1,795 research outputs found
The role of nursing in multimorbidity care
Background
Multimorbidity (the co-occurrence of two or more chronic conditions in the same person) affects around one in three persons, and it is strongly associated with a range of negative outcomes including worsening physical function, increased health care use, and premature death. Due to the way healthcare is provided to people with multimorbidity, treatment can become burdensome, fragmented and inefficient. In people with palliative conditions, multimorbidity is increasingly common. Better models of care are needed.
Methods
A mixed-methods programme of research designed to inform the development of a nurse-led intervention for people with multimorbidity and palliative conditions. A mixed-methods systematic review explored nurse-led interventions for multimorbidity and their effects on outcomes. A cross-sectional study of 63,328 emergency department attenders explored the association between multimorbidity, complex multimorbidity (â„3 conditions affecting â„3 body systems), and disease-burden on healthcare use and inpatient mortality. A focussed ethnographic study of people with multimorbidity and life-limiting conditions and their carers (n=12) explored the concept of treatment burden.
Findings
Nurse-led interventions for people with multimorbidity generally focus on care coordination (i.e., case management or transitional care); patients view them positively, but they do not reliably reduce health care use or costs. Multimorbidity and complex multimorbidity were significantly associated with admission from the emergency department and reattendance within 30 and 90 days. The association was greater in those with more conditions. There was no association with inpatient mortality. People with multimorbidity and palliative conditions experienced treatment burden in a manner consistent with existing theoretical models. This thesis also noted the effect of uncertainty on the balance between capacity and workload and proposes a model of how these concepts relate to one another.
Discussion
This thesis addresses a gap in what is known about the role of nurses in providing care to the growing number of people with multimorbidity. A theory-based nurse-led intervention is proposed which prioritises managing treatment burden and uncertainty.
Conclusions
Nursing in an age of multimorbidity necessitates a perspective shift which conceptualises chronic conditions as multiple overlapping phenomena situated within an individual. The role of the nurse should be to help patients navigate the complexity of living with multiple chronic conditions
Recalibrating machine learning for social biases: demonstrating a new methodology through a case study classifying gender biases in archival documentation
This thesis proposes a recalibration of Machine Learning for social biases to minimize harms from existing approaches and practices in the field. Prioritizing quality over quantity, accuracy over efficiency, representativeness over convenience, and situated thinking over universal thinking, the thesis demonstrates an alternative approach to creating Machine Learning models. Drawing on GLAM, the Humanities, the Social Sciences, and Design, the thesis focuses on understanding and communicating biases in a specific use case. 11,888 metadata descriptions from the University of Edinburgh Heritage Collections' Archives catalog were manually annotated for gender biases and text classification models were then trained on the resulting dataset of 55,260 annotations. Evaluations of the models' performance demonstrates that annotating gender biases can be automated; however, the subjectivity of bias as a concept complicates the generalizability of any one approach.
The contributions are: (1) an interdisciplinary and participatory Bias-Aware Methodology, (2) a Taxonomy of Gendered and Gender Biased Language, (3) data annotated for gender biased language, (4) gender biased text classification models, and (5) a human-centered approach to model evaluation. The contributions have implications for Machine Learning, demonstrating how bias is inherent to all data and models; more specifically for Natural Language Processing, providing an annotation taxonomy, annotated datasets and classification models for analyzing gender biased language at scale; for the Gallery, Library, Archives, and Museum sector, offering guidance to institutions seeking to reconcile with histories of marginalizing communities through their documentation practices; and for historians, who utilize cultural heritage documentation to study and interpret the past. Through a real-world application of the Bias-Aware Methodology in a case study, the thesis illustrates the need to shift away from removing social biases and towards acknowledging them, creating data and models that surface the uncertainty and multiplicity characteristic of human societies
Digitalization and Development
This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents.
The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term.
This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Investigation of the metabolism of rare nucleotides in plants
Nucleotides are metabolites involved in primary metabolism, and specialized
metabolism and have a regulatory role in various biochemical reactions in all forms of life. While in other organisms, the nucleotide metabolome was characterized
extensively, comparatively little is known about the cellular concentrations of
nucleotides in plants. The aim of this dissertation was to investigate the nucleotide metabolome and enzymes influencing the composition and quantities of nucleotides in plants. For this purpose, a method for the analysis of nucleotides and nucleosides in plants and algae was developed (Chapter 2.1), which comprises efficient quenching of enzymatic
activity, liquid-liquid extraction and solid phase extraction employing a weak-anionexchange resin. This method allowed the analysis of the nucleotide metabolome of plants in great depth including the quantification of low abundant deoxyribonucleotides and deoxyribonucleosides. The details of the method were summarized in an article, serving as a laboratory protocol (Chapter 2.2).
