1,110 research outputs found

    Libro de Abstracts | VIII Jornadas de Investigación y Doctorado: “Ética en la Investigación Científica”

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    El objetivo de estas Jornadas es promover el intercambio científico entre estudiantes de doctorado, fomentando la participación, el debate y la discusión, de aspectos científicos tan importantes como la ética de la investigación. Para poner en valor el papel de los doctores en la sociedad, no podemos pasar por alto las competencias transversales que estos deben adquirir en su formación como doctores. Si bien la ética es algo fundamental en todas las facetas de la vida, en el caso de los investigadores cobra especial relevancia, ya que son generadores de conocimiento sobre el que se asentarán futuros desarrollos y políticas de interés para toda la sociedad. Por lo tanto, con el fin de incrementar la proyección social de las investigaciones llevadas a cabo y la proyección profesional de los doctores, es importante incidir en su formación ética. La base de la investigación académica está construida sobre la confianza. Los investigadores confían en que los resultados informados por otros son veraces. La sociedad confía en que los resultados de la investigación reflejan un intento honesto por parte de los científicos de describir el mundo de forma precisa. Pero esta confianza sólo perdurará si la comunidad científica transmite los valores asociados a la conducta de la ética de investigación. Por este motivo, la Universidad juega un papel muy importante en la formación de los doctores en cuestiones éticas que son inherentes al método científico y a la generación de conocimiento. Dentro de las universidades, las Escuelas Internacionales de Doctorado, con nuestros recursos, aptitudes y espacio de influencia, nos convertimos en actores clave para promover actitudes éticas entre los doctorandos, y estas Jornadas son una oportunidad muy valiosa para tratar este tema. Las ramas de conocimiento que se incluyen para estas Jornadas son las derivadas de los programas de doctorado de la EIDUCAM: -Ciencias de la Salud -Tecnologías de la Computación e Ingeniería Ambiental -Ciencias Sociales -Ciencias del DeporteActividad Física y DeporteAdministración y Dirección de EmpresasAgricultura y VeterinariaArte y HumanidadesCiencias AmbientalesCiencias de la AlimentaciónCiencias de la ComunicaciónCiencias ReligiosasDerechoEducaciónEnfermeríaFarmaciaIdiomasIngeniería, Industria y ConstrucciónMedicinaOdontologíaPodologíaPsicologíaTerapia y RehabilitaciónTurism

    SAF-IS: a Spatial Annotation Free Framework for Instance Segmentation of Surgical Tools

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    Instance segmentation of surgical instruments is a long-standing research problem, crucial for the development of many applications for computer-assisted surgery. This problem is commonly tackled via fully-supervised training of deep learning models, requiring expensive pixel-level annotations to train. In this work, we develop a framework for instance segmentation not relying on spatial annotations for training. Instead, our solution only requires binary tool masks, obtainable using recent unsupervised approaches, and binary tool presence labels, freely obtainable in robot-assisted surgery. Based on the binary mask information, our solution learns to extract individual tool instances from single frames, and to encode each instance into a compact vector representation, capturing its semantic features. Such representations guide the automatic selection of a tiny number of instances (8 only in our experiments), displayed to a human operator for tool-type labelling. The gathered information is finally used to match each training instance with a binary tool presence label, providing an effective supervision signal to train a tool instance classifier. We validate our framework on the EndoVis 2017 and 2018 segmentation datasets. We provide results using binary masks obtained either by manual annotation or as predictions of an unsupervised binary segmentation model. The latter solution yields an instance segmentation approach completely free from spatial annotations, outperforming several state-of-the-art fully-supervised segmentation approaches

    AI: Limits and Prospects of Artificial Intelligence

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    The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence

    Gratitude in Healthcare an interdisciplinary inquiry

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    The expression and reception of gratitude is a significant dimension of interpersonal communication in care-giving relationships. Although there is a growing body of evidence that practising gratitude has health and wellbeing benefits for the giver and receiver, gratitude as a social emotion made in interaction has received comparatively little research attention. To address this gap, this thesis draws on a portfolio of qualitative methods to explore the ways in which gratitude is constituted in care provision in personal, professional, and public discourse. This research is informed by a discursive psychology approach in which gratitude is analysed, not as a morally virtuous character trait, but as a purposeful, performative social action that is mutually co-constructed in interaction.I investigate gratitude through studies that approach it on a meta, meso, macro, and micro level. Key intellectual traditions that underpin research literature on gratitude in healthcare are explored through a metanarrative review. Six underlying metanarratives were identified: social capital; gifts; care ethics; benefits of gratitude; staff wellbeing; and gratitude as an indicator of quality of care. At the meso (institutional) level, a narrative analysis of an archive of letters between patients treated for tuberculosis and hospital almoners positions gratitude as participating in a Maussian gift-exchange ritual in which communal ties are created and consolidated.At the macro (societal) level, a discursive analysis of tweets of gratitude to the National Health Service at the outset of the Covid-19 pandemic shows that attitudes to gratitude were dynamic in response to events, with growing unease about deflecting attention from risk reduction for those working in the health and social care sectors. A follow-up analysis of the clap-for-carers movement implicates gratitude in embodied, symbolic, and imagined performances in debates about care justice. At the micro (interpersonal) level, an analysis of gratitude encounters broadcast in the BBC documentary series, Hospital, uses pragmatics and conversation analysis to argue that gratitude is an emotion made in talk, with the uptake of gratitude opportunities influencing the course of conversational sequencing. The findings challenge the oftenmade distinction between task-oriented and relational conversation in healthcare.Moral economics are paradigmatic in the philosophical conceptualisation of gratitude. My research shows that, although balance-sheet reciprocity characterised the institutional culture of the voluntary hospital, it is hardly ever a feature ofinterpersonal gratitude encounters. Instead, gratitude is accomplished as shared moments of humanity through negotiated encounters infused with affect. Gratitude should never be instrumentalised as compensating for unsafe, inadequatelyrenumerated work. Neither should its potential to enhance healthcare encounters be underestimated. Attention to gratitude can participate in culture change by affirming modes of acting, emoting, relating, expressing, and connecting that intersect with care justice.This thesis speaks to gratitude as a culturally salient indicator of what people express as worthy of appreciation. It calls for these expressions to be more closely attended to, not only as useful feedback that can inform change, but also because gratitude is a resource on which we can draw to enhance and enrich healthcare as a communal, collaborative, cooperative endeavour

    30th European Congress on Obesity (ECO 2023)

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    This is the abstract book of 30th European Congress on Obesity (ECO 2023

    Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives

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    Deep learning has demonstrated remarkable performance across various tasks in medical imaging. However, these approaches primarily focus on supervised learning, assuming that the training and testing data are drawn from the same distribution. Unfortunately, this assumption may not always hold true in practice. To address these issues, unsupervised domain adaptation (UDA) techniques have been developed to transfer knowledge from a labeled domain to a related but unlabeled domain. In recent years, significant advancements have been made in UDA, resulting in a wide range of methodologies, including feature alignment, image translation, self-supervision, and disentangled representation methods, among others. In this paper, we provide a comprehensive literature review of recent deep UDA approaches in medical imaging from a technical perspective. Specifically, we categorize current UDA research in medical imaging into six groups and further divide them into finer subcategories based on the different tasks they perform. We also discuss the respective datasets used in the studies to assess the divergence between the different domains. Finally, we discuss emerging areas and provide insights and discussions on future research directions to conclude this survey.Comment: Under Revie

    Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

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    Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

    The Host-Microbiota Axis in Chronic Wound Healing

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    Chronic, non-healing skin wounds represent a substantial area of unmet clinical need, leading to debilitating morbidity and mortality in affected individuals. Due to their high prevalence and recurrence, chronic wounds pose a significant economic burden. Wound infection is a major component of healing pathology, with up to 70% of wound-associated lower limb amputations preceded by infection. Despite this, the wound microbiome remains poorly understood. Studies outlined in this thesis aimed to characterise the wound microbiome and explore the complex interactions that occur in the wound environment. Wound samples were analysed using a novel long-read nanopore sequencing-based approach that delivers quantitative species-level taxonomic identification. Clinical wound specimens were collected at both the point of lower-extremity amputation and via a pilot clinical trial evaluating extracorporeal shockwave therapy (ESWT) for wound healing. Combining microbial community composition, host tissue transcriptional (RNAseq) profiling, with clinical parameters has provided new insight into healing pathology. Specific commensal and pathogenic organisms appear mechanistically linked to healing, eliciting unique host response signatures. Patient- and site-specific shifts in microbial abundance and communitycomposition were observed in individuals with chronic wounds versus healthy skin. Transcriptional profiling (RNAseq) of the wound tissue revealed important insight into functional elements of the host-microbe interaction. Finally, ESWT was shown to confer beneficial effects on both cellular and microbial aspects of healing. High-resolution long-read sequencing offers clinically important genomic insights, including rapid wide-spectrum pathogen identification and antimicrobial resistance profiling, which are not possible using current culture-based diagnostic approaches. Thus, data presented in this thesis provides important new insight into complex host-microbe interactions within the wound microbiome, providing new and exciting future avenues for diagnostic and therapeutic approaches to wound management
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