Designing novel applications for emerging multimedia technology

Current R&D in media technologies such as Multimedia, Semantic Web and Sensor Web technologies are advancing in a fierce rate and will sure to become part of our important regular items in a 'conventional' technology inventory in near future. While the R&D nature of these technologies means their accuracy, reliability and robustness are not sufficient enough to be used in real world yet, we want to envision now the near-future where these technologies will have matured and used in real applications in order to explore and start shaping many possible new ways these novel technologies could be utilised. In this talk, some of this effort in designing novel applications that incorporate various media technologies as their backend will be presented. Examples include novel scenarios of LifeLogging application that incorporate automatic structuring of millions of photos passively captured from a SenseCam (wearable digital camera that automatically takes photos triggered by environmental sensors) and an interactive TV application incorporating a number of multimedia tools yet extremely simple and easy to use with a remote control in a lean-back position. The talk will conclude with remarks on how the design of novel applications that have no precedence or existing user base should require somewhat different approach from those suggested and practiced in conventional usability engineering methodology

IgG antibody production and persistence to 6 months following SARS-CoV-2 vaccination: a Northern Ireland observational study

BACKGROUND: This study evaluates spike protein IgG antibody response following Oxford-AstraZeneca COVID-19 vaccination using the AbC-19™ lateral flow device. METHODS: Plasma samples were collected from n=111 individuals from Northern Ireland. The majority were >50 years old and/or clinically vulnerable. Samples were taken at five timepoints from pre-vaccination until 6-months post-first dose. RESULTS: 20.3% of participants had detectable IgG responses pre-vaccination, indicating prior COVID-19. Antibodies were detected in 86.9% of participants three weeks after the first vaccine dose, falling to 74.7% immediately prior to the second dose, and rising to 99% three weeks post-second vaccine. At 6-months post-first dose, this decreased to 90.5%. At all timepoints, previously infected participants had significantly higher antibody levels than those not previously infected. CONCLUSION: This study demonstrates that strong anti-spike protein antibody responses are evoked in almost all individuals that receive two doses of Oxford-AstraZeneca vaccine, and largely persist beyond six months after first vaccination

Pilot cluster randomised trial of an evidence-based intervention to reduce avoidable hospital admissions in nursing home residents (Better Health in Residents of Care Homes with Nursing—BHiRCH-NH Study)

Objectives: To pilot a complex intervention to support healthcare and improve early detection and treatment for common health conditions experienced by nursing home (NH) residents. / Design: Pilot cluster randomised controlled trial. / Setting: 14 NHs (7 intervention, 7 control) in London and West Yorkshire. / Participants: NH residents, their family carers and staff. / Intervention: Complex intervention to support healthcare and improve early detection and treatment of urinary tract and respiratory infections, chronic heart failure and dehydration, comprising: (1) ‘Stop and Watch (S&W)’ early warning tool for changes in physical health, (2) condition-specific care pathway and (3) Situation, Background, Assessment and Recommendation tool to enhance communication with primary care. Implementation was supported by Practice Development Champions, a Practice Development Support Group and regular telephone coaching with external facilitators. / Outcome measures: Data on NH (quality ratings, size, ownership), residents, family carers and staff demographics during the month prior to intervention and subsequently, numbers of admissions, accident and emergency visits, and unscheduled general practitioner visits monthly for 6 months during intervention. We collected data on how the intervention was used, healthcare resource use and quality of life data for economic evaluation. We assessed recruitment and retention, and whether a full trial was warranted. / Results: We recruited 14 NHs, 148 staff, 95 family carers and 245 residents. We retained the majority of participants recruited (95%). 15% of residents had an unplanned hospital admission for one of the four study conditions. We were able to collect sufficient questionnaire data (all over 96% complete). No NH implemented intervention tools as planned. Only 16 S&W forms and 8 care pathways were completed. There was no evidence of harm. / Conclusions: Recruitment, retention and data collection processes were effective but the intervention not implemented. A full trial is not warranted. Trial registration number ISRCTN74109734 (https://doi.org/10.1186/ISRCTN74109734)

