9,653 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

    Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

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    In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent

    Identifying and responding to people with mild learning disabilities in the probation service

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    It has long been recognised that, like many other individuals, people with learningdisabilities find their way into the criminal justice system. This fact is not disputed. Whathas been disputed, however, is the extent to which those with learning disabilities arerepresented within the various agencies of the criminal justice system and the ways inwhich the criminal justice system (and society) should address this. Recently, social andlegislative confusion over the best way to deal with offenders with learning disabilities andmental health problems has meant that the waters have become even more muddied.Despite current government uncertainty concerning the best way to support offenders withlearning disabilities, the probation service is likely to continue to play a key role in thesupervision of such offenders. The three studies contained herein aim to clarify the extentto which those with learning disabilities are represented in the probation service, toexamine the effectiveness of probation for them and to explore some of the ways in whichprobation could be adapted to fit their needs.Study 1 and study 2 showed that around 10% of offenders on probation in Kent appearedto have an IQ below 75, putting them in the bottom 5% of the general population. Study 3was designed to assess some of the support needs of those with learning disabilities in theprobation service, finding that many of the materials used by the probation service arelikely to be too complex for those with learning disabilities to use effectively. To addressthis, a model for service provision is tentatively suggested. This is based on the findings ofthe three studies and a pragmatic assessment of what the probation service is likely to becapable of achieving in the near future

    Wildlife trade in Latin America: people, economy and conservation

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    Wildlife trade is among the main threats to biodiversity conservation and may pose a risk to human health because of the spread of zoonotic diseases. To avoid social, economic and environmental consequences of illegal trade, it is crucial to understand the factors influencing the wildlife market and the effectiveness of policies already in place. I aim to unveil the biological and socioeconomic factors driving wildlife trade, the health risks imposed by the activity, and the effectiveness of certified captive-breeding as a strategy to curb the illegal market in Latin America through a multidisciplinary approach. I assess socioeconomic correlates of the emerging international trade in wild cat species from Latin America using a dataset of >1,000 seized cats, showing that high levels of corruption and Chinese private investment and low income per capita were related to higher numbers of jaguar seizures. I assess the effectiveness of primate captive-breeding programmes as an intervention to curb wildlife trafficking. Illegal sources held >70% of the primate market share. Legal primates are more expensive, and the production is not sufficiently high to fulfil the demand. I assess the scale of the illegal trade and ownership of venomous snakes in Brazil. Venomous snake taxa responsible for higher numbers of snakebites were those most often kept as pets. I uncover how online wildlife pet traders and consumers responded to campaigns associating the origin of the COVID-19 pandemic. Of 20,000 posts on Facebook groups, only 0.44% mentioned COVID-19 and several stimulated the trade in wild species during lockdown. Despite the existence of international and national wildlife trade regulations, I conclude that illegal wildlife trade is still an issue that needs further addressing in Latin America. I identify knowledge gaps and candidate interventions to amend the current loopholes to reduce wildlife trafficking. My aspiration with this thesis is to provide useful information that can inform better strategies to tackle illegal wildlife trade in Latin America

    A Molecular Approach to the Diagnosis, Assessment, Monitoring and Treatment of Pulmonary Non-Tuberculous Mycobacterial Disease

