The perfect stroke: Moving beyond the performance narrative within rowing

This Thesis presents a narrative inquiry into the experiences rowers have within their sport, through exploring the stories they tell. Following Carless and Douglas (2012) my thesis identifies that success is a multi-dimensional concept. In doing so, I challenge the dominant performance narrative within sport that conceptualises success solely as winning. The performance narrative is often unsustainable and therefore can be damaging to an athlete’s well-being, due to such circumstances as injury, de-selection, drop out, aging, and losing. This can lead to narrative wreckage; where the individual no longer knows how to make sense of their life as the dominant story they told no longer aligns with their experiences. Highlighting, exploring and sharing stories that resist or move past the performance narrative can give individuals the ability to view themselves as a multi-faceted identity, allowing them to holistically enjoy sporting participation. Rowers interviewed in this thesis told stories of winning, but also of friendship, loyalty, and the freedom of movement. Of particular interest were the stories told that shared a sense of embodied convergence; the sensation of merging with the medium (Anderson, 2012). As yet, the convergence narrative is largely underdeveloped in the broader sporting literature; thus the paper drew heavily on the literature surrounding surfing in which the notion of convergence has been developed. By sharing and spreading of a range of different stories, individuals develop their narrative repertoire. This gives them the resources to be able to move past the performance narrative, and restory themselves if, and when, it no longer aligns with their experiences. My thesis looks to add to the existing literature resisting the performance narrative, by sharing evocative tales showing the complexities and intricacies of resisting or conforming to the performance narrative, and essentially, the joy that can be found within the sport of rowing

Exploring Pharmacists’ Roles during the 2019–2020 Australian Black Summer Bushfires

Background: Australians are no strangers to sudden natural disasters, such as bushfires. The effects of a natural disaster can devastate local communities and health care services. Currently, limited research has explored the role of the pharmacist during a natural disaster. This study explores the role of the Australian pharmacist during the 2019/2020 Black Summer Bushfires. Methods: Semi-structured phone interviews were conducted with ten community pharmacists who worked through the Black Summer Bushfires whose daily tasks and work environment were directly affected by the bushfires. Thematic analysis using NVivo®, a qualitative data analysis software was conducted. Results: Analysis of the transcripts generated six main themes: collaboration; trauma and mental health; power and communication; acute presentations; triaging and emergency prescribing. Pharmacists worked in close collaboration with doctors and members of the local community. They provided triaging services, timely health advice about chronic health problems, and managed acute issues, including wound and burn management and mental health support in traumatic conditions, sometimes without power and communication amenities. The challenges presented to pharmacists during the bushfires warranted creative and flexible approaches at times. Conclusion: This study highlights the need for mental health support and training for pharmacists, provisional prescribing privileges, and a clearer set of contingency regulations and legislation related to emergencies and natural disasters. Further research is warranted to gain greater insight into the roles undertaken by Australian pharmacists during natural disasters and their autonomy in decision making processes during such times

A semi-parametric model for lactation curves: development and application

We propose a semi-parametric model for lactation curves that, along with stage of lactation, accounts for day of the year at milk recording and stage of gestation. Lactation is described as having 3 different phases defined by 2 change points of which the second is a function of gestation stage. Season of milk recording is modelled using cosine and sine functions. As an application, the model is used to estimate the association between intramammary infections (IMI) dynamics as measured by somatic cell count (SCC) over the dry period and the shape of the lactation curve. Milk recording data collected in 2128 herds from England and Wales between 2004 and 2007 were used in the analysis. From a random sample of 1000 of these herds, smoothed milk production was used to test the behaviour of the model and estimate model parameters. The first change point was set at 60 days in milk. The second change point was set at 100 days of gestation or 200 days in milk when the latter was not available. Using data from the 1128 remaining herds, multilevel models were then used to model individual test-day milk production within lactations within herds. Average milk production at 60 days in milk for cows of parities 1, 2, 3 and greater than 3 were 26.9 kg, 31.6 kg, 34.4 kg and 34.7 kg respectively and, after this stage, decreases in milk production per 100 days milk of lactation were 3.1 kg, 5.1 kg, 6.3 kg and 6.7 kg respectively. Compared to cows that had an SCC below 200,000 cells/mL on both the last milk recording in a lactation and the first milk recording in the following lactation, cows that had an SCC greater than 200,000 cells/mL on their first milk recording after calving had an estimated loss of milk production of between 216 and 518 kg depending on parity. These estimates demonstrate the impact of the dynamics of SCC during the dry period on milk production during the following lactation

