiting additional metadata on individual statements, often available in modern graphs. Supported with results from a large-scale experimental study, we present an analysis of the effect of individual components of machine learning models, e.g., the interaction function or loss criterion, on the task of link prediction. We also introduce a software library that simplifies the implementation and study of such components and makes them accessible to a wide research community, ranging from relational learning researchers to applied fields, such as life sciences. Finally, we propose a novel metric for evaluating ranking results, as used for both completion tasks. It allows for easier interpretation and comparison, especially in cases with different numbers of ranking candidates, as encountered in the de-facto standard evaluation protocols for both tasks.Mit der rasant fortschreitenden Digitalisierung des privaten, kommerziellen und öffentlichen Sektors werden immer größere Datenmengen verfügbar. Um aus diesen enormen Mengen an Rohdaten Erkenntnisse oder Wissen zu gewinnen, ist eine tiefgehende Analyse unerlässlich. Das immense Volumen erfordert hochautomatisierte Prozesse mit minimaler manueller Interaktion. In den letzten Jahren haben Methoden des maschinellen Lernens eine zentrale Rolle bei dieser Aufgabe eingenommen. Neben den einzelnen Datenpunkten spielen oft auch deren Zusammenhänge eine entscheidende Rolle, z.B. ob zwei Patienten miteinander verwandt sind oder ob sie vom selben Arzt behandelt werden. Daher ist das relationale Lernen ein wichtiger Forschungszweig, der untersucht, wie diese explizit verfügbaren strukturellen Informationen zwischen verschiedenen Datenpunkten nutzbar gemacht werden können. In letzter Zeit haben Graph Neural Networks an Bedeutung gewonnen. Diese können als eine Erweiterung von CNNs von regelmäßigen Gittern auf allgemeine (unregelmäßige) Graphen betrachtet werden. Wissensgraphen spielen eine wesentliche Rolle bei der Darstellung von Fakten über Entitäten in maschinenlesbaren Form. Obwohl große Anstrengungen unternommen werden, so viele Fakten wie möglich in diesen Graphen zu speichern, bleiben sie oft unvollständig, d. h. es fehlen Fakten. Die manuelle Überprüfung und Erweiterung der Graphen wird aufgrund der großen Datenmengen immer schwieriger und muss daher durch automatisierte Verfahren unterstützt oder ersetzt werden, die fehlende Fakten vorhersagen. Das Gebiet der Wissensgraphenvervollständigung lässt sich grob in zwei Kategorien einteilen: Link Prediction und Entity Alignment. Bei der Link Prediction werden maschinelle Lernmodelle trainiert, um unbekannte Fakten zwischen Entitäten auf der Grundlage der bekannten Fakten vorherzusagen. Entity Alignment zielt darauf ab, gemeinsame Entitäten zwischen Graphen zu identifizieren, um mehrere solcher Wissensgraphen auf der Grundlage einiger vorgegebener Paare zu verknüpfen. In dieser Arbeit stellen wir wichtige Fortschritte auf dem Gebiet der Vervollständigung von Wissensgraphen vor. Für das Entity Alignment zeigen wir, wie die Anzahl der benötigten Paare reduziert werden kann, während die Leistung durch neuartige aktive Lerntechniken erhalten bleibt. Wir erörtern auch die Leistungsfähigkeit von Textmerkmalen und zeigen, dass auf Graph-Neural-Networks basierende Methoden Schwierigkeiten mit verrauschten Paar-Daten haben. Für die Link Prediction demonstrieren wir, wie die Vorhersage für unbekannte Entitäten zur Trainingszeit verbessert werden kann, indem zusätzliche Metadaten zu einzelnen Aussagen genutzt werden, die oft in modernen Graphen verfügbar sind. Gestützt auf Ergebnisse einer groß angelegten experimentellen Studie präsentieren wir eine Analyse der Auswirkungen einzelner Komponenten von Modellen des maschinellen Lernens, z. B. der Interaktionsfunktion oder des Verlustkriteriums, auf die Aufgabe der Link Prediction. Außerdem stellen wir eine Softwarebibliothek vor, die die Implementierung und Untersuchung solcher Komponenten vereinfacht und sie einer breiten Forschungsgemeinschaft zugänglich macht, die von Forschern im Bereich des relationalen Lernens bis hin zu angewandten Bereichen wie den Biowissenschaften reicht. Schließlich schlagen wir eine neuartige Metrik für die Bewertung von Ranking-Ergebnissen vor, wie sie für beide Aufgaben verwendet wird. Sie ermöglicht eine einfachere Interpretation und einen leichteren Vergleich, insbesondere in Fällen mit einer unterschiedlichen Anzahl von Kandidaten, wie sie in den de-facto Standardbewertungsprotokollen für beide Aufgaben vorkommen

