Inferring the Components of Residual Switch Costs

There are many theories that have attempted to explain the underlying mechanisms of task switching. While these theories have furthered the understanding of switch costs, there are inconsistencies that prevent an all-encompassing explanation. Dykstra et al. (in prep) provided evidence which suggests that switch costs may vary depending on the type of task performed. While this finding is interesting, the probability of switches were not equal between tasks. This may have altered switch costs. The present study utilized the inferred switch task from Dykstra et al. (in prep) and adjusted the switch probabilities to match the comparison task. We found that even after corrections were made, the inferred switch cost was still greater than the comparison switch cost. This finding may help clarify current theories of task switching

Personal thermophoretic sampler for airborne nanoparticles, A

2010 Summer.Includes bibliographic references (pages 44-45).Covers not scanned.Print version deaccessioned 2022.Engineered nanoparticles are materials with at least one dimension measuring less than 100 nm that are designed on the molecular scale to produce unique or enhanced properties that differ from the bulk material. However, the same properties that make engineered nanoparticles attractive to industry also may present potential health risks to the workers who manufacture them. Very little human exposure data exist for these particles, although they are known enter the body through a number of routes (e.g., respiration, dermal penetrations, and ingestion). Nanoparticles that enter the body can also translocate from one organ to another by virtue of their small size. A cost-effective personal sampler is necessary to evaluate levels of worker exposure to these materials to determine the relative levels of individual risk. Such a sampler must be capable of collecting nanoparticles with high efficiency for subsequent analysis of size, surface chemistry, morphology, and other properties. In addition, the sampler must be able to differentiate between incidental nanoparticles, which are nanoparticles that are naturally present in the environment, and engineered nanoparticles. As detailed in this thesis, a small thermal precipitator was designed to measure breathing-zone concentrations of airborne nanoparticles. The thermal precipitator samples aerosol by producing a 1000 °C cm ' temperature gradient between two aluminum plates (0.1 cm separation distance) using a resistive heater, a thermoelectric cooler, a temperature controller, and two thermistor sensors. The collection efficiency was evaluated for 15, 51, 100, and 240 nm particles at flow rates of 5 and 20 mL/min. Tests were also performed with a zero temperature gradient to determine losses in the device for measurement correction. The homogeneity of particle collection across the collection surface was evaluated using electron microscopy and imaging software. The results indicate that thermal precipitation is a feasible approach for personal monitoring of airborne nanoparticle concentrations in the workplace

Medicines prescribed for asthma, discontinuation and perinatal outcomes, including breastfeeding: A population cohort analysis

ObjectivesTo explore associations between exposures to medicines prescribed for asthma and their discontinuation in pregnancy and preterm birth [MethodsDesign. A population-based cohort study. Setting. The Secure Anonymised Information Linkage [SAIL] databank in Wales, linking maternal primary care data with infant outcomes. Population. 107,573, 105,331, and 38,725 infants born 2000-2010 with information on premature birth, SGA and breastfeeding respectively, after exclusions. Exposures. maternal prescriptions for asthma medicines or their discontinuation in pregnancy. Methods. Odds ratios for adverse pregnancy outcomes were calculated for the exposed versus the unexposed population, adjusted for smoking, parity, age and socio-economic status.ResultsPrescriptions for asthma, whether continued or discontinued during pregnancy, were associated with birth atConclusionsPrescription of asthma medicines before or during pregnancy was associated with higher prevalence of adverse perinatal outcomes, particularly if prescriptions were discontinued during pregnancy. Women discontinuing medicines during pregnancy could be identified from prescription records. The impact of targeting close monitoring and breastfeeding support warrants exploration

Antidepressant prescriptions, discontinuation, depression and perinatal outcomes, including breastfeeding: A population cohort analysis

ObjectivesTo explore associations between exposure to antidepressants, their discontinuation, depression [medicated or unmediated] and preterm birth [MethodsDesign: A population-based cohort study. Setting: The Secure Anonymised Information Linkage [SAIL] databank in Wales, linking maternal primary care data with infant outcomes. Participants: 107,573, 105,331, and 38,725 infants born 2000-2010 with information on prematurity, SGA and breastfeeding respectively, after exclusions. Exposures: Maternal antidepressant prescriptions in trimesters 2 or 3, discontinuation after trimester 1, recorded diagnosis of depression [medicated or unmediated] in pregnancy. Methods: Odds ratios for adverse pregnancy outcomes were calculated, adjusted for smoking, parity, socio-economic status, and depression.ResultsExclusive formula feeding at 6-8 weeks was associated with prescriptions in trimesters 2 or 3 for any antidepressants (adjusted odds ratio [aOR] 0.81, 95% confidence intervals 0.67-0.98), SSRIs [aOR 0.77, 0.62-0.95], particularly higher doses [aOR 0.45, 0.23-0.86], discontinuation of antidepressants or SSRIs after trimester 1 (aOR 0.70, 0.57-0.83 and 0.66, 0.51-0.87), diagnosis of depression aOR 0.76 [0.70-0.82), particularly if medicated (aOR 0.70, 0.58-0.85), rather than unmedicated (aOR 0.87, 0.82-0.92). Preterm birth at ImplicationsExposure to antidepressants or depression increased risks of exclusive formula feeding at 6-8 weeks, and prescription of antidepressants was associated with SGA <3rd centile. Prescription of antidepressants offers a useful marker to target additional support and additional care before and during pregnancy and lactation

