Wards occupy significant proportions of hospital floor areas and due to their constant use, represent a worthwhile focus of study. Single-bed wards are specifically of interest owing to the isolation aspect they bring to infection control, including airborne pathogens, but threats posed by airborne pandemics and family-involvement in hospital care means cross-infection is still a potential problem. In its natural mode, ventilation driven by combined wind and buoyancy forces can lead to energy savings and achieve thermal comfort and high air change rates through secure openings. These are advantageous for controlling indoor airborne pathogens and external air and noise pollution. However, there is lack of detailed evidence and guidance is needed to gain optimum performance from available natural ventilation systems. This research is a proof of concept investigation into the feasibility and impact of natural ventilation systems targeting airflow rates, thermal comfort, heating energy and control of pathogenic bio-aerosols in hospital wards. In particular, it provides insights into the optimal areas of vent openings which could satisfy the complex three-pronged criteria of contaminant dilution, low heating energy and acceptable thermal comfort for occupants in a naturally ventilated single bed ward. The main aim of this thesis is the structured study of four systems categorised into three groups: Simple Natural Ventilation (SNV) in which single and dual-openings are used on the same external wall; Advanced Natural Ventilation (ANV) which is an emerging concept; and finally Natural Personalised Ventilation (NPV) which is an entirely new concept borne out of the limitations of previous systems and gaps in literature. The focus of this research is in the exploratory study of the weaknesses and potentials of the four systems, based on multi-criteria performances metrics within three architecturally distinct single-bed ward designs. In contributing to the body of existing knowledge, this thesis provides a better understanding of the performances of three existing systems while presenting the new NPV system. The analysis is based on dynamic thermal modelling and computational fluid dynamics and in the case of the NPV system, salt-bath experiments for validation and visualisation of transient flows. In all cases, wards were assumed to be free of mechanical ventilation systems that might influence the natural flow of air. The thesis meets three major objectives which have resulted in the following contributions to current knowledge: An understanding of the limitations and potentials of same-side openings, especially why and how dual-openings can be useful when retrofitted into existing wards. Detailed analysis of bulk airflow, thermal comfort, heating energy and room air distribution achievable from existing SNV and ANV systems, including insights to acceptable trickle ventilation rates, which will be particular useful in meeting minimum dilution and energy requirements in winter. This also includes qualitative predictions of the airflow pattern and direction obtainable from both systems. The innovation and study of a new natural ventilation system called Natural Personalised Ventilation (NPV) which provides fresh air directly over a patient s bed, creating a mixing regime in the space and evaluation of its comfort and energy performances. A low-energy solution for airborne infection control in clinical spaces is demonstrated by achieving buoyancy-driven mixing ventilation via the NPV system, and a derivative called ceiling-based natural ventilation (CBNV) is shown. A comparative analysis of four unique natural ventilation strategies including their performance rankings for airflow rates, thermal comfort, energy consumption and contaminant dilution or removal using an existing single-bed ward design as case study. Development of design and operational recommendations for future guidelines on utilising natural ventilation in single-bed wards either for refurbishment or for proposed designs. These contributions can be extended to other clinical and non-clinical spaces which are suitable to be naturally ventilated including treatment rooms, office spaces and waiting areas. The findings signify that natural ventilation is not only feasible for ward spaces but that there is opportunity for innovation in its application through further research. Future work could focus on related aspects like: impacts of fan-assisted ventilation for a hybrid flow regime; pre-heating of supply air; integration with passive heat recovery systems as well the use of full-scale experiments to fine-tune and validate findings.

DNA is wrapped around proteins called histones, whose modification regulates numerous cellular processes. Therefore it is not surprising that mutations in the genes that modify the histones are frequently associated with human cancer. For example, mutations in SETD2, encoding the sole enzyme that catalyses histone H3 lysine 36 trimethylation (H3K36me3), occur frequently in multiple cancer types. This identifies H3K36me3 loss as an important event in cancer development, and also as a potential therapeutic target. This thesis investigates the following questions: (1) how does the loss of H3K36me3 contribute to cancer development; and (2) what therapy can be used to kill cancers that have already lost H3K36me3. To answer the first question, this thesis shows that H3K36me3 facilitates the accurate repair of DNA double-stranded breaks (DSBs) by homologous recombination (HR). H3K36me3 promotes HR by recruiting CtIP to the site of DSBs to carry out resection, allowing the binding of HR proteins (such as RPA and RAD51) to the damage sites. Thus it is proposed that error-free HR repair within H3K36me3-decorated transcriptionally active genomic regions suppresses genetic mutations which could promote tumourigenesis. To answer the second question, this thesis reveals a clinically relevant synthetic lethal interaction between H3K36me3 loss and WEE1 inhibition. WEE1 inhibition selectively kills H3K36me3-deficient cells by inhibiting DNA replication, and subsequent fork stalling results in MUS81 endonuclease-dependent DNA damage and cell death. The mechanism is found to be synergistic depletion of RRM2 (ribonucleotide reductase small subunit), the enzyme that generates deoxyribonucleotides (dNTPs). This work reveals two pathways that regulate RRM2: one involves transcriptional activation of RRM2 by H3K36me3, and the other involves RRM2 degradation regulated by Cyclin-Dependent Kinase, CDK1 (which is controlled by WEE1, CHK1 and ATR). Based on this mechanism, the synthetic lethal interaction is expanded, from between two genes, to between two pathways. Supported by in vivo experiments, the study suggests that patients with cancers that have lost H3K36me3 could benefit from treatment with the inhibitors of WEE1, CHK1 or ATR.

