| 1. |
- Niemi, MEK, et al.
(author)
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- 2021
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swepub:Mat__t
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| 2. |
- Kanai, M, et al.
(author)
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- 2023
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swepub:Mat__t
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| 11. |
- Gould, A., et al.
(author)
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MOA-2010-BLG-523:" Failed Planet"= RS CVn Star
- 2013
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In: Astrophysical Journal. - 0004-637X. ; 763:2
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Journal article (peer-reviewed)abstract
- The Galactic bulge source MOA-2010-BLG-523S exhibited short-term deviations from a standard microlensing light curve near the peak of an A(max) similar to 265 high-magnification microlensing event. The deviations originally seemed consistent with expectations for a planetary companion to the principal lens. We combine long-term photometric monitoring with a previously published high-resolution spectrum taken near peak to demonstrate that this is an RS CVn variable, so that planetary microlensing is not required to explain the light-curve deviations. This is the first spectroscopically confirmed RS CVn star discovered in the Galactic bulge.
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| 12. |
- Kennedy, L J, et al.
(author)
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Association of canine hypothyroidism with a common major histocompatibility complex DLA class II allele.
- 2006
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In: Tissue Antigens. - : Wiley. - 0001-2815 .- 1399-0039. ; 68:1, s. 82-6
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Journal article (peer-reviewed)abstract
- Dogs exhibit a range of immune-mediated conditions including a lymphocytic thyroiditis which has many similarities to Hashimoto's thyroiditis in man. We have recently reported an association in Doberman Pinschers between canine hypothyroidism and a rare DLA class II haplotype that contains the DLA-DQA1*00101 allele. We now report a further series of 173 hypothyroid dogs in a range of breeds where a significant association with DLA-DQA1*00101 is shown.
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| 13. |
- Betsou, F, et al.
(author)
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Standard PREanalytical Code Version 3.0
- 2018
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In: Biopreservation and biobanking. - : Mary Ann Liebert Inc. - 1947-5543 .- 1947-5535. ; 16:1, s. 9-12
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Journal article (peer-reviewed)
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| 14. |
- Dodds, Michael G, et al.
(author)
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Robust population pharmacokinetic experiment design.
- 2005
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In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 32:1, s. 33-64
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Journal article (peer-reviewed)abstract
- The population approach to estimating mixed effects model parameters of interest in pharmacokinetic (PK) studies has been demonstrated to be an effective method in quantifying relevant population drug properties. The information available for each individual is usually sparse. As such, care should be taken to ensure that the information gained from each population experiment is as efficient as possible by designing the experiment optimally, according to some criterion. The classic approach to this problem is to design "good" sampling schedules, usually addressed by the D-optimality criterion. This method has the drawback of requiring exact advanced knowledge (expected values) of the parameters of interest. Often, this information is not available. Additionally, if such prior knowledge about the parameters is misspecified, this approach yields designs that may not be robust for parameter estimation. In order to incorporate uncertainty in the prior parameter specification, a number of criteria have been suggested. We focus on ED-optimality. This criterion leads to a difficult numerical problem, which is made tractable here by a novel approximation of the expectation integral usually solved by stochastic integration techniques. We present two case studies as evidence of the robustness of ED-optimal designs in the face of misspecified prior information. Estimates from replicate simulated population data show that such misspecified ED-optimal designs recover parameter estimates that are better than similarly misspecified D-optimal designs, and approach estimates gained from D-optimal designs where the parameters are correctly specified.
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| 15. |
- Foster, Yasmin, et al.
(author)
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Genomic signatures of inbreeding in a critically endangered parrot, the kākāpō
- 2021
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In: G3. - : Oxford University Press (OUP). - 2160-1836. ; 11:11
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Journal article (peer-reviewed)abstract
- Events of inbreeding are inevitable in critically endangered species. Reduced population sizes and unique life-history traits can increase the severity of inbreeding, leading to declines in fitness and increased risk of extinction. Here, we investigate levels of inbreeding in a critically endangered flightless parrot, the kākāpō (Strigops habroptilus), wherein a highly inbred island population and one individual from the mainland of New Zealand founded the entire extant population. Genotyping-by-sequencing (GBS), and a genotype calling approach using a chromosome-level genome assembly, identified a filtered set of 12,241 single-nucleotide polymorphisms (SNPs) among 161 kākāpō, which together encompass the total genetic potential of the extant population. Multiple molecular-based estimates of inbreeding were compared, including genome-wide estimates of heterozygosity (FH), the diagonal elements of a genomic-relatedness matrix (FGRM), and runs of homozygosity (RoH, FRoH). In addition, we compared levels of inbreeding in chicks from a recent breeding season to examine if inbreeding is associated with offspring survival. The density of SNPs generated with GBS was sufficient to identify chromosomes that were largely homozygous with RoH distributed in similar patterns to other inbred species. Measures of inbreeding were largely correlated and differed significantly between descendants of the two founding populations. However, neither inbreeding nor ancestry was found to be associated with reduced survivorship in chicks, owing to unexpected mortality in chicks exhibiting low levels of inbreeding. Our study highlights important considerations for estimating inbreeding in critically endangered species, such as the impacts of small population sizes and admixture between diverse lineages.