Mutation-independent Allele-Specific Editing by CRISPR-Cas9, a Novel Approach to Treat Autosomal Dominant Disease

CRISPR-Cas9 provides a tool to treat autosomal dominant disease by non-homologous end joining (NHEJ) gene disruption of the mutant allele. In order to discriminate between wild-type and mutant alleles, Streptococcus pyogenes Cas9 (SpCas9) must be able to detect a single nucleotide change. Allele-specific editing can be achieved by using either a guide-specific approach, in which the missense mutation is found within the guide sequence, or a protospacer-adjacent motif (PAM)-specific approach, in which the missense mutation generates a novel PAM. While both approaches have been shown to offer allele specificity in certain contexts, in cases where numerous missense mutations are associated with a particular disease, such as TGFBI (transforming growth factor β-induced) corneal dystrophies, it is neither possible nor realistic to target each mutation individually. In this study, we demonstrate allele-specific CRISPR gene editing independent of the disease-causing mutation that is capable of achieving complete allele discrimination, and we propose it as a targeting approach for autosomal dominant disease. Our approach utilizes natural variants in the target region that contain a PAM on one allele that lies in cis with the causative mutation, removing the constraints of a mutation-dependent approach. Our innovative patient-specific guide design approach takes into account the patient’s individual genetic make-up, allowing on- and off-target activity to be assessed in a personalized manner

ImmunoCluster provides a computational framework for the non-specialist to profile high- dimensional cytometry data

High dimensional cytometry is an innovative tool for immune monitoring in health and disease, it has provided novel insight into the underlying biology as well as biomarkers for a variety of diseases. However, the analysis of large multiparametric datasets usually requires specialist computational knowledge. Here we describe ImmunoCluster (https://github.com/kordastilab/ImmunoCluster) an R package for immune profiling cellular heterogeneity in high dimensional liquid and imaging mass cytometry, and flow cytometry data, designed to facilitate computational analysis by a non-specialist. The analysis framework implemented within ImmunoCluster is readily scalable to millions of cells and provides a variety of visualization and analytical approaches, as well as a rich array of plotting tools that can be tailored to users' needs. The protocol consists of three core computational stages: 1, data import and quality control; 2, dimensionality reduction and unsupervised clustering; and 3, annotation and differential testing, all contained within an R-based open-source framework

Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes

Prediction models for population-based screening need, for global usage, to be resource-driven, involving predictors that are affordably resourced. Here, we report the development and validation of three resource-driven risk models to identify people with type 2 diabetes (T2DM) at risk of stage 3 CKD defined by a decline in estimated glomerular filtration rate (eGFR) to below 60 mL/min/1.73m2. The observational study cohort used for model development consisted of data from a primary care dataset of 20,510 multi-ethnic individuals with T2DM from London, UK (2007–2018). Discrimination and calibration of the resulting prediction models developed using cox regression were assessed using the c-statistic and calibration slope, respectively. Models were internally validated using tenfold cross-validation and externally validated on 13,346 primary care individuals from Wales, UK. The simplest model was simplified into a risk score to enable implementation in community-based medicine. The derived full model included demographic, laboratory parameters, medication-use, cardiovascular disease history (CVD) and sight threatening retinopathy status (STDR). Two less resource-intense models were developed by excluding CVD and STDR in the second model and HbA1c and HDL in the third model. All three 5-year risk models had good internal discrimination and calibration (optimism adjusted C-statistics were each 0.85 and calibration slopes 0.999–1.002). In Wales, models achieved excellent discrimination(c-statistics ranged 0.82–0.83). Calibration slopes at 5-years suggested models over-predicted risks, however were successfully updated to accommodate reduced incidence of stage 3 CKD in Wales, which improved their alignment with the observed rates in Wales (E/O ratios near to 1). The risk score demonstrated similar model performance compared to direct evaluation of the cox model. These resource-driven risk prediction models may enable universal screening for Stage 3 CKD to enable targeted early optimisation of risk factors for CKD