Asymptotic iteration method for eigenvalue problems

An asymptotic interation method for solving second-order homogeneous linear differential equations of the form y'' = lambda(x) y' + s(x) y is introduced, where lambda(x) \neq 0 and s(x) are C-infinity functions. Applications to Schroedinger type problems, including some with highly singular potentials, are presented.Comment: 14 page

End-of-season influenza vaccine effectiveness in adults and children, United Kingdom, 2016/17

Introduction The United Kingdom is in the fourth season of introducing a universal childhood influenza vaccine programme. The 2016/17 season saw early influenza A(H3N2) virus circulation with care home outbreaks and increased excess mortality particularly in those 65 years or older. Virus characterisation data indicated emergence of genetic clusters within the A(H3N2) 3C.2a group which the 2016/17 vaccine strain belonged to. Methods: The test-negative case-control (TNCC) design was used to estimate vaccine effectiveness (VE) against laboratory confirmed influenza in primary care. Results: Adjusted end-of-season vaccine effectiveness (aVE) estimates were 39.8% (95% confidence interval (CI): 23.1 to 52.8) against all influenza and 40.6% (95% CI: 19.0 to 56.3) in 18-64-year-olds, but no significant aVE in ≥ 65-year-olds. aVE was 65.8% (95% CI: 30.3 to 83.2) for 2-17-year-olds receiving quadrivalent live attenuated influenza vaccine. Discussion: The findings continue to provide support for the ongoing roll-out of the paediatric vaccine programme, with a need for ongoing evaluation. The importance of effective interventions to protect the ≥ 65-year-olds remains

Breaking the waves: improved detection of copy number variation from microarray-based comparative genomic hybridization.

BACKGROUND: Large-scale high throughput studies using microarray technology have established that copy number variation (CNV) throughout the genome is more frequent than previously thought. Such variation is known to play an important role in the presence and development of phenotypes such as HIV-1 infection and Alzheimer's disease. However, methods for analyzing the complex data produced and identifying regions of CNV are still being refined. RESULTS: We describe the presence of a genome-wide technical artifact, spatial autocorrelation or 'wave', which occurs in a large dataset used to determine the location of CNV across the genome. By removing this artifact we are able to obtain both a more biologically meaningful clustering of the data and an increase in the number of CNVs identified by current calling methods without a major increase in the number of false positives detected. Moreover, removing this artifact is critical for the development of a novel model-based CNV calling algorithm - CNVmix - that uses cross-sample information to identify regions of the genome where CNVs occur. For regions of CNV that are identified by both CNVmix and current methods, we demonstrate that CNVmix is better able to categorize samples into groups that represent copy number gains or losses. CONCLUSION: Removing artifactual 'waves' (which appear to be a general feature of array comparative genomic hybridization (aCGH) datasets) and using cross-sample information when identifying CNVs enables more biological information to be extracted from aCGH experiments designed to investigate copy number variation in normal individuals.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Confirming the Primarily Smooth Structure of the Vega Debris Disk at Millimeter Wavelengths

Clumpy structure in the debris disk around Vega has been previously reported at millimeter wavelengths and attributed to concentrations of dust grains trapped in resonances with an unseen planet. However, recent imaging at similar wavelengths with higher sensitivity has disputed the observed structure. We present three new millimeter wavelength observations that help to resolve the puzzling and contradictory observations. We have observed the Vega system with the Submillimeter Array (SMA) at a wavelength of 880 μm and an angular resolution of 5"; with the Combined Array for Research in Millimeter-wave Astronomy (CARMA) at a wavelength of 1.3 mm and an angular resolution of 5"; and with the Green Bank Telescope (GBT) at a wavelength of 3.3 mm and angular resolution of 10". Despite high sensitivity and short baselines, we do not detect the Vega debris disk in either of the interferometric data sets (SMA and CARMA), which should be sensitive at high significance to clumpy structure based on previously reported observations. We obtain a marginal (3σ) detection of disk emission in the GBT data; the spatial distribution of the emission is not well constrained.We analyze the observations in the context of several different models, demonstrating that the observations are consistent with a smooth, broad, axisymmetric disk with inner radius 20–100 AU and width ≾50 AU. The interferometric data require that at least half of the 860 μm emission detected by previous single-dish observations with the James Clerk Maxwell Telescope be distributed axisymmetrically, ruling out strong contributions from flux concentrations on spatial scales of ≾100 AU. These observations support recent results from the Plateau de Bure Interferometer indicating that previous detections of clumpy structure in the Vega debris disk were spurious

Publishing and sharing multi-dimensional image data with OMERO

Imaging data are used in the life and biomedical sciences to measure the molecular and structural composition and dynamics of cells, tissues, and organisms. Datasets range in size from megabytes to terabytes and usually contain a combination of binary pixel data and metadata that describe the acquisition process and any derived results. The OMERO image data management platform allows users to securely share image datasets according to specific permissions levels: data can be held privately, shared with a set of colleagues, or made available via a public URL. Users control access by assigning data to specific Groups with defined membership and access rights. OMERO’s Permission system supports simple data sharing in a lab, collaborative data analysis, and even teaching environments. OMERO software is open source and released by the OME Consortium at www.openmicroscopy.org

