Epitaxial Stabilization of Ultrathin Films of Rare-Earth Nickelates

We report on the synthesis of ultrathin films of highly distorted EuNiO3 (ENO) grown by interrupted pulse laser epitaxy on YAlO3 (YAO) substrates. Through mapping the phase space of nickelate thin film epitaxy, the optimal growth temperatures were found to scale linearly with the Goldschmidt tolerance factor. Considering the gibbs energy of the expanding film, this empirical trend is discussed in terms of epitaxial stabilization and the escalation of the lattice energy due to lattice distortions and decreasing symmetry. These findings are fundamental to other complex oxide perovskites, and provide a route to the synthesis of other perovskite structures in ultrathin-film form.Comment: 7 pages, 3 figure

Paediatric chronic kidney disease

Doctors use various guidelines on paediatric chronic kidney disease (CKD) for managing their patients according to the availability ofresources. As with adolescent and adult patients, CKD in children can also progress to end-stage renal failure – the time course beinginfluenced by several modifiable factors. Decline in renal failure is best categorised in stages, which determine management and prognosis.Staging is based on three categories, i.e. cause, glomerular filtration rate and proteinuria. Early diagnosis of CKD allows for the institutionof renoprotective treatment of modifiable factors and treatment to prevent the development of complications. The two most importantmodifiable factors that can be treated successfully are hypertension and proteinuria.The objective of this article is to provide information on the diagnosis and treatment of CKD in children. Early identification andtreatment of modifiable risk factors of CKD decreases the burden of disease and delays or prevents the need for renal replacement therapy

Treating patients as persons : a capabilities approach to support delivery of person-centered care

Peer reviewedPublisher PD

RICE Limits on the Diffuse Ultra-High Energy Neutrino Flux

We present new limits on ultra-high energy neutrino fluxes above 100 PeV based on data collected by the Radio Ice Cherenkov Experiment (RICE) at the South Pole from 1999-2005. We discuss estimation of backgrounds, calibration and data analysis algorithms (both on-line and off-line), procedures used for the dedicated neutrino search, and refinements in our Monte Carlo (MC) simulation, including recent in situ measurements of the complex ice dielectric constant. An enlarged data set and a more detailed study of hadronic showers results in a sensitivity improvement of more than one order of magnitude compared to our previously published results. Examination of the full RICE data set yields zero acceptable neutrino candidates, resulting in 95% confidence-level model dependent limits on the flux (E_\nu)^2(d\phi/dE_\nu)<10^{-6} GeV/(cm^2s~sr}) in the energy range 10^{17}< E_\nu< 10^{20} eV. The new RICE results rule out the most intense flux model projections at 95% confidence level.Comment: Submitted to Astropart. Phy

A dynamic network approach for the study of human phenotypes

The use of networks to integrate different genetic, proteomic, and metabolic datasets has been proposed as a viable path toward elucidating the origins of specific diseases. Here we introduce a new phenotypic database summarizing correlations obtained from the disease history of more than 30 million patients in a Phenotypic Disease Network (PDN). We present evidence that the structure of the PDN is relevant to the understanding of illness progression by showing that (1) patients develop diseases close in the network to those they already have; (2) the progression of disease along the links of the network is different for patients of different genders and ethnicities; (3) patients diagnosed with diseases which are more highly connected in the PDN tend to die sooner than those affected by less connected diseases; and (4) diseases that tend to be preceded by others in the PDN tend to be more connected than diseases that precede other illnesses, and are associated with higher degrees of mortality. Our findings show that disease progression can be represented and studied using network methods, offering the potential to enhance our understanding of the origin and evolution of human diseases. The dataset introduced here, released concurrently with this publication, represents the largest relational phenotypic resource publicly available to the research community.Comment: 28 pages (double space), 6 figure