1,032 research outputs found

    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

    The Epidemiology and Management of Kawasaki Disease in Australia

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    Kawasaki disease (KD) is a syndrome of systemic inflammation with the potential to cause life-threatening aneurysms of the coronary arteries. I sought to contribute to our understanding of this important condition, particularly with regard to Australian children. By determining the hospitalisation rate and IVIG-treatment rate I estimated the incidence of KD to be about 14 per 100,000 children under the age of 5 between 2007 and 2015. I also showed that the hospitalisation rate nationally had increased on average 3.5% annually between 1993 and 2018, with significant changes in the age distribution over that period. In collaboration with the Paediatric Active Enhanced Disease Surveillance (PAEDS) network, I undertook a large multicentre prospective surveillance study of KD in Australia. My analysis of that cohort confirmed several of the findings from the survey, such as the preference of Australian clinicians for low-dose aspirin from the time of diagnosis, and the considerable variability around how IVIG resistance is diagnosed and managed. Importantly, I observed that a significant subset of children diagnosed with, and treated for, KD do not meet the diagnostic criteria outlined in the 2017 statement by the American Heart Association. This work has contributed significantly to the understanding of KD’s epidemiology, management, and outcomes in Australia. I have shown that the incidence of the condition is increasing, and the clinical picture is changing. I identified important areas of practice variation and highlighted the need for international collaboration around agreed definitions (such as for IVIG resistance). Finally, I have played a central role in establishing an important resource for future resource: prospective surveillance of KD in Australia continues, with well over 700 cases recruited so far. It is hoped that this work will be of benefit to the researchers, clinicians, patients, and families affected by KD now, and into the future

    Air Quality Research Using Remote Sensing

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    Air pollution is a worldwide environmental hazard that poses serious consequences not only for human health and the climate but also for agriculture, ecosystems, and cultural heritage, among other factors. According to the WHO, there are 8 million premature deaths every year as a result of exposure to ambient air pollution. In addition, more than 90% of the world’s population live in areas where the air quality is poor, exceeding the recommended limits. On the other hand, air pollution and the climate co-influence one another through complex physicochemical interactions in the atmosphere that alter the Earth’s energy balance and have implications for climate change and the air quality. It is important to measure specific atmospheric parameters and pollutant compound concentrations, monitor their variations, and analyze different scenarios with the aim of assessing the air pollution levels and developing early warning and forecast systems as a means of improving the air quality and safeguarding public health. Such measures can also form part of efforts to achieve a reduction in the number of air pollution casualties and mitigate climate change phenomena. This book contains contributions focusing on remote sensing techniques for evaluating air quality, including the use of in situ data, modeling approaches, and the synthesis of different instrumentations and techniques. The papers published in this book highlight the importance and relevance of air quality studies and the potential of remote sensing, particularly that conducted from Earth observation platforms, to shed light on this topic

    Irish Ocean Climate and Ecosystem Status Report

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    Summary report for Irish Ocean Climate & Ecosystem Status Report also published here. This Irish Ocean Climate & Ecosystem Status Summary for Policymakers brings together the latest evidence of ocean change in Irish waters. The report is intended to summarise the current trends in atmospheric patterns, ocean warming, sea level rise, ocean acidification, plankton and fish distributions and abundance, and seabird population trends. The report represents a collaboration between marine researchers within the Marine Institute and others based in Ireland’s higher education institutes and public bodies. It includes authors from Met Éireann, Maynooth University, the University of Galway, the Atlantic Technological University, National Parks and Wildlife, Birdwatch Ireland, Trinity College Dublin, University College Dublin, Inland Fisheries Ireland, The National Water Forum, the Environmental Protection Agency, and the Dundalk Institute of Technology.This report is intended to summarise the current trends in Ireland’s ocean climate. Use has been made of archived marine data held by a range of organisations to elucidate some of the key trends observed in phenomena such as atmospheric changes, ocean warming, sea level rise, acidification, plankton and fish distributions and abundance, and seabirds. The report aims to summarise the key findings and recommendations in each of these areas as a guide to climate adaptation policy and for the public. It builds on the previous Ocean Climate & Ecosystem Status Report published in 2010. The report examines the recently published literature in each of the topic areas and combines this in many cases with analysis of new data sets including long-term time series to identify trends in essential ocean variables in Irish waters. In some cases, model projections of the likely future state of the atmosphere and ocean are presented under different climate emission scenarios.Marine Institut

    Bayesian spatio-temporal model with INLA for dengue fever risk prediction in Costa Rica