From discs to planetesimals I: evolution of gas and dust discs

I review the processes that shape the evolution of protoplanetary discs around young, solar-mass stars. I first discuss observations of protoplanetary discs, and note in particular the constraints these observations place on models of disc evolution. The processes that affect the evolution of gas discs are then discussed, with the focus in particular on viscous accretion and photoevaporation, and recent models which combine the two. I then discuss the dynamics and growth of dust grains in discs, considering models of grain growth, the gas-grain interaction and planetesimal formation, and review recent research in this area. Lastly, I consider the so-called "transitional" discs, which are thought to be observed during disc dispersal. Recent observations and models of these systems are reviewed, and prospects for using statistical surveys to distinguish between the various proposed models are discussed.Comment: 28 pages, 9 figures. Refereed review chapter for proceedings of VLTI summer school on "Circumstellar discs and planets at very high angular resolution", to appear in New Astronomy Reviews. See http://www.vlti.org/ for more detail

Photochemical fingerprinting is a sensitive probe for the detection of synthetic cannabinoid receptor agonists; towards robust point-of-care detection

With synthetic cannabinoid receptor agonist (SCRA) use still prevalent across Europe and structurally advanced generations emerging, it is imperative that drug detection methods advance in parallel. SCRAs are a chemically diverse and evolving group, which makes rapid detection challenging. We have previously shown that fluorescence spectral fingerprinting (FSF) has the potential to provide rapid assessment of SCRA presence directly from street material with minimal processing and in saliva. Enhancing the sensitivity and discriminatory ability of this approach has high potential to accelerate the delivery of a point-of-care technology that can be used confidently by a range of stakeholders, from medical to prison staff. We demonstrate that a range of structurally distinct SCRAs are photochemically active and give rise to distinct FSFs after irradiation. To explore this in detail, we have synthesized a model series of compounds which mimic specific structural features of AM-694. Our data show that FSFs are sensitive to chemically conservative changes, with evidence that this relates to shifts in the electronic structure and cross-conjugation. Crucially, we find that the photochemical degradation rate is sensitive to individual structures and gives rise to a specific major product, the mechanism and identification of which we elucidate through density-functional theory (DFT) and time-dependent DFT. We test the potential of our hybrid “photochemical fingerprinting” approach to discriminate SCRAs by demonstrating SCRA detection from a simulated smoking apparatus in saliva. Our study shows the potential of tracking photochemical reactivity via FSFs for enhanced discrimination of SCRAs, with successful integration into a portable device

Photochemical Fingerprinting Is a Sensitive Probe for the Detection of Synthetic Cannabinoid Receptor Agonists; Toward Robust Point-of-Care Detection

With synthetic cannabinoid receptor agonist (SCRA) use still prevalent across Europe and structurally advanced generations emerging, it is imperative that drug detection methods advance in parallel. SCRAs are a chemically diverse and evolving group, which makes rapid detection challenging. We have previously shown that fluorescence spectral fingerprinting (FSF) has the potential to provide rapid assessment of SCRA presence directly from street material with minimal processing and in saliva. Enhancing the sensitivity and discriminatory ability of this approach has high potential to accelerate the delivery of a point-of-care technology that can be used confidently by a range of stakeholders, from medical to prison staff. We demonstrate that a range of structurally distinct SCRAs are photochemically active and give rise to distinct FSFs after irradiation. To explore this in detail, we have synthesized a model series of compounds which mimic specific structural features of AM-694. Our data show that FSFs are sensitive to chemically conservative changes, with evidence that this relates to shifts in the electronic structure and cross-conjugation. Crucially, we find that the photochemical degradation rate is sensitive to individual structures and gives rise to a specific major product, the mechanism and identification of which we elucidate through density-functional theory (DFT) and time-dependent DFT. We test the potential of our hybrid "photochemical fingerprinting"approach to discriminate SCRAs by demonstrating SCRA detection from a simulated smoking apparatus in saliva. Our study shows the potential of tracking photochemical reactivity via FSFs for enhanced discrimination of SCRAs, with successful integration into a portable device.</p

Machine Learning Improves Upon Clinicians' Prediction of End Stage Kidney Disease

Background and ObjectivesChronic kidney disease progression to ESKD is associated with a marked increase in mortality and morbidity. Its progression is highly variable and difficult to predict. MethodsThis is an observational, retrospective, single-centre study. The cohort was patients attending hospital and nephrology clinic at The Canberra Hospital from September 1996 to March 2018. Demographic data, vital signs, kidney function test, proteinuria, and serum glucose were extracted. The model was trained on the featurised time series data with XGBoost. Its performance was compared against six nephrologists and the Kidney Failure Risk Equation (KFRE). ResultsA total of 12,371 patients were included, with 2,388 were found to have an adequate density (three eGFR data points in the first 2 years) for subsequent analysis. Patients were divided into 80%/20% ratio for training and testing datasets.ML model had superior performance than nephrologist in predicting ESKD within 2 years with 93.9% accuracy, 60% sensitivity, 97.7% specificity, 75% positive predictive value. The ML model was superior in all performance metrics to the KFRE 4- and 8-variable models.eGFR and glucose were found to be highly contributing to the ESKD prediction performance. ConclusionsThe computational predictions had higher accuracy, specificity and positive predictive value, which indicates the potential integration into clinical workflows for decision support.</p