Diversity characteristics and the experiences of nursing students during clinical placements: A qualitative study of student, faculty and supervisors' views

Background: Little is known about which diversity characteristics if any, impact on nursing students' clinical placements or how these may affect the quality of their learning experiences. There is therefore a need to better understand these effects not only from the student's perspective but also from the perspective of the staff who supervise them, in order to ensure students obtain maximal benefit from their placements. Aim: To describe the clinical experiences of nursing students and the diversity characteristics that affect this learning experience. Methods: Data were collected from a series of open-ended questions embedded within a larger anonymous web-based survey, from August 2011 to March 2012. Participants included first, second and third year undergraduate Bachelor of Nursing students (N = 704) and faculty members involved in the clinical learning environment (N = 165) from seven Australian universities. Findings: Qualitative findings were clustered into three main themes: differences, difficulty and discrimination, each with three subthemes. Conclusion: Findings suggest a need to offer appropriate support for nursing students who feel different because of diversity characteristics. Whilst some of the participant perceptions are confronting they provide valuable insights for universities developing curricula and the clinical placement facilities where students obtain their experience

Fatigue crack initiation and small crack growth in several airframe alloys

The growth of naturally-initiated small cracks under a variety of constant amplitude and variable amplitude load sequences is examined for several airframe materials: the conventional aluminum alloys, 2024-T3 and 7075-T6, the aluminum-lithium alloy, 2090-T8E41, and 4340 steel. Loading conditions investigated include constant amplitude loading at R = 0.5, 0, -1 and -2 and the variable amplitude sequences FALSTAFF, Mini-TWIST and FELIX/28. Crack growth was measured at the root of semicircular edge notches using acetate replicas. Crack growth rates are compared on a stress intensity factor basis, to those for large cracks to evaluate the extent of the small crack effect in each alloy. In addition, the various alloys are compared on a crack initiation and crack growth morphology basis

Exascale Deep Learning for Climate Analytics

We extract pixel-level masks of extreme weather patterns using variants of Tiramisu and DeepLabv3+ neural networks. We describe improvements to the software frameworks, input pipeline, and the network training algorithms necessary to efficiently scale deep learning on the Piz Daint and Summit systems. The Tiramisu network scales to 5300 P100 GPUs with a sustained throughput of 21.0 PF/s and parallel efficiency of 79.0%. DeepLabv3+ scales up to 27360 V100 GPUs with a sustained throughput of 325.8 PF/s and a parallel efficiency of 90.7% in single precision. By taking advantage of the FP16 Tensor Cores, a half-precision version of the DeepLabv3+ network achieves a peak and sustained throughput of 1.13 EF/s and 999.0 PF/s respectively.Comment: 12 pages, 5 tables, 4, figures, Super Computing Conference November 11-16, 2018, Dallas, TX, US

Flavin-Containing Monooxygenase 1 Catalyzes the Production of Taurine from Hypotaurine

Taurine is one of the most abundant amino acids in mammalian tissues. It is obtained from the diet and by de novo synthesis, from cysteic acid or hypotaurine. Despite the discovery in 1954 that the oxygenation of hypotaurine produces taurine, the identification of an enzyme catalyzing this reaction has remained elusive. In large part this is due to the incorrect assignment, in 1962, of the enzyme as an NAD-dependent hypotaurine dehydrogenase. For more than 55 years the literature has continued to refer to this enzyme as such. Here we show, both in vivo and in vitro, that the enzyme that oxygenates hypotaurine to produce taurine is flavin-containing monooxygenase 1 (FMO1). Metabolite analysis of the urine of Fmo1-null mice by 1H NMR spectroscopy revealed a build-up of hypotaurine and a deficit of taurine in comparison with the concentrations of these compounds in the urine of wild-type mice. In vitro assays confirmed that human FMO1 catalyzes the conversion of hypotaurine to taurine utilizing either NADPH or NADH as co-factor. FMO1 has a wide substrate range and is best known as a xenobiotic- or drug-metabolizing enzyme. The identification that the endogenous molecule hypotaurine is a substrate for the FMO1-catalyzed production of taurine resolves a long-standing mystery. This finding should help establish the role FMO1 plays in a range of biological processes in which taurine or its deficiency is implicated, including conjugation of bile acids, neurotransmitter, anti-oxidant and anti-inflammatory functions, and the pathogenesis of obesity and skeletal muscle disorders