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    Introduction: Non-Tuberculous Mycobacteria (NTM) can cause disease of the lungs and sinuses, lymph nodes, joints and central nervous system as well as disseminated infections in immunocompromised individuals. Efforts to tackle infections in NTM are hampered by a lack of reliable biomarkers for diagnosis, assessment of disease activity, and prognostication. Aims: The broad aims of this thesis are: 1. to develop molecular assays capable of quantifying the 6 most common pathogenic mycobacteria (M. abscessus, M. avium, M. intracellulare, M. malmoense, M. kansasii, M. xenopi) and calculate comparative sensitivities and specificities for each assay. 2. to assess patients’ clinical course over 12 – 18 months by performing the developed molecular assays against DNA extracted from sputum from patients with NTM infection. 3. to assess dynamic bacterial changes of the lung microbiome in patients on treatment for NTM disease and those who are treatment na ve. Methods: DNA was extracted from a total of 410 sputum samples obtained from 38 patients who were either: • commencing treatment for either M. abscessus or Mycobacterium avium complex. • considered colonised with M. abscessus or Mycobacterium avium complex (i.e. cultured NTM but were not deemed to have infection as they did not meet ATS or BTS criteria for disease). • Diagnosed with cystic fibrosis (CF) or non-CF bronchiectasis but had never cultured NTM. For the development of quantitative molecular assays, NTM hsp65 gene sequences were aligned and interrogated for areas of variability. These variable regions enabled the creation of species specific probes. In vitro sensitivity and specificity for each probe was determined by testing each probe against a panel of plasmids containing hsp65 gene inserts from different NTM species. Quantification accuracy was determined by using each assay against a mock community containing serial dilutions of target DNA. Each sample was tested with the probes targeting: M. abscessus, M. avium and M. intracellulare producing a longitudinal assessment of NTM copy number during each patient’s clinical course. In addition, a total of 64 samples from 16 patients underwent 16S rRNA gene sequencing to characterise longitudinal changes in the microbiome of both NTM disease and controls. Results: In vitro sensitivity for the custom assays were 100% and specificity ranged from 91.6% to 100%. In terms of quantification accuracy, there was no significant difference between the measured results of each assay and the expected values when performed in singleplex. The assays were able to accurately determine NTM copy number to a theoretical limit of 10 copies/μl. When used against samples derived from human sputum and using culture results as a gold standard, the sensitivity of the assay for M. abscessus was found to be 0.87 and 0.86 for MAC. The specificity of the assay for M. abscessus was 0.95 and 0.62 for MAC. The negative predictive value of the assay for M. abscessus was 0.98 and 0.95 for MAC. This resulted in an AUC of 0.92 for M. abscessus and 0.74 for MAC. Longitudinal analysis of the lung microbiome using 16SrRNA gene sequencing showed that bacterial burden initially decreases after initiation of antibiotic therapy but begins to return to normal levels over several months of antibiotic therapy. This effect is mirrored by changes in alpha diversity. The decrease in bacterial burden and loss of alpha diversity was found to be secondary to significant changes in specific genera such as Veillonella and Streptococcus. The abundance of other Proteobacteria such as Pseudomonas remain relatively constant. Conclusion: The molecular assay has shown high in vitro sensitivity and specificity for the detection and accurate quantification of the 6 most commonly pathogenic NTM species. The assays successfully identified NTM DNA from human sputum samples. A notable association between NTM copy number and the cessation of one or more antibiotics existed (i.e. when one antibiotic was stopped because of patient intolerance, NTM copy number increased, often having been unrecordable prior to this). The qPCR assays developed in this thesis provide an affordable, real time and rapid measurement of NTM burden allowing clinicians to act on problematic results sooner than currently possible. There was no significant difference between the microbiome in bronchiectasis and cystic fibrosis nor was there a significant difference between the microbiome in patients requiring treatment for NTM and those who did not. Patients receiving treatment experienced an initial decrease in bacterial burden over the first weeks of treatment followed by a gradual increase towards baseline over the next weeks to months. This change was mirrored in measures of alpha diversity. Changes in abundance and diversity were accounted for by decreases in specific bacteria whilst the abundance of other bacteria increased, occupying the microbial niche created. These bacteria (for example Pseudomonas spp) are often associated with morbidity.Open Acces

    Socio-endocrinology revisited: New tools to tackle old questions

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    Animals’ social environments impact their health and survival, but the proximate links between sociality and fitness are still not fully understood. In this thesis, I develop and apply new approaches to address an outstanding question within this sociality-fitness link: does grooming (a widely studied, positive social interaction) directly affect glucocorticoid concentrations (GCs; a group of steroid hormones indicating physiological stress) in a wild primate? To date, negative, long-term correlations between grooming and GCs have been found, but the logistical difficulties of studying proximate mechanisms in the wild leave knowledge gaps regarding the short-term, causal mechanisms that underpin this relationship. New technologies, such as collar-mounted tri-axial accelerometers, can provide the continuous behavioural data required to match grooming to non-invasive GC measures (Chapter 1). Using Chacma baboons (Papio ursinus) living on the Cape Peninsula, South Africa as a model system, I identify giving and receiving grooming using tri-axial accelerometers and supervised machine learning methods, with high overall accuracy (~80%) (Chapter 2). I then test what socio-ecological variables predict variation in faecal and urinary GCs (fGCs and uGCs) (Chapter 3). Shorter and rainy days are associated with higher fGCs and uGCs, respectively, suggesting that environmental conditions may impose stressors in the form of temporal bottlenecks. Indeed, I find that short days and days with more rain-hours are associated with reduced giving grooming (Chapter 4), and that this reduction is characterised by fewer and shorter grooming bouts. Finally, I test whether grooming predicts GCs, and find that while there is a long-term negative correlation between grooming and GCs, grooming in the short-term, in particular giving grooming, is associated with higher fGCs and uGCs (Chapter 5). I end with a discussion on how the new tools I applied have enabled me to advance our understanding of sociality and stress in primate social systems (Chapter 6)

    Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning

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    Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led to neural approaches to natural language generation (NLG). These methods combine generative language learning techniques with neural-networks based frameworks. With a wide range of applications in natural language processing, neural NLG (NNLG) is a new and fast growing field of research. In this state-of-the-art report, we investigate the recent developments and applications of NNLG in its full extent from a multidimensional view, covering critical perspectives such as multimodality, multilinguality, controllability and learning strategies. We summarize the fundamental building blocks of NNLG approaches from these aspects and provide detailed reviews of commonly used preprocessing steps and basic neural architectures. This report also focuses on the seminal applications of these NNLG models such as machine translation, description generation, automatic speech recognition, abstractive summarization, text simplification, question answering and generation, and dialogue generation. Finally, we conclude with a thorough discussion of the described frameworks by pointing out some open research directions.This work has been partially supported by the European Commission ICT COST Action “Multi-task, Multilingual, Multi-modal Language Generation” (CA18231). AE was supported by BAGEP 2021 Award of the Science Academy. EE was supported in part by TUBA GEBIP 2018 Award. BP is in in part funded by Independent Research Fund Denmark (DFF) grant 9063-00077B. IC has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 838188. EL is partly funded by Generalitat Valenciana and the Spanish Government throught projects PROMETEU/2018/089 and RTI2018-094649-B-I00, respectively. SMI is partly funded by UNIRI project uniri-drustv-18-20. GB is partly supported by the Ministry of Innovation and the National Research, Development and Innovation Office within the framework of the Hungarian Artificial Intelligence National Laboratory Programme. COT is partially funded by the Romanian Ministry of European Investments and Projects through the Competitiveness Operational Program (POC) project “HOLOTRAIN” (grant no. 29/221 ap2/07.04.2020, SMIS code: 129077) and by the German Academic Exchange Service (DAAD) through the project “AWAKEN: content-Aware and netWork-Aware faKE News mitigation” (grant no. 91809005). ESA is partially funded by the German Academic Exchange Service (DAAD) through the project “Deep-Learning Anomaly Detection for Human and Automated Users Behavior” (grant no. 91809358)

    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

    Linguistic- and Acoustic-based Automatic Dementia Detection using Deep Learning Methods

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    Dementia can affect a person's speech and language abilities, even in the early stages. Dementia is incurable, but early detection can enable treatment that can slow down and maintain mental function. Therefore, early diagnosis of dementia is of great importance. However, current dementia detection procedures in clinical practice are expensive, invasive, and sometimes inaccurate. In comparison, computational tools based on the automatic analysis of spoken language have the potential to be applied as a cheap, easy-to-use, and objective clinical assistance tool for dementia detection. In recent years, several studies have shown promise in this area. However, most studies focus heavily on the machine learning aspects and, as a consequence, often lack sufficient incorporation of clinical knowledge. Many studies also concentrate on clinically less relevant tasks such as the distinction between HC and people with AD which is relatively easy and therefore less interesting both in terms of the machine learning and the clinical application. The studies in this thesis concentrate on automatically identifying signs of neurodegenerative dementia in the early stages and distinguishing them from other clinical, diagnostic categories related to memory problems: (FMD, MCI, and HC). A key focus, when designing the proposed systems has been to better consider (and incorporate) currently used clinical knowledge and also to bear in mind how these machine-learning based systems could be translated for use in real clinical settings. Firstly, a state-of-the-art end-to-end system is constructed for extracting linguistic information from automatically transcribed spontaneous speech. The system's architecture is based on hierarchical principles thereby mimicking those used in clinical practice where information at both word-, sentence- and paragraph-level is used when extracting information to be used for diagnosis. Secondly, hand-crafted features are designed that are based on clinical knowledge of the importance of pausing and rhythm. These are successfully joined with features extracted from the end-to-end system. Thirdly, different classification tasks are explored, each set up so as to represent the types of diagnostic decision-making that is relevant in clinical practice. Finally, experiments are conducted to explore how to better deal with the known problem of confounding and overlapping symptoms on speech and language from age and cognitive decline. A multi-task system is constructed that takes age into account while predicting cognitive decline. The studies use the publicly available DementiaBank dataset as well as the IVA dataset, which has been collected by our collaborators at the Royal Hallamshire Hospital, UK. In conclusion, this thesis proposes multiple methods of using speech and language information for dementia detection with state-of-the-art deep learning technologies, confirming the automatic system's potential for dementia detection
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