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| 16. |
- Hooker, Andrew C, et al.
(author)
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An evaluation of population D-optimal designs via pharmacokinetic simulations.
- 2003
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In: Annals of Biomedical Engineering. - 0090-6964 .- 1573-9686. ; 31:1, s. 98-111
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Journal article (peer-reviewed)abstract
- One goal of large scale clinical trials is to determine how a drug is processed by, and cleared from, the human body [i.e., its pharmacokinetic (PK) properties] and how these PK properties differ between individuals in a population (i.e., its population PK properties). Due to the high cost of these studies and the limited amount of data (e.g., blood samples) available from each study subject, it would be useful to know how many measurements are needed and when those measurements should be taken to accurately quantify population PK model parameters means and variances. Previous studies have looked at optimal design strategies of population PK experiments by developing an optimal design for an individual study (i.e., no interindividual variability was considered in the design), and then applying that design to each individual in a population study (where interindividual variability is present). A more algorithmically and informationally intensive approach is to develop a population optimal design, which inherently includes the assessment of interindividual variability. We present a simulation-based evaluation of these two design methods based on nonlinear Gaussian population PK models. Specifically, we compute standard individual and population D-optimal designs and compare population PK model parameter estimates based on simulated optimal design measurements. Our results show that population and standard D-optimal designs are not significantly different when both designs have the same number of samples per individual. However, population optimal designs allow for sampling schedules where the number of samples per individual is less than the number of model parameters, the theoretical limit allowed in standard optimal design. These designs with a low number of samples per individual are shown to be nearly as robust in parameter estimation as standard D-optimal designs. In the limit of just one sample per individual, however, population D-optimal designs are shown to be inadequate.
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| 19. |
- Rogozińska, Ewelina, et al.
(author)
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Effects of antenatal diet and physical activity on maternal and fetal outcomes : Individual patient data meta-analysis and health economic evaluation
- 2017
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In: Health Technology Assessment. - : National Institute for Health Research. - 1366-5278 .- 2046-4924. ; 21:41
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Journal article (peer-reviewed)abstract
- Background: Diet- and physical activity-based interventions in pregnancy have the potential to alter maternal and child outcomes. Objectives: To assess whether or not the effects of diet and lifestyle interventions vary in subgroups of women, based on maternal body mass index (BMI), age, parity, Caucasian ethnicity and underlying medical condition(s), by undertaking an individual patient data (IPD) meta-analysis. We also evaluated the association of gestational weight gain (GWG) with adverse pregnancy outcomes and assessed the cost-effectiveness of the interventions. Data sources: MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Database of Abstracts of Reviews of Effects and Health Technology Assessment database were searched from October 2013 to March 2015 (to update a previous search). Review methods: Researchers from the International Weight Management in Pregnancy Collaborative Network shared the primary data. For each intervention type and outcome, we performed a two-step IPD random-effects meta-analysis, for all women (except underweight) combined and for each subgroup of interest, to obtain summary estimates of effects and 95% confidence intervals (CIs), and synthesised the differences in effects between subgroups. In the first stage, we fitted a linear regression adjusted for baseline (for continuous outcomes) or a logistic regression model (for binary outcomes) in each study separately; estimates were combined across studies using random-effects meta-analysis models. We quantified the relationship between weight gain and complications, and undertook a decision-analytic model-based economic evaluation to assess the cost-effectiveness of the interventions. Results: Diet and lifestyle interventions reduced GWG by an average of 0.70 kg (95% CI-0.92 to-0.48 kg; 33 studies, 9320 women). The effects on composite maternal outcome [summary odds ratio (OR) 0.90, 95% CI 0.79 to 1.03; 24 studies, 8852 women] and composite fetal/neonatal outcome (summary OR 0.94, 95% CI 0.83 to 1.08; 18 studies, 7981 women) were not significant. The effect did not vary with baseline BMI, age, ethnicity, parity or underlying medical conditions for GWG, and composite maternal and fetal outcomes. Lifestyle interventions reduce Caesarean sections (OR 0.91, 95% CI 0.83 to 0.99), but not other individual maternal outcomes such as gestational diabetes mellitus (OR 0.89, 95% CI 0.72 to 1.10), pre-eclampsia or pregnancy-induced hypertension (OR 0.95, 95% CI 0.78 to 1.16) and preterm birth (OR 0.94, 95% CI 0.78 to 1.13). There was no significant effect on fetal outcomes. The interventions were not cost-effective. GWG, including adherence to the Institute of Medicine-recommended targets, was not associated with a reduction in complications. Predictors of GWG were maternal age (summary estimate-0.10 kg, 95% CI-0.14 to-0.06 kg) and multiparity (summary estimate-0.73 kg, 95% CI-1.24 to-0.23 kg). Limitations: The findings were limited by the lack of standardisation in the components of intervention, residual heterogeneity in effects across studies for most analyses and the unavailability of IPD in some studies. Conclusion: Diet and lifestyle interventions in pregnancy are clinically effective in reducing GWG irrespective of risk factors, with no effects on composite maternal and fetal outcomes. Future work: The differential effects of lifestyle interventions on individual pregnancy outcomes need evaluation. Study registration: This study is registered as PROSPERO CRD42013003804.
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