Fatigue life and crack growth prediction methodology

The capabilities of a plasticity-induced crack-closure model and life-prediction code to predict fatigue crack growth and fatigue lives of metallic materials are reviewed. Crack-tip constraint factors, to account for three-dimensional effects, were selected to correlate large-crack growth rate data as a function of the effective-stress-intensity factor range (delta(K(sub eff))) under constant-amplitude loading. Some modifications to the delta(K(sub eff))-rate relations were needed in the near threshold regime to fit small-crack growth rate behavior and endurance limits. The model was then used to calculate small- and large-crack growth rates, and in some cases total fatigue lives, for several aluminum and titanium alloys under constant-amplitude, variable-amplitude, and spectrum loading. Fatigue lives were calculated using the crack growth relations and microstructural features like those that initiated cracks. Results from the tests and analyses agreed well

Feynman's interpretation of quantum theory

A historically important but little known debate regarding the necessity and meaning of macroscopic superpositions, in particular those containing different gravitational fields, is discussed from a modern perspective.Comment: Published version for Eur.Phys.J. H. 15 pages pdf. Final version available at http://www.springerlink.com/openurl.asp?genre=article&id=doi:10.1140/epjh/e2011-10035-

The Gattini cameras for optical sky brightness measurements at Dome C, Antarctica

The Gattini cameras are two site testing instruments for the measurement of optical sky brightness, large area cloud cover and auroral detection of the night sky above the high altitude Dome C site in Antarctica. The cameras have been operating since installation in January 2006 and are currently at the end of the first Antarctic winter season. The cameras are transit in nature and are virtually identical both adopting Apogee Alta CCD detectors. By taking frequent images of the night sky we obtain long term cloud cover statistics, measure the sky background intensity as a function of solar and lunar altitude and phase and directly measure the spatial extent of bright aurora if present and when they occur. The full data set will return in December 2006 however a limited amount of data has been transferred via the Iridium network enabling preliminary data reduction and system evaluation. An update of the project is presented together with preliminary results from data taken since commencement of the winter season

Treatment of wild-type mice with 2,3-butanediol, a urinary biomarker of fmo5-/- mice, decreases plasma cholesterol and epididymal fat deposition

We previously showed that Fmo5−/− mice exhibit a lean phenotype and slower metabolic ageing. Their characteristics include lower plasma glucose and cholesterol, greater glucose tolerance and insulin sensitivity, and a reduction in age-related weight gain and whole-body fat deposition. In this paper, nuclear magnetic resonance (NMR) spectroscopy-based metabolite analyses of the urine of Fmo5−/− and wild-type mice identified two isomers of 2,3-butanediol as discriminating urinary biomarkers of Fmo5−/− mice. Antibiotic-treatment of Fmo5−/− mice increased plasma cholesterol concentration and substantially reduced urinary excretion of 2,3-butanediol isomers, indicating that the gut microbiome contributed to the lower plasma cholesterol of Fmo5−/− mice, and that 2,3-butanediol is microbially derived. Short- and long-term treatment of wild-type mice with a 2,3-butanediol isomer mix decreased plasma cholesterol and epididymal fat deposition but had no effect on plasma concentrations of glucose or insulin, or on body weight. In the case of long-term treatment, the effects were maintained after withdrawal of 2,3-butanediol. Short-, but not long-term treatment, also decreased plasma concentrations of triglycerides and non-esterified fatty acids. Fecal transplant from Fmo5−/− to wild-type mice had no effect on plasma cholesterol, and 2,3-butanediol was not detected in the urine of recipient mice, suggesting that the microbiota of the large intestine was not the source of 2,3-butanediol. However, 2,3-butanediol was detected in the stomach of Fmo5−/− mice, which was enriched for Lactobacillus genera, known to produce 2,3-butanediol. Our results indicate a microbial contribution to the phenotypic characteristic of Fmo5−/− mice of decreased plasma cholesterol and identify 2,3-butanediol as a potential agent for lowering plasma cholesterol

EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy

Traversing terrain with good traction is crucial for achieving fast off-road navigation. Instead of manually designing costs based on terrain features, existing methods learn terrain properties directly from data via self-supervision, but challenges remain to properly quantify and mitigate risks due to uncertainties in learned models. This work efficiently quantifies both aleatoric and epistemic uncertainties by learning discrete traction distributions and probability densities of the traction predictor's latent features. Leveraging evidential deep learning, we parameterize Dirichlet distributions with the network outputs and propose a novel uncertainty-aware squared Earth Mover's distance loss with a closed-form expression that improves learning accuracy and navigation performance. The proposed risk-aware planner simulates state trajectories with the worst-case expected traction to handle aleatoric uncertainty, and penalizes trajectories moving through terrain with high epistemic uncertainty. Our approach is extensively validated in simulation and on wheeled and quadruped robots, showing improved navigation performance compared to methods that assume no slip, assume the expected traction, or optimize for the worst-case expected cost.Comment: Under review. Journal extension for arXiv:2210.00153. Project website: https://xiaoyi-cai.github.io/evora