977 resultados para Emma Hamilton


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In the emergent field of creative practice higher degrees by research, first generation supervisors have developed new models of supervision for an unprecedented form of research that combines creative practice and written thesis. In a national research project, entitled 'Effective supervision of creative practice higher research degrees', we set out to capture and share early supervisors' insights, strategies and approaches to supporting their creative practice PhD students. From the insights we gained during the early interview process, we expanded our research methods in line with a distributed leadership model and developed a dialogic framework. This led us to unanticipated conclusions and unexpected recommendations. In this study, we primarily draw on philosopher and literary theorist Mikhail Bakhtin's dialogics to explain how giving precedence to the voices of supervisors not only facilitated the articulation of dispersed tacit knowledge, but also led to other 20 discoveries. These include the nature of supervisors' resistance to prescribed models, policies and central academic development programmes; the importance of polyvocality and responsive dialogue in enabling continued innovation in the field; the benefits to supervisors of reflecting, discussing and sharing practices with colleagues; and the value of distributed leadership and dialogue to academic development and supervision capacity building in research education.

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Sessional Academics enhance students’ learning experience by bringing a diverse range of perspectives and expertise into the classroom. As industry specialists, research students, and recent graduates who have excelled in their courses, they complement the discipline expertise of career academics. With increasing casualization of the academic workforce, Sessional Academics now deliver the majority of face-to-face undergraduate teaching in Australian Universities. To enable them to realize their full potential as effective contributors to student learning and course quality, universities need to offer effective training and access to advice and support and facilitate engagement in university life. However, in the face of complex and diverse contexts, overwhelming numbers, and the transitory nature of sessional cohorts, few universities have developed a comprehensive, systematic approach. During the past three years at QUT, we have set out to develop a multifaceted approach to Sessional Academic support and development. In this paper I will explain why and how we have done so, and describe the range of strategies and programs we have developed. They include a central academic development program, which is structured and scaffolded with learning objectives and outcomes, and aligned with a graduate certificate in Academic Practice; a Sessional Academic Success program, which deploys experienced, school-based sessional academic success advisors to provide local support, build a sense of community, and offer discipline focused academic development; an online, dialogic communication strategy; and opportunities to present and be acknowledged for good learning and teaching practices. Together, these strategies have impacted on sessional academics’ confidence, learning and teaching capacity, reflection and engagement.

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Introduction and Aims Wastewater analysis (WWA) is intended to be a direct and objective method of measuring substance use in large urban populations. It has also been used to measure prison substance use in two previous studies. The application of WWA in this context has raised questions as to how best it might be used to measure illicit drug use in prisons, and whether it can also be used to measure prescription misuse. We applied WWA to a small regional prison to measure the use of 12 licit and illicit substances. We attempted to measure the non-medical use of methadone and buprenorphine and to compare our findings with the results of the prison's mandatory drug testing (MDT). Design and Methods Representative daily composite samples were collected for two periods of 12 consecutive days in May to July 2013 and analysed for 18 drug metabolites. Prescription data and MDT results were obtained from the prison and compared with the substance use estimates calculated from WWA data. Results Daily use of methamphetamine, methadone, buprenorphine and codeine was detected, while sporadic detection of ketamine and methylone was also observed. Overall buprenorphine misuse appeared to be greater than methadone misuse. Discussion and Conclusions Compared with MDT, WWA provides a more comprehensive picture of prison substance use. WWA also has the potential to measure the misuse of medically prescribed substances. However, a great deal of care must be exercised in quantifying the usage of any substance in small populations, such as in prisons.

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Prison substance use is a major concern for prison authorities and the wider community. Australia has responded to this problem by implementing the National Corrections Drug Strategy. Across Australia, the true extent of prison substance use cannot be determined. As a result, the effectiveness of the interventions employed as part of this strategy cannot be properly assessed. This has important implications for the allocation of corrective services resources and future policy development. This article explores the benefits and limitations, as well as the ethical and practical issues in using wastewater analysis (WWA) to measure levels of substance use in prisons. It reports results from the first application of WWA to an Australian prison, which supports the use of WWA in this context. Given the increasing concern for prescription misuse in prisons, we also highlight the novel use of WWA to measure the extent of prescription misuse by prisoners. The article concludes that as a result of its objectivity, sensitivity and cost-effectiveness, the use of WWA in prisons warrants further consideration in Australia.

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The successful completion of the Human Genome Project (HGP) was an unprecedented scientific advance that has become an invaluable resource in the search for genes that cause monogenic and common (polygenic) diseases. Prior to the HGP, linkage analysis had successfully mapped many disease genes for monogenic disorders; however, the limitations of this approach were particularly evident for identifying causative genes in rare genetic disorders affecting lifespan and/or reproductive fitness, such as skeletal dysplasias. In this review, we illustrate the challenges of mapping disease genes in such conditions through the ultra-rare disorder fibrodysplasia ossificans progressiva (FOP) and we discuss the advances that are being made through current massively parallel (“next generation”) sequencing (MPS) technologies.

