Measuring and modelling air mass flow rate in the injection stretch blow moulding process

The injection stretch blow moulding process involves the inflation and stretching of a hot preform into a mould to form bottles. A critical process variable and an essential input for process simulations is the rate of pressure increase within the preform during forming, which is regulated by an air flow restrictor valve. The paper describes a set of experiments for measuring the air flow rate within an industrial ISBM machine and the subsequent modelling of it with the FEA package ABAQUS. Two rigid containers were inserted into a Sidel SBO1 blow moulding machine and subjected to different supply pressures and air flow restrictor settings. The pressure and air temperature were recorded for each experiment enabling the mass flow rate of air to be determined along with an important machine characteristic known as the ‘dead volume’. The experimental setup was simulated within the commercial FEA package ABAQUS/Explicit using a combination of structural, fluid and fluid link elements that idealize the air flowing through an orifice behaving as an ideal gas under isothermal conditions. Results between experiment and simulation are compared and show a good correlation.

LD Score regression distinguishes confounding from polygenicity in genome-wide association studies

Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.

Address inaccuracy in health registration data: determinants and impacts

Access to demographic data that are complete, accurate and up-to-date is fundamental to many aspects of public health, government and academic work and for accurate interpretation of other databases. Health registration data are the prime source of demographic information for health and social care systems; for example, as an indicator of need, as a source of denominators to convert number of events into rates, or in the case of the residential address information as the basis for generating the call-recall invitation letters that are used for most screening programs (e.g. breast, colo-rectal and AAA screening). However, list inflation (ghosts, duplicates or emigrants) and a degree of address inaccuracy are recognised caveats with the health registration data and a recent NILS-related study on breast screening suggests that improved address accuracy might be a fast and efficient means of increasing screening uptake rates in cities and amongst deprived populations. In NI these data are collated by the BSO who uniquely in the UK also have access to data relating to prescribing, dental registrations and use of A&E services. These can be used to supplement the standard demographic and address information by (i) indicating patients who are alive and resident in NI and (ii) providing an independent source of probably improved address information. This study will use the NI Unique Property Reference Number (UPRN), rather than the addresses per se which are difficult to work with, to compare the addresses registered in the BSO with those addresses in the enumerated 2011 census. Assuming that the census is a more accurate source of address information for individuals, a comparison of the health registration addresses with those recorded at the census, the aim of the proposed study will be to (i) characterise the amount and distributions of these differences, (ii) to see what proportion of those who do not attend for screening did not actually receive an invitation letter because the addresses were incorrect, (iii) to determine how much of the social gradient (and urban/rural differences) in screening uptake are due to address inaccuracies, (iv) a comparison of timing of address changes at the BSO will provide information on the delays in updating of addresses.

Network signatures based on gene pair expression ratios improve classification and the analysis of muscle-invasive urothelial cancer

Urothelial cancer (UC) is highly recurrent and can progress from non-invasive (NMIUC) to a more aggressive muscle-invasive (MIUC) subtype that invades the muscle tissue layer of the bladder. We present a proof of principle study that network-based features of gene pairs can be used to improve classifier performance and the functional analysis of urothelial cancer gene expression data. In the first step of our procedure each individual sample of a UC gene expression dataset is inflated by gene pair expression ratios that are defined based on a given network structure. In the second step an elastic net feature selection procedure for network-based signatures is applied to discriminate between NMIUC and MIUC samples. We performed a repeated random subsampling cross validation in three independent datasets. The network signatures were characterized by a functional enrichment analysis and studied for the enrichment of known cancer genes. We observed that the network-based gene signatures from meta collections of proteinprotein interaction (PPI) databases such as CPDB and the PPI databases HPRD and BioGrid improved the classification performance compared to single gene based signatures. The network based signatures that were derived from PPI databases showed a prominent enrichment of cancer genes (e.g., TP53, TRIM27 and HNRNPA2Bl). We provide a novel integrative approach for large-scale gene expression analysis for the identification and development of novel diagnostical targets in bladder cancer. Further, our method allowed to link cancer gene associations to network-based expression signatures that are not observed in gene-based expression signatures.