1,720 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

    Get PDF

    Graduate Catalog of Studies, 2023-2024

    Get PDF

    Genomic investigation of antimicrobial resistant enterococci

    Get PDF
    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

    UMSL Bulletin 2023-2024

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

    Graduate Catalog of Studies, 2023-2024

    Get PDF

    Undergraduate Catalog of Studies, 2022-2023

    Get PDF

    2023-2024 Graduate School Catalog

    Get PDF
    You and your peers represent more than 67 countries and your shared scholarship spans 140 programs - from business administration and biomedical engineering to history, horticulture, musical performance, marine science, and more. Your ideas and interests will inform public health, create opportunities for art and innovation, contribute to the greater good, and positively impact economic development in Maine and beyond

    2017 GREAT Day Program

    Get PDF
    SUNY Geneseo’s Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp

    Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators

    Full text link
    Analog in-memory computing (AIMC) -- a promising approach for energy-efficient acceleration of deep learning workloads -- computes matrix-vector multiplications (MVMs) but only approximately, due to nonidealities that often are non-deterministic or nonlinear. This can adversely impact the achievable deep neural network (DNN) inference accuracy as compared to a conventional floating point (FP) implementation. While retraining has previously been suggested to improve robustness, prior work has explored only a few DNN topologies, using disparate and overly simplified AIMC hardware models. Here, we use hardware-aware (HWA) training to systematically examine the accuracy of AIMC for multiple common artificial intelligence (AI) workloads across multiple DNN topologies, and investigate sensitivity and robustness to a broad set of nonidealities. By introducing a new and highly realistic AIMC crossbar-model, we improve significantly on earlier retraining approaches. We show that many large-scale DNNs of various topologies, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, can in fact be successfully retrained to show iso-accuracy on AIMC. Our results further suggest that AIMC nonidealities that add noise to the inputs or outputs, not the weights, have the largest impact on DNN accuracy, and that RNNs are particularly robust to all nonidealities.Comment: 35 pages, 7 figures, 5 table

    An empirical evaluation of m-health service users’ behaviours: A case of Bangladesh

    Get PDF
    A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Mobile health (m-health) services are revolutionising healthcare in the developing world by improving accessibility, affordability, and availability. Although these services are revolutionising healthcare in various ways, there are growing concerns regarding users' service quality perceptions and overall influence on satisfaction and usage behaviours. In developing countries, access to healthcare and low healthcare costs are insufficient if users lack confidence in healthcare service quality. Bangladesh's Directorate General of Health Services (DGHS) provides the only government-sponsored m-health service available to the entire population. DGHS's m-health service, available since 2009, is yet to be evaluated in terms of users' perceptions of the quality of service and its impact on satisfaction and usage. Hence, this study developed a conceptual model for evaluating the associations between overall DGHS m-health service quality, satisfaction, and usage behaviours. This study operationalised overall m-health service quality as a higher-order construct with three dimensions- platform quality, information quality, and outcome quality, and nine corresponding subdimensions-privacy, systems availability, systems reliability, systems efficiency, responsiveness, empathy, assurance, emotional benefit, and functional benefit. Moreover, researchers in various service domains, including- healthcare, marketing, environmental protection, and information systems, evaluated and confirmed the influence of social and personal norms on satisfaction and behavioural outcomes like- intention to use. Despite this, no research has been conducted to determine whether these normative components affect m-health users' service satisfaction and usage behaviours. As a result, this study included social and personal norms along with overall service quality into the conceptual model to assess the influence of these variables on users' satisfaction and m-health service usage behaviours. Data was collected from two districts in Bangladesh- Dhaka and Rajshahi, utilising the online survey approach. A total of 417 usable questionnaires were analysed using partial least squares structural equation modelling to investigate the relationships between the constructs in Warp PLS. The study confirms that all three dimensions of service quality and their corresponding subdimensions influence users' overall perceptions of DGHS m-health service quality. Moreover, overall DGHS m-health service quality has a significant direct association with satisfaction and an indirect association with usage behaviours through satisfaction. While social norms do not influence satisfaction and usage behaviours within the DGHS m-health context, personal norms directly influence users' satisfaction and indirectly influence usage behaviours through satisfaction. Theoretically, the study contributes by framing the influence of users' overall m-health service quality perceptions, social and personal norms on their actual usage behaviours rather than the intention to use. It also extends the existing knowledge by assessing and comparing m-health users' continuous and discontinuous behaviours. Methodologically this study confirms the usefulness of partial least squares structural equational modelling to analyse a complex model including a higher order construct (i.e., overall perceived service quality). Practically, the study demonstrates the importance of users' satisfaction in addition to service quality, as service quality only affects usage behaviours through satisfaction in the current study context. Additionally, knowing that personal norms significantly influence service satisfaction motivates providers of m-health services to strive to enhance users' personal norms toward m-health service to enhance service satisfaction and usage. Overall, the study will help enhance patient outcomes and m-health service usage
    • …
    corecore