6,044 research outputs found

    Antimicrobial learning systems: an implementation blueprint for artificial intelligence to tackle antimicrobial resistance.

    Get PDF
    The proliferation of various forms of artificial intelligence (AI) brings many opportunities to improve health care. AI models can harness complex evolving data, inform and augment human actions, and learn from health outcomes such as morbidity and mortality. The global public health challenge of antimicrobial resistance (AMR) needs large-scale optimisation of antimicrobial use and wider infection care, which could be enabled by carefully constructed AI models. As AI models become increasingly useful and robust, health-care systems remain challenging places for their deployment. An implementation gap exists between the promise of AI models and their use in patient and population care. Here, we outline an adaptive implementation and maintenance framework for AI models to improve antimicrobial use and infection care as a learning system. The roles of AMR problem identification, law and regulation, organisational support, data processing, and AI development, assessment, maintenance, and scalability in the implementation of AMR-targeted AI models are considered

    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

    Proteomic-based stratification of intermediate-risk prostate cancer patients

    Full text link
    Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in the Gleason grade group (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or overtreatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B, and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomize prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management

    Climate Change and Critical Agrarian Studies

    Full text link
    Climate change is perhaps the greatest threat to humanity today and plays out as a cruel engine of myriad forms of injustice, violence and destruction. The effects of climate change from human-made emissions of greenhouse gases are devastating and accelerating; yet are uncertain and uneven both in terms of geography and socio-economic impacts. Emerging from the dynamics of capitalism since the industrial revolution — as well as industrialisation under state-led socialism — the consequences of climate change are especially profound for the countryside and its inhabitants. The book interrogates the narratives and strategies that frame climate change and examines the institutionalised responses in agrarian settings, highlighting what exclusions and inclusions result. It explores how different people — in relation to class and other co-constituted axes of social difference such as gender, race, ethnicity, age and occupation — are affected by climate change, as well as the climate adaptation and mitigation responses being implemented in rural areas. The book in turn explores how climate change – and the responses to it - affect processes of social differentiation, trajectories of accumulation and in turn agrarian politics. Finally, the book examines what strategies are required to confront climate change, and the underlying political-economic dynamics that cause it, reflecting on what this means for agrarian struggles across the world. The 26 chapters in this volume explore how the relationship between capitalism and climate change plays out in the rural world and, in particular, the way agrarian struggles connect with the huge challenge of climate change. Through a huge variety of case studies alongside more conceptual chapters, the book makes the often-missing connection between climate change and critical agrarian studies. The book argues that making the connection between climate and agrarian justice is crucial

    Resource-aware scheduling for 2D/3D multi-/many-core processor-memory systems

    Get PDF
    This dissertation addresses the complexities of 2D/3D multi-/many-core processor-memory systems, focusing on two key areas: enhancing timing predictability in real-time multi-core processors and optimizing performance within thermal constraints. The integration of an increasing number of transistors into compact chip designs, while boosting computational capacity, presents challenges in resource contention and thermal management. The first part of the thesis improves timing predictability. We enhance shared cache interference analysis for set-associative caches, advancing the calculation of Worst-Case Execution Time (WCET). This development enables accurate assessment of cache interference and the effectiveness of partitioned schedulers in real-world scenarios. We introduce TCPS, a novel task and cache-aware partitioned scheduler that optimizes cache partitioning based on task-specific WCET sensitivity, leading to improved schedulability and predictability. Our research explores various cache and scheduling configurations, providing insights into their performance trade-offs. The second part focuses on thermal management in 2D/3D many-core systems. Recognizing the limitations of Dynamic Voltage and Frequency Scaling (DVFS) in S-NUCA many-core processors, we propose synchronous thread migrations as a thermal management strategy. This approach culminates in the HotPotato scheduler, which balances performance and thermal safety. We also introduce 3D-TTP, a transient temperature-aware power budgeting strategy for 3D-stacked systems, reducing the need for Dynamic Thermal Management (DTM) activation. Finally, we present 3QUTM, a novel method for 3D-stacked systems that combines core DVFS and memory bank Low Power Modes with a learning algorithm, optimizing response times within thermal limits. This research contributes significantly to enhancing performance and thermal management in advanced processor-memory systems