Hydrodynamical Simulations of the Lyman Alpha Forest: Model Comparisons

We investigate the properties of the Lyman alpha forest as predicted by numerical simulations for a range of currently viable cosmological models. This is done in order to understand the dependencies of the forest on cosmological parameters. Focusing on the redshift range from two to four, we show that: (1) most of the evolution in the distributions of optical depth, flux and column density can be understood by simple scaling relations, (2) the shape of optical depth distribution is a sensitive probe of the amplitude of density fluctuations on scales of a few hundred kpc, (3) the mean of the b distribution (a measure of the width of the absorption lines) is also very sensitive to fluctuations on these scales, and decreases as they increase. We perform a preliminary comparison to observations, where available. A number of other properties are also examined, including the evolution in the number of lines, the two-point flux distribution and the HeII opacity.Comment: 37 pages, 21 figures, submitted to Ap

Reusable, set-based selection algorithm for matched control groups

ABSTRACT Aims The wealth of data available in linked administrative datasets offers great potential for research, but researchers face methodological and computational challenges in data preparation, due to the size and complexity of the data. The creation of matched control groups in the Secure Anonymised Information Linkage (SAIL) Databank illustrates this point: SAIL contains multiple health datasets describing millions of individuals in Wales. The volume of data creates the potential for more precise matching, but only if an appropriate algorithm can be applied. We aimed to create such an algorithm for reuse by many research projects. Methods We developed set-based code in SQL that efficiently selects matches from millions of potential combinations in a relational database environment. It is parameterized to allow different matching criteria to be employed as needed, including follow-up time around an index event. A combinatorial optimisation problem occurs when a potential control could match more than one subject, which we solved by ranking potential match pairs first by subject with the fewest potential matches, then by closeness of the match. Results One example of the algorithm’s use was the Suicide Information Database Cymru, an electronic case-control study on suicide in Wales between 2003 and 2011. Subjects who had a cause of death recorded as self-harm were each matched to twenty controls who were alive at the subject’s date of death and had the same gender and similar birth week. The rate of matching success was >99.9%, with all subjects but one matching the full twenty controls. >99.99% of the matched controls had a week of birth that was identical to the subject. The second example was a matched cohort study looking at hospital admissions and type 1 diabetes, using the Brecon register of childhood diabetes in Wales, with matching based on week of birth within two weeks, gender, county of residence, deprivation quintile, and residence in Wales at time of diagnosis. This study had a matching rate of 98.9%; 97.5% of subjects matched to five controls, and 69.8% of matches had the same week of birth. Conclusions This algorithm provides good matching performance while executing efficiently and scalably on large datasets. Its implementation as reusable code will facilitate more efficient, high-quality research in SAIL. Instead of spending many hours developing a custom solution, analysts can execute parameterized code in a few minutes. We hope it to be useful more widely beyond SAIL as well

Measuring follow-up time in routinely-collected health datasets: Challenges and solutions

A key requirement for longitudinal studies using routinely-collected health data is to be able to measure what individuals are present in the datasets used, and over what time period. Individuals can enter and leave the covered population of administrative datasets for a variety of reasons, including both life events and characteristics of the datasets themselves. An automated, customizable method of determining individuals' presence was developed for the primary care dataset in Swansea University's SAIL Databank. The primary care dataset covers only a portion of Wales, with 76% of practices participating. The start and end date of the data varies by practice. Additionally, individuals can change practices or leave Wales. To address these issues, a two step process was developed. First, the period for which each practice had data available was calculated by measuring changes in the rate of events recorded over time. Second, the registration records for each individual were simplified. Anomalies such as short gaps and overlaps were resolved by applying a set of rules. The result of these two analyses was a cleaned set of records indicating start and end dates of available primary care data for each individual. Analysis of GP records showed that 91.0% of events occurred within periods calculated as having available data by the algorithm. 98.4% of those events were observed at the same practice of registration as that computed by the algorithm. A standardized method for solving this common problem has enabled faster development of studies using this data set. Using a rigorous, tested, standardized method of verifying presence in the study population will also positively influence the quality of research