Traces of Extra Dimensions in Cosmology

In this thesis, we discuss the observational consequences of extra dimensions on cosmological phenomena. We begin with an overview of extra dimensions, from the initial ideas of Kaluza and Klein to the more recent concept of braneworld models and in particular review the cosmological aspects of the DGP braneworld model, which can produce late time acceleration. We then go on to consider the asymmetric brane model, comparing its cosmology to the standard concordance and DGP models and showing how the asymmetric model can be considered a one-parameter extension of the DGP model over a range of relevant physical scales. Using type Ia supernovae data and the cosmic microwave background shift parameter, the effect of this new parameter on the expansion history of the universe is considered. We then turn our attention to cosmic string loops, which emit bursts of gravitational radiation, produced by cusps and kinks on the loops. We investigate the kinematic effect extra dimensions will have on these gravitational wave bursts and find that the effects of the additional dimensions are more pronounced for cusps than for kinks: cusps are rounded off and their probability of formation is reduced, however, the probability of kink formation is unchanged. Finally, we recompute the gravitational wave bursts taking the various factors into account and look at the implications of this recalculation for the LIGO and LISA gravitational wave detectors, find that both signals, and in particular the cusp signal, have a potentially significant damping, and consider the implications for the detection of extra dimensions

Effect of extra dimensions on gravitational waves from cosmic strings

We show how the motion of cosmic superstrings in extra dimensions can modify the gravitational wave signal from cusps. Additional dimensions both round off cusps, as well as reducing the probability of their formation, and thus give a significant dimension dependent damping of the gravitational waves. We look at the implication of this effect for LIGO and LISA, as well as commenting on more general frequency bands

The effect of extra dimensions on gravity wave bursts from cosmic string cusps

We explore the kinematical effect of having extra dimensions on the gravity wave emission from cosmic strings. Additional dimensions both round off cusps, and reduce the probability of their formation. We recompute the gravity wave burst, taking into account these two factors, and find a potentially significant damping on the gravity waves of the strings.Comment: 33 pages, 8 figures, published versio

The Cosmology of Asymmetric Brane Modified Gravity

We consider the asymmetric branes model of modified gravity, which can produce late time acceleration of the universe and compare the cosmology of this model to the standard $\Lambda$CDM model and to the DGP braneworld model. We show how the asymmetric cosmology at relevant physical scales can be regarded as a one-parameter extension of the DGP model, and investigate the effect of this additional parameter on the expansion history of the universe.Comment: 21 pages, 9 figures, journal versio

On detection of extra dimensions with gravity waves from cosmic strings

We show how taking into account the kinematical effect of extra dimensions can have a significant impact on the gravity wave emission from cosmic strings. Additional dimensions both round off cusps, as well as reduce the probability of their formation. We recompute the cusp gravity wave burst with these factors and find a significant dimension dependent damping of the gravity waves

Comprehensive functional analysis reveals that acrosome integrity and viability are key variables distinguishing artificial insemination bulls of varying fertility

In vitro methods of assessing bull semen quality in artificial insemination (AI) centers are unable to consistently detect individuals of lower fertility, and attempts to reliably predict bull fertility are still ongoing. This highlights the need to identify robust biomarkers that can be readily measured in a practical setting and used to improve current predictions of bull fertility. In this study, we comprehensively analyzed a range of functional, morphological, and intracellular attributes in cryopreserved spermatozoa from a selected cohort of Holstein Friesian AI bulls classified as having either high or low fertility (n = 10 of each fertility phenotype; difference of 11.4% in adjusted pregnancy rate between groups). Here, spermatozoa were assessed for motility and kinematic parameters, morphology, acrosome integrity, plasma membrane lipid packing, viability (or membrane integrity), superoxide production, and DNA integrity. In addition, spermatozoa were used for in vitro fertilization to evaluate their capacity for fertilization and successful embryo development. The information collected from these assessments was then used to phenotypically profile the 2 groups of bulls of divergent fertility status as well as to develop a model to predict bull fertility. According to the results, acrosome integrity and viability were the only sperm attributes that were significantly different between high- and low-fertility bulls. Interestingly, although spermatozoa from low-fertility bulls, on average, had reduced viability and acrosome integrity, this response varied considerably from bull to bull. Principal component analysis revealed a sperm phenotypic profile that represented a high proportion of ejaculates from low-fertility bulls. This was constructed based on the collective influence of several sperm attributes, including the presence of cytoplasmic droplets and superoxide production. Finally, using the combined results as a basis for modeling, we developed a linear model that was able to explain 47% of the variation in bull field fertility in addition to a logistic predictive model that had a 90% chance of distinguishing between fertility groups. Taken together, we conclude that viability and acrosome integrity could serve as fertility biomarkers in the field and, when used alongside other sperm attributes, may be useful in detecting low-fertility bulls. However, the variable nature of low-fertility bulls suggests that additional, in-depth characterization of spermatozoa at a molecular level is required to further understand the etiology of low fertility in dairy bulls

SARS-CoV-2 infection in general practice in Ireland: a seroprevalence study

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody testing in community settings may help us better understand the immune response to this virus and, therefore, help guide public health efforts. Aim: To conduct a seroprevalence study of immunoglobulin G (IgG) antibodies in Irish GP clinics. Design & setting: Participants were 172 staff and 799 patients from 15 general practices in the Midwest region of Ireland. Method: This seroprevalence study utilised two manufacturers’ point-of-care (POC) SARS-CoV-2 immunoglobulin M (IgM)—IgG combined antibody tests, which were offered to patients and staff in general practice from 15 June to 10 July 2020. Results: IgG seroprevalence was 12.6% in patients attending general practice and 11.1% in staff working in general practice, with administrative staff having the lowest seroprevalence at 2.5% and nursing staff having the highest at 17.6%. Previous symptoms suggestive of COVID-19 and history of a polymerase chain reaction (PCR) test were associated with higher seroprevalence. IgG antibodies were detected in approximately 80% of participants who had a previous PCR-confirmed infection. Average length of time between participants’ positive PCR test and positive IgG antibody test was 83 days. Conclusion: Patients and healthcare staff in general practice in Ireland had relatively high rates of IgG to SARS-CoV-2 compared with the national average between 15 June and 10 July 2020 (1.7%). Four fifths of participants with a history of confirmed COVID-19 disease still had detectable antibodies an average of 12 weeks post-infection. While not proof of immunity, SARS-CoV-2 POC testing can be used to estimate IgG seroprevalence in general practice settings

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R2 increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R2 increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase