Optimal Data Distribution for Big-Data All-to-All Comparison using Finite Projective and Affine Planes

An All-to-All Comparison problem is where every element of a data set is compared with every other element. This is analogous to projective planes and affine planes where every pair of points share a common line. For large data sets, the comparison computations can be distributed across a cluster of computers. All-to-All Comparison does not fit the highly successful Map-Reduce pattern, so a new distributed computing framework is required. The principal challenge is to distribute the data in such a way that computations can be scheduled where the data already lies. This paper uses projective planes, affine planes and balanced incomplete block designs to design data distributions and schedule computations. The data distributions based on these geometric and combinatorial structures achieve minimal data replication whilst balancing the computational load across the cluster

An automated system for polymer wear debris analysis in total disc arthroplasty using convolution neural network

Introduction: Polymer wear debris is one of the major concerns in total joint replacements due to wear-induced biological reactions which can lead to osteolysis and joint failure. The wear-induced biological reactions depend on the wear volume, shape and size of the wear debris and their volumetric concentration. The study of wear particles is crucial in analysing the failure modes of the total joint replacements to ensure improved designs and materials are introduced for the next generation of devices. Existing methods of wear debris analysis follow a traditional approach of computer-aided manual identification and segmentation of wear debris which encounters problems such as significant manual effort, time consumption, low accuracy due to user errors and biases, and overall lack of insight into the wear regime. Methods: This study proposes an automatic particle segmentation algorithm using adaptive thresholding followed by classification using Convolution Neural Network (CNN) to classify ultra-high molecular weight polyethylene polymer wear debris generated from total disc replacements tested in a spine simulator. A CNN takes object pixels as numeric input and uses convolution operations to create feature maps which are used to classify objects. Results: Classification accuracies of up to 96.49% were achieved for the identification of wear particles. Particle characteristics such as shape, size and area were estimated to generate size and volumetric distribution graphs. Discussion: The use of computer algorithms and CNN facilitates the analysis of a wider range of wear debris with complex characteristics with significantly fewer resources which results in robust size and volume distribution graphs for the estimation of the osteolytic potential of devices using functional biological activity estimates.</p

An automated system for polymer wear debris analysis in total disc arthroplasty using convolution neural network

Introduction: Polymer wear debris is one of the major concerns in total joint replacements due to wear-induced biological reactions which can lead to osteolysis and joint failure. The wear-induced biological reactions depend on the wear volume, shape and size of the wear debris and their volumetric concentration. The study of wear particles is crucial in analysing the failure modes of the total joint replacements to ensure improved designs and materials are introduced for the next generation of devices. Existing methods of wear debris analysis follow a traditional approach of computer-aided manual identification and segmentation of wear debris which encounters problems such as significant manual effort, time consumption, low accuracy due to user errors and biases, and overall lack of insight into the wear regime.Methods: This study proposes an automatic particle segmentation algorithm using adaptive thresholding followed by classification using Convolution Neural Network (CNN) to classify ultra-high molecular weight polyethylene polymer wear debris generated from total disc replacements tested in a spine simulator. A CNN takes object pixels as numeric input and uses convolution operations to create feature maps which are used to classify objects.Results: Classification accuracies of up to 96.49% were achieved for the identification of wear particles. Particle characteristics such as shape, size and area were estimated to generate size and volumetric distribution graphs.Discussion: The use of computer algorithms and CNN facilitates the analysis of a wider range of wear debris with complex characteristics with significantly fewer resources which results in robust size and volume distribution graphs for the estimation of the osteolytic potential of devices using functional biological activity estimates

What is the impact of volunteers providing care and support for people with dementia in acute hospitals? : a meta-synthesis

A quarter of acute hospital beds are occupied by people with dementia, and a hospital stay may impact negatively on their health and wellbeing. The development and implementation of volunteers to provide social, activity-based, one-to-one support for people with dementia in acute hospitals has become routine practice. However, the evidence to support this practice has not been identified or evaluated. This systematic review considers the effect of volunteers on the care and experience of people with co-morbid cognitive impairment/dementia in acute hospitals. The systematic search identified 444 papers, although only three papers included specific analysis relating to the impact of volunteers. The evidence suggests volunteers may have potential to enhance the experiences of people with dementia in acute hospitals; however, there is currently a marked lack of evidence to support the widespread implementation of volunteers. There is therefore an urgent need for multi-site robust research to provide evidence of the impact of volunteers supporting people with cognitive impairment/dementia during an acute hospital stay

Evaluating the communication within fire and rescue services and the NHS on the fire risk of emollients in accordance of the MHRA safety update

The Medicines and Healthcare products Regulatory Agency update in 2018 reported 50 fatal fires linked with emollient use. It detailed the fire risk and new advice aimed at fire service and health care professionals in reporting of such fire incidents and educating the public on safer use of emollients. This study investigates how this has been communicated internally and publicly, with 52 Fire and Rescue Services (FRSs) websites and, 191 Clinical Commissioning Groups (CCGs), and 21 Local Health Boards (LHBs) formularies accessed. A Freedom of Information Request (FOIR) was also made, giving further details of implementations. Our study revealed that 63% of FRSs, 32% of CCGs and, 72% of LHBs gave no safety advice within their website or formularies. Of the 37% of FRSs and 68% of CCGs that did, only 5% and 4% were sufficiently up to date. 27% of FRSs and 28% of CCGs/LHBs revealed that they had no warning/advice internally in their FOIR responses and 25% of FRSs and, 35% of CCG/LHBs had not disseminated advice on using emollient safely to the public. We suggest improvements in safety campaigns using a multiagency and national approach and recommend organizations to educate professionals to improve reporting and effective dissemination

