A new model for procuring e-books

This paper draws on a recent ground-breaking tender for e-books for higher education libraries in the UK. The strategy for the tender was informed by standard procurement practice and by the experience of acquiring other e-resources, particularly journals under the so-called big deal. Both are examined as background to the discussion of e-books in general and the tender in particular

Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees

We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that these star/galaxy classifications are expected to be reliable for approximately 22 million objects with r < ~20. The general machine learning environment Data-to-Knowledge and supercomputing resources enabled extensive investigation of the decision tree parameter space. This work presents the first public release of objects classified in this way for an entire SDSS data release. The objects are classified as either galaxy, star or nsng (neither star nor galaxy), with an associated probability for each class. To demonstrate how to effectively make use of these classifications, we perform several important tests. First, we detail selection criteria within the probability space defined by the three classes to extract samples of stars and galaxies to a given completeness and efficiency. Second, we investigate the efficacy of the classifications and the effect of extrapolating from the spectroscopic regime by performing blind tests on objects in the SDSS, 2dF Galaxy Redshift and 2dF QSO Redshift (2QZ) surveys. Given the photometric limits of our spectroscopic training data, we effectively begin to extrapolate past our star-galaxy training set at r ~ 18. By comparing the number counts of our training sample with the classified sources, however, we find that our efficiencies appear to remain robust to r ~ 20. As a result, we expect our classifications to be accurate for 900,000 galaxies and 6.7 million stars, and remain robust via extrapolation for a total of 8.0 million galaxies and 13.9 million stars. [Abridged]Comment: 27 pages, 12 figures, to be published in ApJ, uses emulateapj.cl

Calcium Bistriflimide-Mediated Sulfur(VI)–Fluoride Exchange (SuFEx): Mechanistic Insights toward Instigating Catalysis

We report a mechanistic investigation of calcium bistriflimide-mediated sulfur(VI)–fluoride exchange (SuFEx) between sulfonyl fluorides and amines. We determine the likely pre-activation resting state─a calcium bistriflimide complex with ligated amines─thus allowing for corroborated calculation of the SuFEx activation barrier at ∼21 kcal/mol, compared to 21.5 ± 0.14 kcal/mol derived via kinetics experiments. Transition state analysis revealed: (1) a two-point calcium-substrate contact that activates the sulfur(VI) center and stabilizes the leaving fluoride and (2) a 1,4-diazabicyclo[2.2.2]octane additive that provides Brønsted-base activation of the nucleophilic amine. Stable Ca–F complexes upon sulfonamide formation are likely contributors to inhibited catalytic turnover, and a proof-of-principle redesign provided evidence that sulfonamide formation is feasible with 10 mol % calcium bistriflimide

Predicting Driving Performance in Older Adults with the Useful Field of View Test: A Meta-Analysis

This investigation examines the Useful Field of View (specifically theUFOV® test), as a predictor of objective measures of driving performance.PubMed and PsycInfo databases were searched to retrieve eight independentstudies reporting bivariate relationships between the UFOV® test and drivingmeasures (driving simulator performance, state-recorded crashes, and on-roaddriving). Cumulative meta-analysis techniques were used to examine thepredictive utility of the test, to determine whether the effect size was stable acrossstudies, and to assess whether a sufficient number of studies have been conductedto conclude that the test is an effective predictor of driving competence. Resultsshowed that the study samples could have been drawn from the same population.The weighted mean effect size across all studies revealed a large effect, Cohen’sd=0.945, with poorer UFOV® test performance associated with negative drivingoutcomes. This relationship was robust across multiple indices of drivingperformance and several research laboratories. A concrete measure of sufficiencyrevealed that an additional 513 studies averaging a null result must be conductedto bring the p-value for the cumulative effect size to greater than .05. Thisconvergence of evidence across different points in time and different researchteams confirms the importance of the UFOV® assessment as a valid and reliableindex of driving performance and safety. Corroborating this finding, a recent largefield study in Maryland has further established the UFOV® test as a usefulscreening instrument to identify at-risk older drivers. Taken together, thesefindings could have far-reaching implications for public policy

Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX

We apply machine learning in the form of a nearest neighbor instance-based algorithm (NN) to generate full photometric redshift probability density functions (PDFs) for objects in the Fifth Data Release of the Sloan Digital Sky Survey (SDSS DR5). We use a conceptually simple but novel application of NN to generate the PDFs - perturbing the object colors by their measurement error - and using the resulting instances of nearest neighbor distributions to generate numerous individual redshifts. When the redshifts are compared to existing SDSS spectroscopic data, we find that the mean value of each PDF has a dispersion between the photometric and spectroscopic redshift consistent with other machine learning techniques, being sigma = 0.0207 +/- 0.0001 for main sample galaxies to r < 17.77 mag, sigma = 0.0243 +/- 0.0002 for luminous red galaxies to r < ~19.2 mag, and sigma = 0.343 +/- 0.005 for quasars to i < 20.3 mag. The PDFs allow the selection of subsets with improved statistics. For quasars, the improvement is dramatic: for those with a single peak in their probability distribution, the dispersion is reduced from 0.343 to sigma = 0.117 +/- 0.010, and the photometric redshift is within 0.3 of the spectroscopic redshift for 99.3 +/- 0.1% of the objects. Thus, for this optical quasar sample, we can virtually eliminate 'catastrophic' photometric redshift estimates. In addition to the SDSS sample, we incorporate ultraviolet photometry from the Third Data Release of the Galaxy Evolution Explorer All-Sky Imaging Survey (GALEX AIS GR3) to create PDFs for objects seen in both surveys. For quasars, the increased coverage of the observed frame UV of the SED results in significant improvement over the full SDSS sample, with sigma = 0.234 +/- 0.010. We demonstrate that this improvement is genuine. [Abridged]Comment: Accepted to ApJ, 10 pages, 12 figures, uses emulateapj.cl

Letter to the Editor: "Pediatric Obesity-Assessment, Treatment, and Prevention: An Endocrine Society Clinical Practice Guideline"

We read with interest the recently published clinical practice guidelines for preventing and treating childhood obesity (1). The authors reported their evaluation of the quality of the evidence and an assessment of the strength of recommendations according to objective criteria across a diverse literature. In our view, however, this excellent and comprehensive report does not mention two relevant issues: attrition and enrollment. These issues are likely to be of concern for clinicians, administrators, and researchers because they can have a substantial impact on clinical care

Data Mining and Machine Learning in Astronomy

We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra figures, some minor additions to the tex

Salt stress in the renal tubules is Linked to TAL specific expression of uromodulin and an upregulation of heat shock genes

Previously, our comprehensive cardiovascular characterisation study validated Uromodulin as a blood pressure gene. Uromodulin is a glycoprotein exclusively synthesised at the thick ascending limb of the loop of Henle and is encoded by the Umod gene. Umod(-/-) mice have significantly lower blood pressure than Umod(+/+) mice, are resistant to salt-induced changes in blood pressure, and show a leftward shift in pressure-natriuresis curves reflecting changes of sodium reabsorption. Salt stress triggers transcription factors and genes that alter renal sodium reabsorption. To date there are no studies on renal transcriptome responses to salt stress. Here we aimed to delineate salt stress pathways in tubules isolated from Umod(+/+) mice (a model of sodium retention) and Umod(-/-) mice (a model of sodium depletion) +/-300mOsmol sodium chloride (n=3 per group) performing RNA-Seq. In response to salt stress, the tubules of Umod(+/+) mice displayed an up regulation of heat shock transcripts. The greatest changes occurred in the expression of: Hspa1a (Log2 fold change 4.35, p=2.48e-12) and Hspa1b (Log2 fold change 4.05, p=2.48e-12). This response was absent in tubules of Umod(-/-) mice. Interestingly, 7 of the genes discordantly expressed in the Umod(-/-) tubules were electrolyte transporters. Our results are the first to show that salt stress in renal tubules alters the transcriptome, increasing the expression of heat shock genes. This direction of effect in Umod(+/+) tubules suggest the difference is due to the presence of Umod facilitating greater sodium entry into the tubule cell reflecting a specific response to salt stress