2,153 research outputs found

    MicroRNAs in Pancreatic Ductal Adenocarcinoma: New Approaches For Better Diagnosis And Therapy

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    Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive malignancies with less than an 8% 5-year survival rate, which has remained unchanged over the last 50 years. Early detection is particularly difficult due to the lack of disease-specific symptoms and reliable diagnostic biomarkers. Multimodality treatment including chemotherapy, radiotherapy (used sparingly) and surgery has become the standard of care for patients with PDAC. Carbohydrate antigen 19-9 (CA 19-9) is the most common diagnostic biomarker; however, it is not specific enough for asymptomatic patients. MicroRNAs (miRs/miRNAs) are small non-encoding RNA molecules, which have been related with PDAC progression and metastasis. In particular, miR-21, miR-221, miR-155 and miR-126 have to date been shown to be highly dysregulated in human malignancies including PDAC and are involved in numerous cancer-related mechanisms such as cell growth, differentiation, metastasis, invasion, and cell death. The aim of this thesis was to examine the mode of action of miR-21, miR-221, miR-155 and miR-126 in vitro for improved diagnosis and treatment of PDAC and specifically, investigate the role of the oncogenic miR-21 in cellular proliferation, migration, invasion, apoptosis, cell cycle arrest, senescence, protein content and mitochondrial function by using CRISPR/Cas9 knockouts. The findings provide promising new insights into the metastatic predisposition of PDAC through the evaluation of specific miR signature profiles (in vitro). Such miR signatures could prompt a pioneer precision medicine approach for individual PDAC cases and allow a more effective early diagnosis and control of PDAC, facilitating more effective treatment

    Genomic investigation of antimicrobial resistant enterococci

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    Enterococcus faecium and Enterococcus faecalis are important causes of healthcare-associated infections in immunocompromised patients. Enterococci thrive in modern healthcare settings, being able to resist killing by a range of antimicrobial agents, persist in the environment, and adapt to changing circumstances. In Scotland, rates of vancomycin resistant E. faecium (VREfm) have risen almost 150% in recent years leaving few treatment options and challenging healthcare delivery. Resistance to the last line agent linezolid has also been detected in E. faecalis. Whole genome sequencing (WGS) allows investigation of the population structure and transmission of microorganisms, and identification of antimicrobial resistance mechanisms. The aim of this thesis was to use WGS to understand the molecular epidemiology of antimicrobial resistant enterococci from human healthcare settings in Scotland. Analysis of some of the earliest identified Scottish linezolid-resistant E. faecalis showed the resistance mechanism, optrA, was present in unrelated lineages and in different genetic elements, suggesting multiple introductions from a larger reservoir. To inform transmission investigations, within-patient diversity of VREfm was explored showing ~30% of patients carried multiple lineages and identifying a within-patient diversity threshold for transmission studies. WGS was then applied to a large nosocomial outbreak of VREfm, highlighting a complex network of related variants across multiple wards. Having examined within-hospital transmission, the role of regional relationships was investigated which showed that VREfm in Scotland is driven by multiple clones transmitted within individual Health Boards with occasional spread between regions. The most common lineage in the national collection (ST203) was estimated to have been present in Scotland since around 2005, highlighting its persistence in the face of increasing infection prevention and control measures. This thesis provides a starting point for genomic surveillance of enterococci in Scotland, and a basis for interventional studies aiming to reduce the burden of enterococcal infections."This work was supported by the Chief Scientist Office (Scotland) [grant number SIRN/10]; the Wellcome Trust [grant numbers 105621/Z/14/Z, 206194]; and the BBSRC [grant number BB/S019669/1]."—Fundin

    An examination of the verbal behaviour of intergroup discrimination

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    This thesis examined relationships between psychological flexibility, psychological inflexibility, prejudicial attitudes, and dehumanization across three cross-sectional studies with an additional proposed experimental study. Psychological flexibility refers to mindful attention to the present moment, willing acceptance of private experiences, and engaging in behaviours congruent with one’s freely chosen values. Inflexibility, on the other hand, indicates a tendency to suppress unwanted thoughts and emotions, entanglement with one’s thoughts, and rigid behavioural patterns. Study 1 found limited correlations between inflexibility and sexism, racism, homonegativity, and dehumanization. Study 2 demonstrated more consistent positive associations between inflexibility and prejudice. And Study 3 controlled for right-wing authoritarianism and social dominance orientation, finding inflexibility predicted hostile sexism and racism beyond these factors. While showing some relationships, particularly with sexism and racism, psychological inflexibility did not consistently correlate with varied prejudices across studies. The proposed randomized controlled trial aims to evaluate an Acceptance and Commitment Therapy intervention to reduce sexism through enhanced psychological flexibility. Overall, findings provide mixed support for the utility of flexibility-based skills in addressing complex societal prejudices. Research should continue examining flexibility integrated with socio-cultural approaches to promote equity

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    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

    Risk, Need, and Racial Inequality: A Machine Learning Analysis of Rearrest in Juvenile Drug Treatment Courts and Traditional Juvenile Courts

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    Juvenile justice system involvement has many impacts on the lives of youth. This often includes negative outcomes for youth who receive highly punitive treatment rather than more rehabilitative approaches. One approach to reforming the juvenile justice system to be rehabilitative is the use of diversion options, such as Juvenile Drug Treatment Courts (JDTCs). JDTCs are intended to offer more personalized interventions for youth based on their risk and need factors as compared to Tradition Juvenile Court (TJC) settings. To better understand the complex interactions of tailored programming and individual factors for justice-involved youth, an integrated theoretical approach, including the Risk-Need-Responsivity framework and Disproportionate Minority Contact, was used to frame the current study. This study applied machine learning analysis techniques (random forests and logistic regression models) to a rigorous, longitudinal secondary dataset of youth in JDTCs and TJCs to determine which risk and protective factors were most important in predicting rearrest up to 1 year following court intake. The sample included 415 youth from JDTCs and TJCs in 10 jurisdictions across the US. Results revealed that both random forest and logistic regression models performed similarly for each court type as well as the combined sample, and that models were most accurate for the JDTC sample and least accurate for the TJC sample. Highly influential risk factors associated with higher likelihood of having at least one rearrest during the study period included higher scores on the family ineffectiveness scale, social risk scale, and crime and violence screener. Alternatively, highly influential protective factors associated with higher likelihood of not having any rearrests during the study period included not having an assessed risk level assigned to youth and being of Hispanic ethnicity. Race and previous juvenile justice system involvement were not important features in preliminary models and therefore were excluded from final models. Implications for future research, data-driven decision-making practices, and the ethics surrounding the use of machine learning models for juvenile justice involved youth are discussed

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Will they take this offer? A machine learning price elasticity model for predicting upselling acceptance of premium airline seating

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    Employing customer information from one of the world's largest airline companies, we develop a price elasticity model (PREM) using machine learning to identify customers likely to purchase an upgrade offer from economy to premium class and predict a customer's acceptable price range. A simulation of 64.3 million flight bookings and 14.1 million email offers over three years mirroring actual data indicates that PREM implementation results in approximately 1.12 million (7.94%) fewer non-relevant customer email messages, a predicted increase of 72,200 (37.2%) offers accepted, and an estimated $72.2 million (37.2%) of increased revenue. Our results illustrate the potential of automated pricing information and targeting marketing messages for upselling acceptance. We also identified three customer segments: (1) Never Upgrades are those who never take the upgrade offer, (2) Upgrade Lovers are those who generally upgrade, and (3) Upgrade Lover Lookalikes have no historical record but fit the profile of those that tend to upgrade. We discuss the implications for airline companies and related travel and tourism industries.© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

    Systems Analysis for Sustainable Wellbeing. 50 years of IIASA research, 40 years after the Brundtland Commission, contributing to the post-2030 Global Agenda

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    This report chronicles the half-century-long history of the International Institute for Applied Systems Analysis (IIASA), established in 1972 in Laxenburg, Austria, to address common social, economic, and environmental challenges at a time when the world was politically dominated by the Cold War. The report shows IIASA’s transition from its original raison d’ĂȘtre as a cooperative scientific venture between East and West to its position today as a global institute engaged in exploring solutions to some of the world’s most intractable problems—the interconnected problems of population, climate change, biodiversity loss, land, energy, and water use, among others. It provides a concise overview of IIASA’s key contributions to science over the last 50 years and of the advances it has made not only in analyzing existing and emerging trends but also in developing enhanced scientific tools to address them. The report also shows how IIASA is currently working with distinguished partners worldwide to establish the scientific basis for a successful transition to sustainable development. The global mandate, to achieve the 2030 Agenda, its 17 Sustainable Development Goals (SDGs), and 169 specific targets, features prominently in the institute’s work and in the report at hand: the pathways needed to achieve the SDGs have been the basis of many scientific studies by IIASA and its partners. The predominantly “bottom-up” nature of tackling the SDGs has required optimal responses to the very diverse and overlapping issues they involve, including judicious tradeoffs among the solutions that can be applied. Now, at the mid-term review point of the 2030 Agenda, this report focuses on the big picture and clarifies why, after years of scientific endeavor, the ultimate goal of this difficult global mandate should be sustainable wellbeing for all. The report is in six parts that summarize past and current IIASA research highlights and point toward future challenges and solutions: i) Systems analysis for a challenged world; ii) Population and human capital; iii) Food security, ecosystems, and biodiversity; iv) Energy, technology, and climate change; v) Global systems analysis for understanding the drivers of sustainable wellbeing; and vi) Moving into the future: Three critical policy messages. The three critical policy messages, necessary to trigger discussions about a post-2030 Agenda for Sustainable Development are: (1) Suboptimization is suboptimal: Mainstream a systems-analysis approach into policymaking at all levels. (2) Enhance individual agency: Prioritize women’s empowerment through universal female education; and (3) Strengthen collective action and governance: Global cooperation and representation for the global common
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