    Antibody Targeting of HIV-1 Env: A Structural Perspective

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    A key component of contemporary efforts toward a human immunodeficiency virus 1 (HIV-1) vaccine is the use of structural biology to understand the structural characteristics of antibodies elicited both from human patients and animals immunized with engineered 'immunogens,' or early vaccine candidates. This thesis will report on projects characterizing both types of antibodies against HIV-1. Chapter 1 will introduce relevant topics, including the reasons HIV-1 is particularly capable of evading the immune system in natural infection and after vaccination, the 20+ year history of unsuccessful HIV-1 vaccine large-scale efficacy trials, an introduction to broadly neutralizing antibodies (bNAbs), and a review of common strategies utilized in HIV-1 immunogen design today. Chapter 2 describes the isolation, high-resolution structural characterization, and in vitro resistance profile of a new bNAb, 1-18, that is both very broad and potent, as well as able to restrict HIV-1 escape in vivo. Chapter 3 reports the results of an epitope-focusing immunogen design and immunization experiment carried out in wild type mice, rabbits, and non-human primates where it was shown that B cells targeting the desired epitope were expanded after a single prime immunization with immunogen RC1 or a variant, RC1-4fill. Chapter 4 describes Ab1245, an off-target non-neutralizing monoclonal antibody isolated in a macaque that had been immunized with a series of sequential immunogens after the prime immunization reported in Chapter 3. The antibody structure describes a specific type of distracting response as it binds in a way that causes a large structural change in Env, resulting in the destruction of the neutralizing fusion peptide epitope. Chapter 5 is adapted from a review about how antibodies differentially recognize the viruses HIV-1, SARS-CoV-2, and Zika virus. This review serves as an introduction to the virus SARS-CoV-2, which is the topic of the final chapter, Chapter 6. In this chapter, structures of many neutralizing antibodies isolated from SARS-CoV-2 patients were used to define potentially therapeutic classes of neutralizing receptor-binding domain (RBD) antibodies based on their epitopes and binding profiles

    Unraveling the effect of sex on human genetic architecture

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    Sex is arguably the most important differentiating characteristic in most mammalian species, separating populations into different groups, with varying behaviors, morphologies, and physiologies based on their complement of sex chromosomes, amongst other factors. In humans, despite males and females sharing nearly identical genomes, there are differences between the sexes in complex traits and in the risk of a wide array of diseases. Sex provides the genome with a distinct hormonal milieu, differential gene expression, and environmental pressures arising from gender societal roles. This thus poses the possibility of observing gene by sex (GxS) interactions between the sexes that may contribute to some of the phenotypic differences observed. In recent years, there has been growing evidence of GxS, with common genetic variation presenting different effects on males and females. These studies have however been limited in regards to the number of traits studied and/or statistical power. Understanding sex differences in genetic architecture is of great importance as this could lead to improved understanding of potential differences in underlying biological pathways and disease etiology between the sexes and in turn help inform personalised treatments and precision medicine. In this thesis we provide insights into both the scope and mechanism of GxS across the genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK Biobank. We found small yet widespread differences in genetic architecture across traits through the calculation of sex-specific heritability, genetic correlations, and sex-stratified genome-wide association studies (GWAS). We further investigated whether sex-agnostic (non-stratified) efforts could potentially be missing information of interest, including sex-specific trait-relevant loci and increased phenotype prediction accuracies. Finally, we studied the potential functional role of sex differences in genetic architecture through sex biased expression quantitative trait loci (eQTL) and gene-level analyses. Overall, this study marks a broad examination of the genetics of sex differences. Our findings parallel previous reports, suggesting the presence of sexual genetic heterogeneity across complex traits of generally modest magnitude. Furthermore, our results suggest the need to consider sex-stratified analyses in future studies in order to shed light into possible sex-specific molecular mechanisms

    Chinese Benteng Women’s Participation in Local Development Affairs in Indonesia: Appropriate means for struggle and a pathway to claim citizen’ right?

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    It had been more than two decades passing by aftermath the devastating Asia’s Financial Crisis in 1997, subsequently followed by Suharto’s step down from his presidential throne which he occupied for more than three decades. The financial turmoil turned to a political disaster furthermore has led to massive looting that severely impacted Indonesians of Chinese descendant, including unresolved mystery of the most atrocious sexual violation against women and covert killings of students and democracy activists in this country. Since then, precisely aftermath May 1998, which publicly known as “Reformasi”1, Indonesia underwent political reform that eventually corresponded positively to its macroeconomic growth. Twenty years later, in 2018, Indonesia captured worldwide attention because it has successfully hosted two internationally renowned events, namely the Asian Games 2018 – the most prestigious sport events in Asia – conducted in Jakarta and Palembang; and the IMF/World Bank Annual Meeting 2018 in Bali. Particularly in the IMF/World Bank Annual Meeting, this event has significantly elevated Indonesia’s credibility and international prestige in the global economic powerplay as one of the nations with promising growth and openness. However, the narrative about poverty and inequality, including increasing racial tension, religious conservatism, and sexual violation against women are superseded by friendly climate for foreign investment and eventually excessive glorification of the nation’s economic growth. By portraying the image of promising new economic power, as rhetorically promised by President Joko Widodo during his presidential terms, Indonesia has swept the growing inequality in this highly stratified society that historically compounded with religious and racial tension under the carpet of digital economy.Arte y Humanidade
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