Augmented Reality (AR) is a recent technology that allows a seamless composition between virtual objects and the real world. This practice-based research uses the affordances of AR to design an AR textbook for collaborative learning experience. It identifies the key concepts of children s AR textbooks for the designing and evaluation of collaborative learning experiences. These concepts were used to develop a conceptual framework for the AR textbook that considers collaborative experience, learning and usability. Informed by these concepts, the research also has identified the design features which are unique to AR affordances which can be integrated in the school textbooks to develop a collaborative AR textbook for primary school children. The research follows a participatory design approach to involve the users of the AR textbook in the design process. The researcher has conducted three co-design studies involving primary school children and adults using cooperative inquiry techniques. The first study uses low-tech prototyping to find the overall direction of designing the AR textbook. After the development of the first AR textbook prototype, two formative evaluations have been conducted using cooperative inquiry critiquing, and layered elaboration techniques. Throughout these studies, a conceptual framework has been developed namely, Experience, Learn and Use (ELU) for the designing and evaluation of children s AR textbooks for collaborative learning experience. This framework is based on the adaption of Janet Read s Play, Learn, Use (PLU) model that defines children s relationships with the interactive technologies. The research proposes the ELU framework as a useful classification framework in the evaluation process, which informs the design features of the AR textbook which are related to the concepts of collaborative experience, learning and usability. The practical component of the thesis proposes a sample of an AR textbook that is integrated in the regular school curriculum. It demonstrates the design features which can be implemented in other textbooks to support collaborative learning experiences for primary school children. The documentation of the co-design process provides a practical framework for co-designing an AR textbook with children, as well as an evidence of using the ELU framework in practice. 4 This research also contributes in bridging the gap between AR and Child-Computer Interaction (CCI) communities, through the use of common CCI methods in the AR development. This research has resulted in key design principles which contribute original knowledge to the literature of the AR for children s education considering the CCI perspective. These important principles are informed by the collaborative experiences, learning and usability aspects that establish a framework for the design and evaluation of collaborative AR textbook for children. The eight identified principles by this research are, Joint Textbooks, Personalised AR Experience, Interactive AR Book, Communication-Based Learning, Rewarding AR feedback, Audio AR Textbook, Intuitive AR Markers, and Mutual AR Display. The research introduces the definition for each of the concepts and a demonstration of the related design features in the outcome of the AR textbook prototype.

This study is concerned with the pre-test counselling conversation (as regulated by policy-governing and training documents) that occurs between HIV counsellors and their clients. It attempts to explore and describe some of the assumptions underlying HIV/AIDS pre-test counselling in South Africa, and reflects on how these assumptions determine the content and process of HIV pre-test counselling. This exploration has been done by means of an analysis of a selection of official and non-official documentation on pre-test counselling. The aim of HIV/AIDS counselling in general is to support and educate infected/affected clients about the HI-virus. Pre-test counselling (the conversation that takes place before an HIV-test is administered) forms the entry level to HIV counselling service delivery in South Africa, and it is often the only opportunity a counsellor has to support and educate a client about HIV/AIDS. Policy-governing pre-test counselling and training documents on such counselling confirm the role played by counsellors. The pre-test counselling conversation is based on the assumption that education about the HI-virus will enable clients to make informed decisions about their health which will help them to live long and healthy lives once they are aware of their HIV-status. However, this educational approach does not seem to be successful, as a change in risk behaviour is often not achieved. My recognition of this situation motivated this study and its focus on the conversation that takes place between counsellors and their clients in pre-test counselling. I was curious about what is discussed during pre-test counselling and why, if we acknowledge that counselling plays a major role in infection rate prevention, risk-reducing behaviour is not being achieved. This qualitative study was based on a social constructionist paradigm and document analysis was used as a research method. This study offers an alternative approach to health education – a drive towards client-centred pre-test counselling where the client’s needs become the focus of the pre-test counselling conversation. / Dissertation (MA (Counselling Psychology))--University of Pretoria, 2007. / Psychology / unrestricted