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    Due to the rapid geographic spread of the Aedes mosquito and the increase in dengue incidence, dengue fever has been an increasing concern for public health authorities in tropical and subtropical countries worldwide. Significant challenges such as climate change, the burden on health systems, and the rise of insecticide resistance highlight the need to introduce new and cost-effective tools for developing public health interventions. Various and locally adapted statistical methods for developing climate-based early warning systems have increasingly been an area of interest and research worldwide. Costa Rica, a country with micro-climates and endemic circulation of the dengue virus (DENV) since 1993, provides ideal conditions for developing projection models with the potential to help guide public health efforts and interventions to control and monitor future dengue outbreaks

    Explainable Contextual Anomaly Detection using Quantile Regression Forests

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    Traditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally. In contrast, contextual anomaly detection methods aim to detect objects that deviate from other objects within a context of similar objects by dividing the features into contextual features and behavioral features. In this paper, we develop connections between dependency-based traditional anomaly detection methods and contextual anomaly detection methods. Based on resulting insights, we propose a novel approach to inherently interpretable contextual anomaly detection that uses Quantile Regression Forests to model dependencies between features. Extensive experiments on various synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art anomaly detection methods in identifying contextual anomalies in terms of accuracy and interpretability.Comment: Manuscript submitted to Data Mining and Knowledge Discovery in October 2022 for possible publication. This is the revised version submitted in April 202

    Environmental variables and machine learning models to predict cetacean abundance in the Central-eastern Mediterranean Sea

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    : Although the Mediterranean Sea is a crucial hotspot in marine biodiversity, it has been threatened by numerous anthropogenic pressures. As flagship species, Cetaceans are exposed to those anthropogenic impacts and global changes. Assessing their conservation status becomes strategic to set effective management plans. The aim of this paper is to understand the habitat requirements of cetaceans, exploiting the advantages of a machine-learning framework. To this end, 28 physical and biogeochemical variables were identified as environmental predictors related to the abundance of three odontocete species in the Northern Ionian Sea (Central-eastern Mediterranean Sea). In fact, habitat models were built using sighting data collected for striped dolphins Stenella coeruleoalba, common bottlenose dolphins Tursiops truncatus, and Risso's dolphins Grampus griseus between July 2009 and October 2021. Random Forest was a suitable machine learning algorithm for the cetacean abundance estimation. Nitrate, phytoplankton carbon biomass, temperature, and salinity were the most common influential predictors, followed by latitude, 3D-chlorophyll and density. The habitat models proposed here were validated using sighting data acquired during 2022 in the study area, confirming the good performance of the strategy. This study provides valuable information to support management decisions and conservation measures in the EU marine spatial planning context

    Multi-trophic Interactions and Long-term Volunteer Collected Data: Networks of plant-caterpillar-parasitoid interactions across time, space, and a changing climate

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    The preservation of ecological complexity is an important goal for ecologists as communities respond to global change. Inherent to these efforts is the quantification and evaluation of the multiple dimensions of biodiversity, including well studied metrics of taxonomic, phylogenetic, and functional diversity. Studies on multi-trophic systems have primarily focused on taxonomic diversity, yet recent efforts have highlighted the importance of examining an underutilized biodiversity metric: interaction diversity, or the richness and abundance of the unique links connecting species. My dissertation research contributes to understanding spatial and temporal variation in the diversity of plant-caterpillar-parasitoid interactions. A central theme of my dissertation research is the use of long-term citizen science data from sites across the Americas to understand how interaction diversity changes across latitudinal, climate, disturbance, and seasonal gradients. My research in tropical forests documented the impacts of climate change. I found increases in extreme precipitation events caused reductions in interaction and species diversity with associated losses in an important ecosystem function: Biological control of herbivores by their natural enemies. In a temperate fire-adapted forest, I provided evidence for the scale-dependent nature of interaction diversity and its implications for how diversity is maintained in frequently disturbed systems. To understand spatial and temporal variation in interactions, I evaluated patterns in the beta-diversity of interactions and its components. Using this methodology, I found evidence of latitudinal patterns in the turnover of interactions, providing support that interactions are more variable in tropical than temperate regions. In the Brazilian Cerrado and Yucatan Peninsula, Mexico, I found seasonal variation in interaction diversity is primarily a consequence of seasonally constant species rewiring their interactions rather than seasonal differences in species composition. Finally, an important goal for ecology is to develop effective methods that increase the public's awareness and action toward biodiversity conservation. I fielded over 300 citizen scientists on research expeditions that contribute to the collection and rearing of these long-term data and administered surveys to understand the impact of different team models. Based on these surveys, multiple team models are effective for achieving diverse objectives and corporate teams are particularly valuable for sustainability partnerships. Together, this body of research provides evidence that interaction diversity uniquely contributes to broad patterns of biodiversity and ecosystem structure. Further, novel partnerships with various citizen science team models are an effective and efficient method to engage a diverse public audience interested in the preservation of biodiversity