Damned if they do, damned if they don't: negotiating the tricky context of anti-social behaviour and keeping safe in disadvantaged urban neighbourhoods

Young people's relationship with anti-social behaviour (ASB) is complicated. While their behaviours are often stereotyped as anti-social (e.g. ‘hanging about’), they also experience ASB in their neighbourhood. In this study, we explore young people's own perspectives on ASB, comparing results from ‘go-along’ interviews and focus groups conducted in disadvantaged neighbourhoods in Glasgow, Scotland. This article discusses how young people's everyday experience of ASB was contextualised by social factors such as cultural stereotyping of marginalised groups, poor social connectivity and spatial marginalisation within their neighbourhood. Furthermore, we found that these social factors were mutually reinforcing and interacted in a way that appeared to leave young people in a ‘no-win’ situation regarding their association with ASB. Participation in ASB and attempts to avoid such involvement were seen to involve negative consequences: participation could entail violence and spatial restrictions linked to territoriality, but avoidance could lead to being ostracised from their peer group. Regardless of involvement, young people felt that adults stereotyped them as anti-social. Our findings therefore provide support for policies and interventions aimed at reducing ASB (perpetrated by residents of all ages); in part by better ensuring that young people have a clear incentive for avoiding such behaviours

Exploiting lung adaptation and phage steering to clear pan-resistant Pseudomonas aeruginosa infections in vivo

Pseudomonas aeruginosa is a major nosocomial pathogen that causes severe disease including sepsis. Carbapenem-resistant P. aeruginosa is recognised by the World Health Organisation as a priority 1 pathogen, with urgent need for new therapeutics. As such, there is renewed interest in using bacteriophages as a therapeutic. However, the dynamics of treating pan-resistant P. aeruginosa with phage in vivo are poorly understood. Using a pan-resistant P. aeruginosa in vivo infection model, phage therapy displays strong therapeutic potential, clearing infection from the blood, kidneys, and spleen. Remaining bacteria in the lungs and liver displays phage resistance due to limiting phage adsorption. Yet, resistance to phage results in re-sensitisation to a wide range of antibiotics. In this work, we use phage steering in vivo, pre-exposing a pan resistant P. aeruginosa infection with a phage cocktail to re-sensitise bacteria to antibiotics, clearing the infection from all organs

A megaplasmid family driving dissemination of multidrug resistance in Pseudomonas.

Multidrug resistance (MDR) represents a global threat to health. Here, we used whole genome sequencing to characterise Pseudomonas aeruginosa MDR clinical isolates from a hospital in Thailand. Using long-read sequence data we obtained complete sequences of two closely related megaplasmids (>420 kb) carrying large arrays of antibiotic resistance genes located in discrete, complex and dynamic resistance regions, and revealing evidence of extensive duplication and recombination events. A comprehensive pangenomic and phylogenomic analysis indicates that: 1) these large plasmids comprise an emerging family present in different members of the Pseudomonas genus, and associated with multiple sources (geographical, clinical or environmental); 2) the megaplasmids encode diverse niche-adaptive accessory traits, including multidrug resistance; 3) the accessory genome of the megaplasmid family is highly flexible and diverse. The history of the megaplasmid family, inferred from our analysis of the available database, suggests that members carrying multiple resistance genes date back to at least the 1970s

Trends in the Population Prevalence of People Who Inject Drugs in US Metropolitan Areas 1992–2007

Background: People who inject drugs (PWID) have increased risk of morbidity and mortality. We update and present estimates and trends of the prevalence of current PWID and PWID subpopulations in 96 US metropolitan statistical areas (MSAs) for 1992–2007. Current estimates of PWID and PWID subpopulations will help target services and help to understand long-term health trends among PWID populations. Methodology: We calculated the number of PWID in the US annually from 1992–2007 and apportioned estimates to MSAs using multiplier methods. We used four types of data indicating drug injection to allocate national annual totals to MSAs, creating four distinct series of component estimates of PWID in each MSA and year. The four component estimates are averaged to create the best estimate of PWID for each MSA and year. We estimated PWID prevalence rates for three subpopulations defined by gender, age, and race/ethnicity. We evaluated trends using multi-level polynomial models. Results: PWID per 10,000 persons aged 15–64 years varied across MSAs from 31 to 345 in 1992 (median 104.4) to 34 to 324 in 2007 (median 91.5). Trend analysis indicates that this rate declined during the early period and then was relatively stable in 2002–2007. Overall prevalence rates for non-Hispanic black PWID increased in 2005 as compared to other racial/ethnic groups. Hispanic prevalence, in contrast, declined across time. Importantly, results show a worrisome trend in young PWID prevalence since HAART was initiated – the mean prevalence was 90 to 100 per 10,000 youth in 1992–1996, but increased to >120 PWID per 10,000 youth in 2006–2007. Conclusions: Overall, PWID rates remained constant since 2002, but increased for two subpopulations: non-Hispanic black PWID and young PWID. Estimates of PWID are important for planning and evaluating public health programs to reduce harm among PWID and for understanding related trends in social and health outcomes