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The maintenance of genome stability is essential to prevent loss of genetic information and the development of diseases such as cancer. One of the most common forms of damage to the genetic code is the oxidation of DNA by reactive oxygen species (ROS), of which 8-oxo-7,8-dihydro-guanine (8-oxoG) is the most frequent modification. Previous studies have established that human single-stranded DNA-binding protein 1 (hSSB1) is essential for the repair of double-stranded DNA breaks by the process of homologous recombination. Here we show that hSSB1 is also required following oxidative damage. Cells lacking hSSB1 are sensitive to oxidizing agents, have deficient ATM and p53 activation and cannot effectively repair 8-oxoGs. Furthermore, we demonstrate that hSSB1 forms a complex with the human oxo-guanine glycosylase 1 (hOGG1) and is important for hOGG1 localization to the damaged chromatin. In vitro, hSSB1 binds directly to DNA containing 8-oxoguanines and enhances hOGG1 activity. These results underpin the crucial role hSSB1 plays as a guardian of the genome.

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Dissecting how genetic and environmental influences impact on learning is helpful for maximizing numeracy and literacy. Here we show, using twin and genome-wide analysis, that there is a substantial genetic component to children’s ability in reading and mathematics, and estimate that around one half of the observed correlation in these traits is due to shared genetic effects (so-called Generalist Genes). Thus, our results highlight the potential role of the learning environment in contributing to differences in a child’s cognitive abilities at age twelve.

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Background Forearm fractures affect 1.7 million individuals worldwide each year and most occur earlier in life than hip fractures. While the heritability of forearm bone mineral density (BMD) and fracture is high, their genetic determinants are largely unknown. Aim To identify genetic variants associated with forearm BMD and forearm fractures. Methods BMD at distal radius, measured by dualenergy x-ray absorptiometry, was tested for association with common genetic variants. We conducted a metaanalysis of genome-wide association studies for BMD in 5866 subjects of European descent and then selected the variants for replication in 715 Mexican American samples. Gene-based association was carried out to supplement the single-nucleotide polymorphism (SNP) association test. We then tested the BMD-associated SNPs for association with forearm fracture in 2023 cases and 3740 controls. Results We found that five SNPs in the introns of MEF2C were associated with forearm BMD at a genome-wide significance level (p<5×10-8) in meta-analysis (lead SNP, rs11951031[T] -0.20 SDs per allele, p=9.01×10-9). The gene-based association test suggested an association between MEF2C and forearm BMD ( p=0.003). The association between MEF2C variants and risk of fracture did not achieve statistical significance (SNP rs12521522[A]: OR=1.14 (95% CI 0.92 to 1.35), p=0.14). Meta-analysis also revealed two genome-wide suggestive loci at CTNNA2 and 6q23.2. Conclusions These findings demonstrate that variants at MEF2C were associated with forearm BMD, implicating this gene in the determination of BMD at forearm.

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In humans, congenital spinal defects occur with an incidence of 0.5-1 per 1000 live births. One of the most severe syndromes with such defects is spondylocostal dysostosis (SCD). Over the past decade, the genetic basis of several forms of autosomal recessive SCD cases has been solved with the identification of four causative genes (DLL3, MESP2, LFNG and HES7). Autosomal dominant forms of SCD have also been reported, but to date no genetic etiology has been described for these. Here, we have used exome capture and next-generation sequencing to identify a stoploss mutation in TBX6 that segregates with disease in two generations of one family. We show that this mutation has a deleterious effect on the transcriptional activation activity of the TBX6 protein, likely due to haploinsufficiency. In mouse, Tbx6 is essential for the patterning of the vertebral precursor tissues, somites; thus, mutation of TBX6 is likely to be causative of SCD in this family. This is the first identification of the genetic cause of an autosomal dominant form of SCD, and also demonstrates the potential of exome sequencing to identify genetic causes of dominant diseases even in small families with few affected individuals.

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Genome-wide association studies (GWAS) are a powerful hypothesis-free tool for the dissection of susceptibility to common heritable human diseases, including osteoporosis. To date, more than 2000 loci for common human diseases have been identified by GWAS. Success using the GWAS model depends on genetic risk being determined by shared stretches of DNA carried with different frequencies in cases and controls, inherited from ancient ancestors, termed the “common disease–common variant” hypothesis. Not all disease risk is caused by common variants, however, and thus GWAS will not detect all variants involved. Successful GWAS performance requires careful quality control, especially as the effect sizes under study are modest, and there are multiple potential sources of error. Conservative interpretation, use of stringent significance thresholds, and replication in independent cohorts are required to ensure results are robust. Despite these challenging parameters, much has been learnt from GWAS and, as the approach matures and is modified to identify a wider range of variants, significantly more will be learnt about the etiopathogenesis of common diseases such as osteoporosis.