Furthermore, we contributed a review article (Chapter 2.3) that summarizes the
literature about nucleotide analysis and recent technological advances with a focus on plants and factors influencing and hindering the analysis of nucleotides in plants, i.e., a complex metabolic matrix, highly stable phosphatases and physicochemical
properties of nucleotides. To analyze the sub-cellular concentrations of metabolites, a protocol for the rapid isolation of highly pure mitochondria utilizing affinity chromatography was developed (Chapter 2.4).
The method for the purification of nucleotides furthermore contributed to the
comprehensive analysis of the nucleotide metabolome in germinating seeds and in
establishing seedlings of A. thaliana, with a focus on genes involved in the synthesis of thymidilates (Chapter 2.5) and the characterization of a novel enzyme of purine nucleotide degradation, the XANTHOSINE MONOPHOSPHATE PHOSPHATASE (Chapter 2.6). Protein homology analysis comparing A. thaliana, S. cerevisiae, and H. sapiens led to the identification and characterization of an enzyme involved in the metabolite damage repair system of plants, the INOSINE TRIPHOSPHATE PYROPHOSPHATASE (Chapter 2.7). It was shown that this enzyme dephosphorylates deaminated purine nucleotide triphosphates and thus prevents their incorporation into nucleic acids. Lossof-function mutants senesce early and have a constitutively increased content of salicylic acid. Also, the source of deaminated purine nucleotides in plants was investigated and it was shown that abiotic factors contribute to nucleotide damage.Nukleotide sind Metaboliten, die am PrimÀrstoffwechsel und an spezialisierten
StoffwechselvorgÀngen beteiligt sind und eine regulierende Rolle bei verschiedenen
biochemischen Reaktionen in allen Lebensformen spielen. WĂ€hrend bei anderen
Organismen das Nukleotidmetabolom umfassend charakterisiert wurde, ist in Pflanzen
vergleichsweise wenig ĂŒber die zellulĂ€ren Konzentrationen von Nukleotiden bekannt.
Ziel dieser Dissertation war es, das Nukleotidmetabolom und die Enzyme zu
untersuchen, die die Zusammensetzung und Menge der Nukleotide in Pflanzen
beeinflussen. Zu diesem Zweck wurde eine Methode zur Analyse von Nukleotiden und
Nukleosiden in Pflanzen und Algen entwickelt (Kapitel 2.1), die ein effizientes Stoppen
enzymatischer AktivitĂ€t, eine FlĂŒssig-FlĂŒssig-Extraktion und eine
Festphasenextraktion unter Verwendung eines schwachen Ionenaustauschers
umfasst. Mit dieser Methode konnte das Nukleotidmetabolom von Pflanzen eingehend
analysiert werden, einschlieĂlich der Quantifizierung von Desoxyribonukleotiden und
Desoxyribonukleosiden mit geringer Abundanz. Die Einzelheiten der Methode wurden
in einem Artikel zusammengefasst, der als Laborprotokoll dient (Kapitel 2.2).
DarĂŒber hinaus wurde ein Ăbersichtsartikel (Kapitel 2.3) verfasst, der die Literatur
ĂŒber die Analyse von Nukleotiden und die jĂŒngsten technologischen Fortschritte
zusammenfasst. Der Schwerpunkt lag hierbei auf Pflanzen und Faktoren, die die
Analyse von Nukleotiden in Pflanzen beeinflussen oder behindern, d. h. eine komplexe
Matrix, hochstabile Phosphatasen und physikalisch-chemische Eigenschaften von
Nukleotiden.
Um die subzellulÀren Konzentrationen von Metaboliten zu analysieren, wurde ein
Protokoll fĂŒr die schnelle Isolierung hochreiner Mitochondrien unter Verwendung einer
AffinitÀtschromatographie entwickelt (Kapitel 2.4).
Die Methode zur Analyse von Nukleotiden trug auĂerdem zu einer umfassenden
Analyse des Nukleotidmetaboloms in keimenden Samen und in sich etablierenden
Keimlingen von A. thaliana bei, wobei der Schwerpunkt auf Genen lag, die an der
Synthese von Thymidilaten beteiligt sind (Kapitel 2.5), sowie zu der Charakterisierung
eines neuen Enzyms des Purinnukleotidabbaus, der XANTHOSINE
MONOPHOSPHATE PHOSPHATASE (Kapitel 2.6). Eine Proteinhomologieanalyse, die A. thaliana, S. cerevisiae und H. sapiens
miteinander verglich fĂŒhrte zur Identifizierung und Charakterisierung eines Enzyms,
das an der Reparatur von geschÀdigten Metaboliten in Pflanzen beteiligt ist, der
INOSINE TRIPHOSPHATE PYROPHOSPHATASE (Kapitel 2.7). Es konnte gezeigt
werden, dass dieses Enzym desaminierte Purinnukleotidtriphosphate
dephosphoryliert und so deren Einbau in NukleinsÀuren verhindert.