    Comparing the Performance of Different Machine Learning Models in the Evaluation of Solder Joint Fatigue Life Under Thermal Cycling

    Get PDF
    Predicting the reliability of board-level solder joints is a challenging process for the designer because the fatigue life of solder is influenced by a large variety of design parameters and many nonlinear, coupled phenomena. Machine learning has shown promise as a way of predicting the fatigue life of board-level solder joints. In the present work, the performance of various machine learning models to predict the fatigue life of board-level solder joints is discussed. Experimental data from many different solder joint thermal fatigue tests are used to train the different machine learning models. A web-based database for storing, sharing, and uploading data related to the performance of electronics materials, the Electronics Packaging Materials Database (EPMD), has been developed and used to store and serve the training data for the present work. Data regression is performed using artificial neural networks, random forests, gradient boosting, extreme gradient boosting (XGBoost), and adaptive boosting with neural networks (AdaBoost). While previous works have studied artificial neural networks as a way to predict the fatigue life of board-level solder joints, the results in this paper suggest that machine learning techniques based on regression trees may also be useful in predicting the fatigue life of board-level solder joints. This paper also demonstrates the need for a large collection of curated data related to board-level solder joint reliability, and presents the Electronics Packaging Materials Database to meet that need

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

    Get PDF
    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (Forlì Campus) in collaboration with the Romagna Chamber of Commerce (Forlì-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices

    Multi-criteria methodology based on data science for the selection of the optimal forecast model for residential electricity consumption

    Get PDF
    There is a wide variety of techniques and models for forecasting electrical energy consumption, depending on both the type of user, the forecast horizon, and the resolution of the available data. Likewise, there are different metrics to evaluate the performance of these models. So, in this research an integrated multi-criteria methodology is proposed to select the best forecast model for residential electricity consumption, using the Analytical Hierarchical Process (AHP) to establish the weights of relative importance of the decision criteria, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to make the selection of the optimal model. The methodology is in turn framed within a data science process, through which the data is extracted, processed, and analyzed, prior to the application of the machine learning algorithms to obtain the forecast models, which will correspond to decision alternatives. The performance metrics in the evaluation phase of the models, and the performance metrics obtained from the forecast phase, are considered as the decision criteria. From the pairwise comparisons technique, it was obtained that the mean absolute percentage error (MAPE) of the prognosis phase was the criterion with the greatest weight of importance, followed by the coefficient of determination R2 and the MAPE of the evaluation phase. From the TOPSIS method, the Multiple Linear Regression model was selected as the optimal forecast model.  Existe una gran variedad de técnicas y modelos para el pronóstico del consumo de energía eléctrica, dependiendo tanto del tipo de usuario, como del horizonte de pronóstico y de la resolución de los datos disponibles. Asimismo, existen distintas métricas para evaluar el desempeño de estos modelos. Entonces, en esta investigación se propone una metodología integrada multicriterio para seleccionar el mejor modelo de pronóstico del consumo de energía eléctrica residencial, utilizando el proceso jerárquico analítico (AHP) para establecer los pesos de importancia relativa de los criterios de decisión, y la técnica para el orden de preferencia por similitud con la solución ideal  (TOPSIS) para hacer la selección del modelo óptimo. La metodología se enmarca a su vez dentro de un proceso de ciencia de datos, a través del cual se extraen, procesan y analizan los datos, previo a la aplicación de los algoritmos de aprendizaje automático para obtener los modelos de pronósticos, que se corresponderán con las alternativas de decisión. Las métricas de desempeño en la fase de evaluación de los modelos, y las métricas de desempeño obtenidas de la fase de pronóstico, son consideradas como los criterios de decisión. De la técnica de comparaciones pareadas se obtuvo que el error porcentual absoluto medio (MAPE) de la fase de pronóstico fue el criterio con mayor peso de importancia, seguido del coeficiente de determinación R2 y del MAPE de la fase de evaluación. A partir del método TOPSIS, se seleccionó el modelo de Regresión Lineal Múltiple como el modelo óptimo de pronóstico

    Analytical Models and Artificial Intelligence for Open and Partially Disaggregated Optical Networks

    Get PDF
    L'abstract è presente nell'allegato / the abstract is in the attachmen
    corecore