Compression test assembly

A compression test assembly is described which prevents buckling of small diameter rigid specimens undergoing compression testing and permits attachment of extensometers for strain measurements. The test specimen is automatically aligned and laterally supported when compressive force is applied to the end caps and transmitted to the test specimen during testing

Unsupervised machine learning of high dimensional data for patient stratification

The development mechanisms of numerous complex, rare diseases are largely unknown to scientists partly due to their multifaceted heterogeneity. Stratifying patients is becoming a very important objective as we further research that inherent heterogeneity which can be utilised towards personalised medicine. However, considerable difficulties slow down accurate patient stratification mainly represented by outdated clinical criteria, weak associations or simple symptom categories. Fortunately, immense steps have been taken towards multiple omic data generation and utilisation aiming to produce new insights as in exploratory machine learning which showed the potential to identify the source of disease mechanisms from patient subgroups. This work describes the development of a modular clustering toolkit, named Omada, designed to assist researchers in exploring disease heterogeneity without extensive expertise in the machine learning field. Subsequently, it assesses Omada’s capabilities and validity by testing the toolkit on multiple data modalities from pulmonary hypertension (PH) patients. I first demonstrate the toolkit’s ability to create biologically meaningful subgroups based on whole blood RNA-seq data from H/IPAH patients in the manuscript “Biological heterogeneity in idiopathic pulmonary arterial hypertension identified through unsupervised transcriptomic profiling of whole blood”. Our work on the manuscript titled “Diagnostic miRNA signatures for treatable forms of pulmonary hypertension highlight challenges with clinical classification” aimed to apply the same clustering approach on a PH microRNA dataset as a first step in forming microRNA diagnostic signatures by recognising the potential of microRNA expression in identifying diverse disease sub-populations irrespectively of pre-existing PH classes. The toolkit’s effectiveness on metabolite data was also tested. Lastly, a longitudinal clustering approach was explored on activity readouts from wearables on COVID-19 patients as part of our manuscript “Unsupervised machine learning identifies and associates trajectory patterns of COVID-19 symptoms and physical activity measured via a smart watch”. Two clusters of high and low activity trajectories were generated and associated with symptom classes showing a weak but interesting relationship between the two. In summary, this thesis is examining the potential of patient stratification based on several data types from patients that represent a new, unseen picture of disease mechanisms. The tools presented provide important indications of distinct patient groups and could generate the insights needed for further targeted research and clinical associations that can help towards understanding rare, complex diseases

Knowledge co-creation in participatory policy and practice: Building community through data-driven direct democracy

Engaging citizens with digital technology to co-create data, information and knowledge has widely become an important strategy for informing the policy response to COVID-19 and the ‘infodemic’ of misinformation in cyberspace. This move towards digital citizen participation aligns well with the United Nations’ agenda to encourage the use of digital tools to enable data-driven, direct democracy. From data capture to information generation, and knowledge co-creation, every stage of the data lifecycle bears important considerations to inform policy and practice. Drawing on evidence of participatory policy and practice during COVID-19, we outline a framework for citizen ‘e-participation’ in knowledge co-creation across every stage of the policy cycle. We explore how coupling the generation of information with that of social capital can provide opportunities to collectively build trust in institutions, accelerate recovery and facilitate the ‘e-society’. We outline the key aspects of realising this vision of data-driven direct democracy by discussing several examples. Sustaining participatory knowledge co-creation beyond COVID-19 requires that local organisations and institutions (e.g. academia, health and welfare, government, business) incorporate adaptive learning mechanisms into their operational and governance structures, their integrated service models, as well as employing emerging social innovations