Funktionsverlustmutanten altern frĂŒh und weisen einen konstitutiv erhöhten Gehalt an SalicylsĂ€ure auf. AuĂerdem wurde die Quelle der desaminierten Purinnukleotide in Pflanzen untersucht, und es wurde gezeigt, dass abiotische Faktoren zur
NukleotidschÀdigung beitragen
Learning and Control of Dynamical Systems
Despite the remarkable success of machine learning in various domains in recent years, our understanding of its fundamental limitations remains incomplete. This knowledge gap poses a grand challenge when deploying machine learning methods in critical decision-making tasks, where incorrect decisions can have catastrophic consequences. To effectively utilize these learning-based methods in such contexts, it is crucial to explicitly characterize their performance. Over the years, significant research efforts have been dedicated to learning and control of dynamical systems where the underlying dynamics are unknown or only partially known a priori, and must be inferred from collected data. However, much of these classical results have focused on asymptotic guarantees, providing limited insights into the amount of data required to achieve desired control performance while satisfying operational constraints such as safety and stability, especially in the presence of statistical noise.
In this thesis, we study the statistical complexity of learning and control of unknown dynamical systems. By utilizing recent advances in statistical learning theory, high-dimensional statistics, and control theoretic tools, we aim to establish a fundamental understanding of the number of samples required to achieve desired (i) accuracy in learning the unknown dynamics, (ii) performance in the control of the underlying system, and (iii) satisfaction of the operational constraints such as safety and stability. We provide finite-sample guarantees for these objectives and propose efficient learning and control algorithms that achieve the desired performance at these statistical limits in various dynamical systems. Our investigation covers a broad range of dynamical systems, starting from fully observable linear dynamical systems to partially observable linear dynamical systems, and ultimately, nonlinear systems.
We deploy our learning and control algorithms in various adaptive control tasks in real-world control systems and demonstrate their strong empirical performance along with their learning, robustness, and stability guarantees. In particular, we implement one of our proposed methods, Fourier Adaptive Learning and Control (FALCON), on an experimental aerodynamic testbed under extreme turbulent flow dynamics in a wind tunnel. The results show that FALCON achieves state-of-the-art stabilization performance and consistently outperforms conventional and other learning-based methods by at least 37%, despite using 8 times less data. The superior performance of FALCON arises from its physically and theoretically accurate modeling of the underlying nonlinear turbulent dynamics, which yields rigorous finite-sample learning and performance guarantees. These findings underscore the importance of characterizing the statistical complexity of learning and control of unknown dynamical systems.</p
Talking about personal recovery in bipolar disorder: Integrating health research, natural language processing, and corpus linguistics to analyse peer online support forum posts
Background: Personal recovery, âliving a satisfying, hopeful and contributing lifeeven with the limitations caused by the illnessâ (Anthony, 1993) is of particular value in bipolar disorder where symptoms often persist despite treatment. So far, personal recovery has only been studied in researcher-constructed environments (interviews, focus groups). Support forum posts can serve as a complementary naturalistic data source. Objective: The overarching aim of this thesis was to study personal recovery experiences that people living with bipolar disorder have shared in online support forums through integrating health research, NLP, and corpus linguistics in a mixed methods approach within a pragmatic research paradigm, while considering ethical issues and involving people with lived experience. Methods: This mixed-methods study analysed: 1) previous qualitative evidence on personal recovery in bipolar disorder from interviews and focus groups 2) who self-reports a bipolar disorder diagnosis on the online discussion platform Reddit 3) the relationship of mood and posting in mental health-specific Reddit forums (subreddits) 4) discussions of personal recovery in bipolar disorder subreddits. Results: A systematic review of qualitative evidence resulted in the first framework for personal recovery in bipolar disorder, POETIC (Purpose & meaning, Optimism & hope, Empowerment, Tensions, Identity, Connectedness). Mainly young or middle-aged US-based adults self-report a bipolar disorder diagnosis on Reddit. Of these, those experiencing more intense emotions appear to be more likely to post in mental health support subreddits. Their personal recovery-related discussions in bipolar disorder subreddits primarily focussed on three domains: Purpose & meaning (particularly reproductive decisions, work), Connectedness (romantic relationships, social support), Empowerment (self-management, personal responsibility). Support forum data highlighted personal recovery issues that exclusively or more frequently came up online compared to previous evidence from interviews and focus groups. Conclusion: This project is the first to analyse non-reactive data on personal recovery in bipolar disorder. Indicating the key areas that people focus on in personal recovery when posting freely and the language they use provides a helpful starting point for formal and informal carers to understand the concerns of people diagnosed with bipolar disorder and to consider how best to offer support
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