Background: The following study focuses on the area of personalisation within streaming services and how vocabulary of playlist names and categories affect expectations and satisfactions. The wording of personalised items is important to convey that content is directly made for a user, yet there are limited studies that explore what users anticipate and if the message conveys correct information to then lead to satisfaction. Purpose: By using Spotify as the prime focus, this research aims to uncover how the vocabulary used in the categories of playlists and playlist titles impacts the user’s expectations and satisfaction with the actual playlist content. Method: The study uses a qualitative approach and semi-structured interviews as data collection. The interviews proceeded with open-ended questions to be able to gain a deeper understanding of the participants opinions and experiences. The analysis of the data is interpreted deductively through a thematic analysis which allowed for common topics, ideas and repeated meanings to be conveyed.

Knowledge Tracing aims to predict future performance of users of learning platforms based on historical data, by modeling their knowledge state. In this task, the target is a binary variable representing the correctness of the exercise, where an exercise is a word uttered by the user. Current state-of-the-art models add attention layers to autoregressive models or rely on self-attention networks. However, these models are built on publicly available datasets that lack useful information about the interactions users have with exercises. In this work, various techniques are introduced that allow for the incorporation of additional information made available in a dataset provided by Astrid Education. They consist of encoding a time dimension, modeling the skill needed for each exercise explicitly, and adjusting the length of the interaction sequence. Introducing new information to the Knowledge Tracing framework allows Astrid to craft a more personalized experience for its users; thus fulfilling the purpose and goal of the thesis. Additionally, we perform experiments to understand what aspects influence the models. Results show that modeling the skills needed to solve an exercise using an encoding strategy and reducing the length of the interaction sequence lead to improvements in terms of both accuracy and AUC. The time-encoding did not lead to better results, further experimentation is needed to include the time dimension successfully. / Mänsklig kunskap är ett försök att förutsäga användarnas framtida prestanda på lärandeplattformar baserat på historiska data, genom att modellera deras kunskaps tillstånd. I denna uppgift är målet en binär variabel som representerar överensstämmelsen av övningen. Nuvarande state-of-the-art-modeller lägger till uppmärksamhetslager på autoregressiva modeller eller förlitar sig på self-attention-nätverk. Dessa modeller bygger dock på offentligt tillgängliga databaser som saknar användbar information om de interaktioner som användare har med övningar. I detta arbete introduceras olika tekniker som gör det möjligt att inkludera ytterligare information som görs tillgänglig i en databas som tillhandahålls av Astrid Education AB. De består av att koda en tidsdimension, modellera färdigheten som krävs för varje övning explicit och justera interaktionssekvenslängden. Genom att introducera ny information i ramverket för kunskapstracing tillåter Astrid att skapa en mer personlig upplevelse för sina användare; därmed uppfyller syftet och målet med denna avhandling. Dessutom genomför vi experiment för att förstå vilka aspekter som påverkar modellerna. Resultaten visar att modellering av färdigheter med en kodningsstrategi och reducering av interaktionssekvenslängden leder till förbättringar både vad gäller noggrannhet och AUC. Tidskodningen ledde inte till bättre resultat, ytterligare experimentering krävs för att inkludera tidsdimensionen på ett framgångsrikt sätt.

The benefit of regular physical activity and exercise training for the prevention of cardiovascular and metabolic diseases is undisputed. Many molecular mechanisms mediating exercise effects have been deciphered. Personalised exercise prescription can help patients in achieving their individual greatest benefit from an exercise-based cardiovascular rehabilitation programme. Yet, we still struggle to provide truly personalised exercise prescriptions to our patients. In this position paper, we address novel basic and translational research concepts that can help us understand the principles underlying the inter-individual differences in the response to exercise, and identify early on who would most likely benefit from which exercise intervention. This includes hereditary, non-hereditary and sex-specific concepts. Recent insights have helped us to take on a more holistic view, integrating exercise-mediated molecular mechanisms with those influenced by metabolism and immunity. Unfortunately, while the outline is recognisable, many details are still lacking to turn the understanding of a concept into a roadmap ready to be used in clinical routine. This position paper therefore also investigates perspectives on how the advent of ‘big data’ and the use of animal models could help unravel interindividual responses to exercise parameters and thus influence hypothesis-building for translational research in exercisebased cardiovascular rehabilitation.