Avatar Therapy for people with schizophrenia or related disorders

BackgroundMany people with schizophrenia do not achieve satisfactory improvements in their mental state, particularly the symptom of hearing voices (hallucinations), with medical treatment.ObjectivesTo examine the effects of Avatar Therapy for people with schizophrenia or related disorders.Search methodsIn December 2016, November 2018 and April 2019, the Cochrane Schizophrenia Group's Study‐Based Register of Trials (including registries of clinical trials) was searched, review authors checked references of all identified relevant reports to identify more studies and contacted authors of trials for additional information.Selection criteriaAll randomised clinical trials focusing on Avatar Therapy for people with schizophrenia or related disorders.Data collection and analysisWe extracted data independently. For binary outcomes, we calculated risk ratio (RR) and 95% confidence intervals (CI), on an intention‐to‐treat basis. For continuous data, we estimated the mean difference (MD) between groups and 95% CIs. We employed a fixed‐effect model for analyses. We assessed risk of bias for included studies and created 'Summary of findings' tables using GRADE. Our main outcomes of interest were clinically important change in; mental state, insight, global state, quality of life and functioning as well as adverse effects and leaving the study early.Main resultsWe found 14 potentially relevant references for three studies (participants = 195) comparing Avatar Therapy with two other interventions; treatment as usual or supportive counselling. Both Avatar Therapy and supportive counselling were given in addition (add‐on) to the participants' normal care. All of the studies had high risk of bias across one or more domains for methodology and, for other risks of bias, authors from one of the studies were involved in the development of the avatar systems on trial and in another trial, authors had patents on the avatar system pending.1. Avatar Therapy compared with treatment as usualWhen Avatar Therapy was compared with treatment as usual average endpoint Positive and Negative Syndrome Scale – Positive (PANSS‐P) scores were not different between treatment groups (MD –1.93, 95% CI –5.10 to 1.24; studies = 1, participants = 19; very low‐certainty evidence). A measure of insight (Revised Beliefs about Voices Questionnaire; BAVQ‐R) showed an effect in favour of Avatar Therapy (MD –5.97, 95% CI –10.98 to –0.96; studies = 1, participants = 19; very low‐certainty evidence). No one was rehospitalised in either group in the short term (risk difference (RD) 0.00, 95% CI –0.20 to 0.20; studies = 1, participants = 19; low‐certainty evidence). Numbers leaving the study early from each group were not clearly different – although more did leave from the Avatar Therapy group (6/14 versus 0/12; RR 11.27, 95% CI 0.70 to 181.41; studies = 1, participants = 26; low‐certainty evidence). There was no clear difference in anxiety between treatment groups (RR 5.54, 95% CI 0.34 to 89.80; studies = 1, participants = 19; low‐certainty evidence). For quality of life, average Quality of Life Enjoyment and Satisfaction Questionnaire‐Short Form (QLESQ‐SF) scores favoured Avatar Therapy (MD 9.99, 95% CI 3.89 to 16.09; studies = 1, participants = 19; very low‐certainty evidence). No study reported data for functioning.2. Avatar Therapy compared with supportive counsellingWhen Avatar Therapy was compared with supportive counselling (all short‐term), general mental state (Psychotic Symptom Rating Scale (PSYRATS)) scores favoured the Avatar Therapy group (MD –4.74, 95% CI –8.01 to –1.47; studies = 1, participants = 124; low‐certainty evidence). For insight (BAVQ‐R), there was a small effect in favour of Avatar Therapy (MD –8.39, 95% CI –14.31 to –2.47; studies = 1, participants = 124; low‐certainty evidence). Around 20% of each group left the study early (risk ratio (RR) 1.06, 95% CI 0.59 to 1.89; studies = 1, participants = 150; moderate‐certainty evidence). Analysis of quality of life scores (Manchester Short Assessment of Quality of Life (MANSA)) showed no clear difference between groups (MD 2.69, 95% CI –1.48 to 6.86; studies = 1, participants = 120; low‐certainty evidence). No data were available for rehospitalisation rates, adverse events or functioning.Authors' conclusionsOur analyses of available data shows few, if any, consistent effects of Avatar Therapy for people living with schizophrenia who experience auditory hallucinations. Where there are effects, or suggestions of effects, we are uncertain because of their risk of bias and their unclear clinical meaning. The theory behind Avatar Therapy is compelling but the practice needs testing in large, long, well‐designed, well‐reported randomised trials undertaken with help from – but not under the direction of – Avatar Therapy pioneers

Development of a lightweight cryogenic insulating system Final report, 30 Jun. 1964 - 31 May 1966

Lightweight external panel insulation systems for thermal protection of cryogenic launch vehicle propellant tank

Program for the evaluation of structural reinforced plastic materials at cryogenic temperatures, phase ii annual and fourth quarterly report, 29 jun. 1964 - 30 jun. 1965

Evaluation of procedures, test specimens, and test techniques for application to structural reinforced plastic materials at cryogenic temperature