Does the Child Health Computing System adequately identify children with cerebral palsy?

Background This paper assesses the usefulness of the Child Health Computing System as a source of information about children with cerebral palsy.

Methods A comparative survey of information held on the Child Health Computing System (CHCS) and the Northern Ireland Cerebral Palsy Register (NICPR) in one Health and Social Services Board in Northern Ireland was carried out. The sample comprised children with cerebral palsy aged 5–9 years.

Results Of the 135 cases recorded on the NICPR, 47 per cent were not found on the CHCS; the majority of these children had no computer record of any medical diagnosis. Of the 82 cases recorded on the CHCS, 10(12 per cent) were not found on the NICPR; five of these cases (6 per cent) were found on follow–up not to have CP.

Conclusions Unless improvements are made in case ascertainment, case validation and recording activities, the evidence suggests that the CHCS will not be able to provide the same quality of information for needs assessment and surveillance of very low birthweight infants in relation to cerebral palsy as is provided by a specialist case register.

Developmental care for high-risk newborns:Emerging science, clinical application, and continuity from newborn intensive care unit to community

Neonatology has optimized medical outcomes for high-risk newborns yet neurodevelopmental outcomes continue to be a concern. Basic science, clinical research, and environmental design perspectives have shown the impact of the caregiving environment on the developing brain and the role of professional caregivers in providing supportive intervention to both infants and their families. This recognition has prompted a focus on early developmentally supportive care (DSC) for high-risk newborns both in the hospital and in community follow up. DSC has emerged as a recognized standard of care in most neonatal intensive care units. Still, many questions remain and much integrative research is needed.

Influence of unit operations on the levels of polyacetylenes in minimally processed carrots and parsnips: An industrial trial

Carrots and parsnips are often consumed as minimally processed ready-to-eat convenient foods and contain in minor quantities, bioactive aliphatic C17-polyacetylenes (falcarinol, falcarindiol, falcarindiol-3-acetate). Their retention during minimal processing in an industrial trial was evaluated. Carrot and parsnips were prepared in four different forms (disc cutting, baton cutting, cubing and shredding) and samples were taken in every point of their processing line. The unit operations were: peeling, cutting and washing with chlorinated water and also retention during 7 days storage was evaluated. The results showed that the initial unit operations (mainly peeling) influence the polyacetylene retention. This was attributed to the high polyacetylene content of their peels. In most cases, when washing was performed after cutting, less retention was observed possibly due to leakage during tissue damage occurred in the cutting step. The relatively high retention during storage indicates high plant matrix stability. Comparing the behaviour of polyacetylenes in the two vegetables during storage, the results showed that they were slightly more retained in parsnips than in carrots. Unit operations and especially abrasive peeling might need further optimisation to make them gentler and minimise bioactive losses.

A Capabilities-Aware Framework for Using Computational Accelerators in Data-Intensive Computing

Multicore computational accelerators such as GPUs are now commodity components for highperformance computing at scale. While such accelerators have been studied in some detail as stand-alone computational engines, their integration in large-scale distributed systems raises new challenges and trade-offs. In this paper, we present an exploration of resource management alternatives for building asymmetric accelerator-based distributed systems. We present these alternatives in the context of a capabilities-aware framework for data-intensive computing, which uses an enhanced implementation of the MapReduce programming model for accelerator-based clusters, compared to the state of the art. The framework can transparently utilize heterogeneous accelerators for deriving high performance with low programming effort. Our work is the first to compare heterogeneous types of accelerators, GPUs and a Cell processors, in the same environment and the first to explore the trade-offs between compute-efficient and control-efficient accelerators on data-intensive systems. Our investigation shows that our framework scales well with the number of different compute nodes. Furthermore, it runs simultaneously on two different types of accelerators, successfully adapts to the resource capabilities, and performs 26.9% better on average than a static execution approach.