Negative consumer opinion poses a potential barrier to the application of nutrigenomic intervention. The present study has aimed to determine attitudes toward genetic testing and personalised nutrition among the European public. An omnibus opinion survey of a representative sample aged 14-55+ years (n 5967) took place in France, Italy, Great Britain, Portugal, Poland and Germany during June 2005 as part of the Lipgene project. A majority of respondents (66 %) reported that they would be willing to undergo genetic testing and 27 % to follow a personalised diet. Individuals who indicated a willingness to have a genetic test for the personalising of their diets were more likely to report a history of high blood cholesterol levels, central obesity and/or high levels of stress than those who would have a test only for general interest. Those who indicated that they would not have a genetic test were more likely to be male and less likely to report having central obesity. Individuals with a history of high blood cholesterol were less likely than those who did not to worry if intervention foods contained GM ingredients. Individuals who were aware that they had health problems associated with the metabolic syndrome appeared particularly favourable toward nutrigenomic intervention. These findings are encouraging for the future application of personalised nutrition provided that policies are put in place to address public concern about how genetic information is used and held.

The thesis provides a comprehensive overview of key issues affecting practice, policy and patients, in current efforts for colorectal cancer (CRC) disease control. The global burden of CRC is expected to increase by 60% to more than 2.2 million new cases and 1.1 million deaths by 2030. CRC incidence and mortality rates vary up to 10-fold worldwide, which is thought to reflect variation in lifestyles, especially diet. Better primary prevention, and more effective early detection, in screening and surveillance, are needed to reduce the number of patients with CRC in future1. The risk factors for CRC development include genetic, behavioural, environmental and socio-economic factors. Changes to surveillance, which offer non-invasive testing and provide primary prevention interventions represent promising opportunities to improve outcomes and personalise care in those at risk of CRC. By systematic review of the literature, I highlight the gaps in comparative effectiveness analyses of post-polypectomy surveillance. Using micro-simulation methods I assess the role of non-invasive, faecal immunochemical testing in surveillance programmes, to optimise post-polypectomy surveillance programmes, and in an accompanying sub-study, I explore the value of adding an adjunct diet and lifestyle intervention. The acceptability of such revisions is exposed to patient preference evaluation by discrete choice experiment methods. These preferences are accompanied by evidence generated from the prospective evaluation of the health literacy, numeracy, sedentary behaviour levels, body mass index (BMI) and information provision about cancer risk factors, to highlight the potential opportunities for personalisation and optimisation of surveillance. Additional analysis examines the optimisation of a screening programme facing colonoscopy constraints, highlighting the attendant potential to reduce costs and save lives within current capacity.

Information Filtering and Recommender Systems have been used and has been implemented in various ways from various entities since the dawn of the Internet, and state-of-the-art approaches rely on Machine Learning and Deep Learning in order to create accurate and personalized recommendations for users in a given context. These models require big amounts of data with a variety of features such as time, location and user data in order to find correlations and patterns that other classical models such as matrix factorization and collaborative filtering cannot. This thesis researches, implements and compares a variety of models with the primary focus of Machine Learning and Deep Learning for the task of music recommendation and do so successfully by representing the task of recommendation as a multi-class extreme classification task with 100 000 distinct labels. By comparing fourteen different experiments, all implemented models successfully learn features such as time, location, user features and previous listening history in order to create context-aware personalized music predictions, and solves the cold start problem by using user demographic information, where the best model being capable of capturing the intended label in its top 100 list of recommended items for more than 1/3 of the unseen data in an offine evaluation, when evaluating on randomly selected examples from the unseen following week. / Informationsfiltrering och rekommendationssystem har använts och implementeratspå flera olika sätt från olika enheter sedan gryningen avInternet, och moderna tillvägagångssätt beror påMaskininlärrning samtDjupinlärningför att kunna skapa precisa och personliga rekommendationerför användare i en given kontext. Dessa modeller kräver data i storamängder med en varians av kännetecken såsom tid, plats och användardataför att kunna hitta korrelationer samt mönster som klassiska modellersåsom matris faktorisering samt samverkande filtrering inte kan. Dettaexamensarbete forskar, implementerar och jämför en mängd av modellermed fokus påMaskininlärning samt Djupinlärning för musikrekommendationoch gör det med succé genom att representera rekommendationsproblemetsom ett extremt multi-klass klassifikationsproblem med 100000 unika klasser att välja utav. Genom att jämföra fjorton olika experiment,så lär alla modeller sig kännetäcken såsomtid, plats, användarkänneteckenoch lyssningshistorik för att kunna skapa kontextberoendepersonaliserade musikprediktioner, och löser kallstartsproblemet genomanvändning av användares demografiska kännetäcken, där den bästa modellenklarar av att fånga målklassen i sin rekommendationslista medlängd 100 för mer än 1/3 av det osedda datat under en offline evaluering,när slumpmässigt valda exempel från den osedda kommande veckanevalueras.