Marginal deformation of N=4 SYM and Penrose limits with continuum spectrum

We study the Penrose limit about a null geodesic with 3 equal angular momenta in the recently obtained type IIB solution dual to an exactly marginal $\gamma$-deformation of N=4 SYM. The resulting background has non-trivial NS 3-form flux as well as RR 5- and 3-form fluxes. We quantise the light-cone Green-Schwarz action and show that it exhibits a continuum spectrum. We show that this is related to the dynamics of a charged particle moving in a Landau plane with an extra interaction induced by the deformation. We interpret the results in the dual N=1 SCFT.Comment: 26 pages, 2 figures; v2: typos corrected, field theory interpretation extende

Transcriptional Heterogeneity of Cryptococcus gattii VGII Compared with Non-VGII Lineages Underpins Key Pathogenicity Pathways

We thank Jose Munoz for his input on the analysis of the mouse RNA-seq enrichment. R.A.F. was supported by a Wellcome Trust-Massachusetts Institute of Technology (MIT) Postdoctoral Fellowship. M.C.F. and J.R. were supported by Medical Research Council grant MR/K000373/1. R.C.M. is supported by a Wolfson Royal Society Research Merit Award and by funding from the European Research Council under the European Union’s Seventh Framework Program (FP/2007-2013)/ERC (grant agreement no. 614562). This work was funded in part by NIAID grant U19AI110818 to the Broad Institute.Peer reviewedPublisher PD

Breast Cancer-Derived Extracellular Vesicles: Characterization and Contribution to the Metastatic Phenotype

The study of extracellular vesicles (EVs) in cancer progression is a complex and rapidly evolving field. Whole categories of cellular interactions in cancer which were originally presumed to be due solely to soluble secreted molecules have now evolved to include membrane-enclosed extracellular vesicles (EVs), which include both exosomes and shed microvesicles (MVs), and can contain many of the same molecules as those secreted in soluble form but many different molecules as well. EVs released by cancer cells can transfer mRNA, miRNA, and proteins to different recipient cells within the tumor microenvironment, in both an autocrine and paracrine manner, causing a significant impact on signaling pathways, mRNA transcription, and protein expression. The transfer of EVs to target cells, in turn, supports cancer growth, immunosuppression, and metastasis formation. This review focuses exclusively on breast cancer EVs with an emphasis on breast cancer-derived exosomes, keeping in mind that breast cancer-derived EVs share some common physical properties with EVs of other cancers

Breast Cancer-Derived Extracellular Vesicles: Characterization and Contribution to the Metastatic Phenotype

The study of extracellular vesicles (EVs) in cancer progression is a complex and rapidly evolving field. Whole categories of cellular interactions in cancer which were originally presumed to be due solely to soluble secreted molecules have now evolved to include membrane-enclosed extracellular vesicles (EVs), which include both exosomes and shed microvesicles (MVs), and can contain many of the same molecules as those secreted in soluble form but many different molecules as well. EVs released by cancer cells can transfer mRNA, miRNA, and proteins to different recipient cells within the tumor microenvironment, in both an autocrine and paracrine manner, causing a significant impact on signaling pathways, mRNA transcription, and protein expression. The transfer of EVs to target cells, in turn, supports cancer growth, immunosuppression, and metastasis formation. This review focuses exclusively on breast cancer EVs with an emphasis on breast cancer-derived exosomes, keeping in mind that breast cancer-derived EVs share some common physical properties with EVs of other cancers

Automatic kernel selection for Gaussian processes regression with approximate Bayesian computation and sequential Monte Carlo

The current work introduces a novel combination of two Bayesian tools, Gaussian Processes (GPs), and the use of the Approximate Bayesian Computation (ABC) algorithm for kernel selection and parameter estimation for machine learning applications. The combined methodology that this research article proposes and investigates offers the possibility to use different metrics and summary statistics of the kernels used for Bayesian regression. The presented work moves a step toward online, robust, consistent, and automated mechanism to formulate optimal kernels (or even mean functions) and their hyperparameters simultaneously offering confidence evaluation when these tools are used for mathematical or engineering problems such as structural health monitoring (SHM) and system identification (SI)