2,156 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Development and assessment of learning-based vessel biomarkers from CTA in ischemic stroke

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    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Development and assessment of learning-based vessel biomarkers from CTA in ischemic stroke

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    RECENT ADVANCES IN MOLECULAR MEDICINE AND TRANSLATIONAL RESEARCH

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    ABSTRACT BOO

    Differences in well-being:the biological and environmental causes, related phenotypes, and real-time assessment

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    Well-being is a complex, and multifaceted construct that includes feeling good and functioning well. There is a growing global recognition of well-being as an important research topic and public policy goal. Well-being is related to less behavioral and emotional problems, and is associated with many positive aspects of daily life, including longevity, higher educational achievement, happier marriage, and more productivity at work. People differ in their levels of well-being, i.e., some people are in general happier or more satisfied with their lives than others. These individual differences in well-being can arise from many different factors, including biological (genetic) influences and environmental influences. To enhance the development of future mental health prevention and intervention strategies to increase well-being, more knowledge about these determinants and factors underlying well-being is needed. In this dissertation, I aimed to increase the understanding of the etiology in a series of studies using different methods, including systematic reviews, meta-analyses, twin designs, and molecular genetic designs. In part I, we brought together all published studies on the neural and physiological factors underlying well-being. This overview allowed us to critically investigate the claims made about the biology involved in well-being. The number of studies on the neural and physiological factors underlying well-being is increasing and the results point towards potential correlates of well-being. However, samples are often still small, and studies focus mostly on a single biomarker. Therefore, more well-powered, data-driven, and integrative studies across biological categories are needed to better understand the neural and physiological pathways that play a role in well-being. In part II, we investigated the overlap between well-being and a range of other phenotypes to learn more about the etiology of well-being. We report a large overlap with phenotypes including optimism, resilience, and depressive symptoms. Furthermore, when removing the genetic overlap between well-being and depressive symptoms, we showed that well-being has unique genetic associations with a range of phenotypes, independently from depressive symptoms. These results can be helpful in designing more effective interventions to increase well-being, taking into account the overlap and possible causality with other phenotypes. In part III, we used the extreme environmental change during the COVID-19 pandemic to investigate individual differences in the effects of such environmental changes on well-being. On average, we found a negative effect of the pandemic on different aspects of well-being, especially further into the pandemic. Whereas most previous studies only looked at this average negative effect of the pandemic on well-being, we focused on the individual differences as well. We reported large individual differences in the effects of the pandemic on well-being in both chapters. This indicates that one-size-fits-all preventions or interventions to maintain or increase well-being during the pandemic or lockdowns will not be successful for the whole population. Further research is needed for the identification of protective factors and resilience mechanisms to prevent further inequality during extreme environmental situations. In part IV, we looked at the real-time assessment of well-being, investigating the feasibility and results of previous studies. The real-time assessment of well-being, related variables, and the environment can lead to new insights about well-being, i.e., results that we cannot capture with traditional survey research. The real-time assessment of well-being is therefore a promising area for future research to unravel the dynamic nature of well-being fluctuations and the interaction with the environment in daily life. Integrating all results in this dissertation confirmed that well-being is a complex human trait that is influenced by many interrelated and interacting factors. Future directions to understand individual differences in well-being will be a data-driven approach to investigate the complex interplay of neural, physiological, genetic, and environmental factors in well-being

    Evaluation of mixed microalgae species biorefinery of Desmodesmus sp. And Scenedesmus sp. For bioproducts synthesis

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    Microalgae is known to produce numerous bioactive compounds for instance proteins, fatty acid, polysaccharides, enzymes, sterols, and antioxidants. Due to their valuable biochemical composition, microalgae are regarded as a very intriguing source to produce novel food products and can be utilised to improve the nutritional content of traditional foods. Additionally, microalgae are used as animal feed and additives in the cosmetics, pharmaceutical as well as nutraceutical industries. As compared to other terrestrial plants and other microorganisms, microalgae possess few advantages: (1) rapid growth rate; (2) able to grow in non-arable land and harsh cultivation conditions; (3) low nutritional requirements; (4) high productivity; and (5) reduce emission of carbon dioxide. Despite the large number of microalgae species found in nature, only a few species are identified and commercialized such as Chlorella sp., Spirulina sp. Haematococcus pluvialis, Nannochloropsis sp. and Chlamydomonas reinhardtii, which is one of the major obstacles preventing the full utilisation of microalgae-based technology. This thesis provides information on the overall composition of mixed microalgae species, Desmodesmus sp. and Scenedesmus sp., for instance protein, carbohydrate, lipid, antioxidants, and pigment. This thesis firstly introduces the application of triphasic partitioning (TPP) in the extraction and partitioning of the biomolecules from the microalgae. The latest advancement of technology has evolved from a liquid biphasic flotation (LBF) to TPP. T-butanol and ammonium sulphate are used in TPP to precipitate desired biomolecules from the aqueous solutions with the formation of three layer. TPP is a simple, time- and cost- efficient, as well as scalable process that does not require toxic organic solvents. Lipase is abundantly produced by microbes, bacteria, fungi, yeast, mammals, and plants. Lipase is widely used in the oleochemical, detergent, dairy, leather, cosmetics, paper, cosmetics, and nutraceutical industries. Therefore, this thesis also discusses the possibility of identifying and extracting enzyme lipase from the microalgae using LBF. Several parameters (volume and concentration of solvents, weight of biomass, flotation kinetics and solvent types, etc.) have been investigated to optimize the lipase extraction from LBF. Chlorophyll is the main pigment present in the microalgae. Thus, this work proposes the digital imaging approach to determine the chlorophyll concentration in the microalgae rapidly because the chlorophyll content has a significant impact on microalgae physiological health status as well as identifies the chlorophyll concentration in the production of by-products. Lastly, microalgae oil can be used as the feedstock for biodiesel as well as nutraceutical, pharmaceutical, and health-care products. The challenge in the lipid extraction is the co-extraction of chlorophyll into the oil, which can have serious consequences for downstream processing. Therefore, the removal of the chlorophyll from the microalgae using activated clay or sodium chlorite in the pre-treatment procedure are examined. The research achievements in these works and future opportunities are highlighted in the last chapter of the thesis

    Undergraduate Catalog of Studies, 2022-2023

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