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Context: Osteoporosis is a common, highly heritable condition that causes substantial morbidity and mortality, the etiopathogenesis of which is poorly understood. Genetic studies are making increasingly rapid progress in identifying the genes involved. Evidence Acquisition and Synthesis: In this review, we will summarize the current understanding of the genetics of osteoporosis based on publications from PubMed from the year 1987 onward. Conclusions: Most genes involved in osteoporosis identified to date encode components of known pathways involved in bone synthesis or resorption, but as the field progresses, new pathways are being identified. Only a small proportion of the total genetic variation involved in osteoporosis has been identified, and new approaches will be required to identify most of the remaining genes.

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Bone mineral density (BMD) is the most widely used predictor of fracture risk. We performed the largest meta-analysis to date on lumbar spine and femoral neck BMD, including 17 genome-wide association studies and 32,961 individuals of European and east Asian ancestry. We tested the top BMD-associated markers for replication in 50,933 independent subjects and for association with risk of low-trauma fracture in 31,016 individuals with a history of fracture (cases) and 102,444 controls. We identified 56 loci (32 new) associated with BMD at genome-wide significance (P < 5 × 10−8). Several of these factors cluster within the RANK-RANKL-OPG, mesenchymal stem cell differentiation, endochondral ossification and Wnt signaling pathways. However, we also discovered loci that were localized to genes not known to have a role in bone biology. Fourteen BMD-associated loci were also associated with fracture risk (P < 5 × 10−4, Bonferroni corrected), of which six reached P < 5 × 10−8, including at 18p11.21 (FAM210A), 7q21.3 (SLC25A13), 11q13.2 (LRP5), 4q22.1 (MEPE), 2p16.2 (SPTBN1) and 10q21.1 (DKK1). These findings shed light on the genetic architecture and pathophysiological mechanisms underlying BMD variation and fracture susceptibility.

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Peak bone mass achieved in adolescence is a determinant of bone mass in later life. In order to identify genetic variants affecting bone mineral density (BMD), we performed a genome-wide association study of BMD and related traits in 1518 children from the Avon Longitudinal Study of Parents and Children (ALSPAC). We compared results with a scan of 134 adults with high or low hip BMD. We identified associations with BMD in an area of chromosome 12 containing the Osterix (SP7) locus, a transcription factor responsible for regulating osteoblast differentiation (ALSPAC: P = 5.8 × 10-4; Australia: P = 3.7 × 10-4). This region has previously shown evidence of association with adult hip and lumbar spine BMD in an Icelandic population, as well as nominal association in a UK population. A meta-analysis of these existing studies revealed strong association between SNPs in the Osterix region and adult lumbar spine BMD (P = 9.9 × 10-11). In light of these findings, we genotyped a further 3692 individuals from ALSPAC who had whole body BMD and confirmed the association in children as well (P = 5.4 × 10-5). Moreover, all SNPs were related to height in ALSPAC children, but not weight or body mass index, and when height was included as a covariate in the regression equation, the association with total body BMD was attenuated. We conclude that genetic variants in the region of Osterix are associated with BMD in children and adults probably through primary effects on growth.

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INTRODUCTION Although the high heritability of BMD variation has long been established, few genes have been conclusively shown to affect the variation of BMD in the general population. Extreme truncate selection has been proposed as a more powerful alternative to unselected cohort designs in quantitative trait association studies. We sought to test these theoretical predictions in studies of the bone densitometry measures BMD, BMC, and femoral neck area, by investigating their association with members of the Wnt pathway, some of which have previously been shown to be associated with BMD in much larger cohorts, in a moderate-sized extreme truncate selected cohort (absolute value BMD Z-scores = 1.5-4.0; n = 344). MATERIALS AND METHODS Ninety-six tag-single nucleotide polymorphism (SNPs) lying in 13 Wnt signaling pathway genes were selected to tag common genetic variation (minor allele frequency [MAF] > 5% with an r(2) > 0.8) within 5 kb of all exons of 13 Wnt signaling pathway genes. The genes studied included LRP1, LRP5, LRP6, Wnt3a, Wnt7b, Wnt10b, SFRP1, SFRP2, DKK1, DKK2, FZD7, WISP3, and SOST. Three hundred forty-four cases with either high or low BMD were genotyped by Illumina Goldengate microarray SNP genotyping methods. Association was tested either by Cochrane-Armitage test for dichotomous variables or by linear regression for quantitative traits. RESULTS Strong association was shown with LRP5, polymorphisms of which have previously been shown to influence total hip BMD (minimum p = 0.0006). In addition, polymorphisms of the Wnt antagonist, SFRP1, were significantly associated with BMD and BMC (minimum p = 0.00042). Previously reported associations of LRP1, LRP6, and SOST with BMD were confirmed. Two other Wnt pathway genes, Wnt3a and DKK2, also showed nominal association with BMD. CONCLUSIONS This study shows that polymorphisms of multiple members of the Wnt pathway are associated with BMD variation. Furthermore, this study shows in a practical trial that study designs involving extreme truncate selection and moderate sample sizes can robustly identify genes of relevant effect sizes involved in BMD variation in the general population. This has implications for the design of future genome-wide studies of quantitative bone phenotypes relevant to osteoporosis.

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The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1–3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region4, 5, 6, 7, 8, 9, 10, 11. Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods—recursive partitioning and regression...