    Validation of resistome signatures through the application of a machine learning prediction algorithm on metagenomic data

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    Dissertação de Mestrado Integrado em Medicina Veterinária, área científica de Sanidade AnimalABSTRACT- Metagenomic data has been increasingly used in antimicrobial resistance (AMR) studies, but there is still a need for accurate and reliable methods for predicting the relative attribution of AMR determinants to different animal reservoirs. AMR data availability has increased exponentially over the past few years, as has global awareness of the threat that AMR poses to public health, often known as the silent pandemic. This has led to an upsurge in interest in applying machine learning to AMR data. In this study, shot-gun sequences were used from fecal samples of pigs, broilers, turkeys, and veal calves, previously collected during national cross-sectional studies across Europe. The data used in this study corresponded to these samples and their associated relative abundance of AMR determinants. A random forest (RF) model was developed to investigate the relative attribution of AMR determinants to those different reservoirs. Additionally, a descriptive analysis was made to further investigate the 15 most important variables for the RF model. A principal component analysis (PCA) and all-subsets regression were performed to identify reservoir-specific AMR determinants. Ultimately, the reservoir-specific AMR determinants identified here were compared with the resistome signatures identified in a previous study. The results demonstrated that the RF model successfully classified resistomes into corresponding reservoir classes, with high accuracy and reliability. The RF model had more difficulty differentiating pig from veal and broiler from turkey, indicating the similarity of resistome composition between each of these two species. The analyses validated several AMR determinants as resistome signatures of specific animal reservoirs, such as tet(40) and sul2 of veal, tet(Q), mef(A) and cfxA2 of veal and pig, blaTEM-126 of broiler, and tet(A) of broiler and turkey. This study describes a reliable and accurate method for the relative attribution of AMR determinants to different animal reservoirs using metagenomic data. Such results are essential for effective surveillance and control of AMR in animal and human populationsRESUMO - Validação de resistome-signatures através da aplicação de um algoritmo de previsão de machine learning em dados metagenómicos - Dados metagenómicos têm sido cada vez mais usados em estudos de resistência aos antimicrobianos, mas ainda há uma escassez de métodos precisos e fidedignos para prever a atribuição relativa de genes de resistência a diferentes espécies animais. A disponibilidade de dados de resistência aos antimicrobianos aumentou exponencialmente nos últimos anos, assim como a consciencialização global sobre a ameaça que as resistências representam para a saúde pública, geralmente conhecida como pandemia silenciosa. Isto levou a um aumento no interesse em aplicar métodos de machine learning a esses dados. Neste estudo, sequências shot-gun foram usadas a partir de amostras fecais de porcos, frangos, perús e vitelos, recolhidas anteriormente durante estudos nacionais por toda a Europa. Os dados utilizados neste estudo corresponderam a essas amostras e os seus valores FPKM associados. Um modelo de random forest (RF) foi desenvolvido para prever a atribuição relativa de gene de resistência para essas diferentes espécies. Além disso, uma análise descritiva foi feita para investigar melhor as 15 variáveis mais importantes para o modelo de RF. Uma análise de componentes principais (PCA) e regressão all-subsets foram realizadas para identificar genes de resistência específicos de certas espécies. Por fim, esses genes específicos aqui identificados foram comparados com os resistome-signatures identificados num estudo anterior. Os nossos resultados demonstraram que o modelo classificou com sucesso as amostras em classes de espécies correspondentes, com alta precisão e confiabilidade. O modelo teve mais dificuldade em diferenciar porco de vitela, e frango de perú, indicando uma semelhança da composição do resistoma entre cada uma dessas duas espécies. Esta análise validou vários genes como resistome-signatures de animais específicos, como tet(40) e sul2 de vitelos, tet(Q), mef(A) e cfxA2 de vitelos e porcos, blaTEM-126 de frangos, e tet(A) de frangos e perús. Este estudo descreve um método confiável e preciso para a atribuição relativa de genes de resistência a diferentes reservatórios animais usando dados metagenómicos. Estes resultados são essenciais para a vigilância e controlo das resistências aos antimicrobianos em populações animais e humanasN/

    The Impact of Artificial Intelligence on Strategic and Operational Decision Making

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    openEffective decision making lies at the core of organizational success. In the era of digital transformation, businesses are increasingly adopting data-driven approaches to gain a competitive advantage. According to existing literature, Artificial Intelligence (AI) represents a significant advancement in this area, with the ability to analyze large volumes of data, identify patterns, make accurate predictions, and provide decision support to organizations. This study aims to explore the impact of AI technologies on different levels of organizational decision making. By separating these decisions into strategic and operational according to their properties, the study provides a more comprehensive understanding of the feasibility, current adoption rates, and barriers hindering AI implementation in organizational decision making
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